Linear.Matrix:det44 from linear-1.19.1.3

Percentage Accurate: 30.6% → 54.9%

Time: 27.4s

Alternatives: 25

Speedup: 4.8×

• 88.4% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (+
  (-
   (+
    (+
     (-
      (* (- (* x y) (* z t)) (- (* a b) (* c i)))
      (* (- (* x j) (* z k)) (- (* y0 b) (* y1 i))))
     (* (- (* x y2) (* z y3)) (- (* y0 c) (* y1 a))))
    (* (- (* t j) (* y k)) (- (* y4 b) (* y5 i))))
   (* (- (* t y2) (* y y3)) (- (* y4 c) (* y5 a))))
  (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    code = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)))

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	return Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * y) - Float64(z * t)) * Float64(Float64(a * b) - Float64(c * i))) - Float64(Float64(Float64(x * j) - Float64(z * k)) * Float64(Float64(y0 * b) - Float64(y1 * i)))) + Float64(Float64(Float64(x * y2) - Float64(z * y3)) * Float64(Float64(y0 * c) - Float64(y1 * a)))) + Float64(Float64(Float64(t * j) - Float64(y * k)) * Float64(Float64(y4 * b) - Float64(y5 * i)))) - Float64(Float64(Float64(t * y2) - Float64(y * y3)) * Float64(Float64(y4 * c) - Float64(y5 * a)))) + Float64(Float64(Float64(k * y2) - Float64(j * y3)) * Float64(Float64(y4 * y1) - Float64(y5 * y0))))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(N[(N[(N[(N[(N[(N[(N[(x * y), $MachinePrecision] - N[(z * t), $MachinePrecision]), $MachinePrecision] * N[(N[(a * b), $MachinePrecision] - N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(x * j), $MachinePrecision] - N[(z * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * b), $MachinePrecision] - N[(y1 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * c), $MachinePrecision] - N[(y1 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * b), $MachinePrecision] - N[(y5 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * c), $MachinePrecision] - N[(y5 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * y1), $MachinePrecision] - N[(y5 * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 30.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (+
  (-
   (+
    (+
     (-
      (* (- (* x y) (* z t)) (- (* a b) (* c i)))
      (* (- (* x j) (* z k)) (- (* y0 b) (* y1 i))))
     (* (- (* x y2) (* z y3)) (- (* y0 c) (* y1 a))))
    (* (- (* t j) (* y k)) (- (* y4 b) (* y5 i))))
   (* (- (* t y2) (* y y3)) (- (* y4 c) (* y5 a))))
  (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    code = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)))

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	return Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * y) - Float64(z * t)) * Float64(Float64(a * b) - Float64(c * i))) - Float64(Float64(Float64(x * j) - Float64(z * k)) * Float64(Float64(y0 * b) - Float64(y1 * i)))) + Float64(Float64(Float64(x * y2) - Float64(z * y3)) * Float64(Float64(y0 * c) - Float64(y1 * a)))) + Float64(Float64(Float64(t * j) - Float64(y * k)) * Float64(Float64(y4 * b) - Float64(y5 * i)))) - Float64(Float64(Float64(t * y2) - Float64(y * y3)) * Float64(Float64(y4 * c) - Float64(y5 * a)))) + Float64(Float64(Float64(k * y2) - Float64(j * y3)) * Float64(Float64(y4 * y1) - Float64(y5 * y0))))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(N[(N[(N[(N[(N[(N[(N[(x * y), $MachinePrecision] - N[(z * t), $MachinePrecision]), $MachinePrecision] * N[(N[(a * b), $MachinePrecision] - N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(x * j), $MachinePrecision] - N[(z * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * b), $MachinePrecision] - N[(y1 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * c), $MachinePrecision] - N[(y1 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * b), $MachinePrecision] - N[(y5 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * c), $MachinePrecision] - N[(y5 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * y1), $MachinePrecision] - N[(y5 * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 54.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right)\\ \mathbf{if}\;t\_1 \leq \infty:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) + y3 \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1
         (+
          (-
           (+
            (+
             (-
              (* (- (* x y) (* z t)) (- (* a b) (* c i)))
              (* (- (* x j) (* z k)) (- (* y0 b) (* y1 i))))
             (* (- (* x y2) (* z y3)) (- (* y0 c) (* y1 a))))
            (* (- (* t j) (* y k)) (- (* y4 b) (* y5 i))))
           (* (- (* t y2) (* y y3)) (- (* y4 c) (* y5 a))))
          (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0))))))
   (if (<= t_1 INFINITY)
     t_1
     (*
      (+
       (fma (- k) (fma y4 b (* (- i) y5)) (* (fma b a (* (- c) i)) x))
       (* y3 (fma y4 c (* (- a) y5))))
      y))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
	double tmp;
	if (t_1 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = (fma(-k, fma(y4, b, (-i * y5)), (fma(b, a, (-c * i)) * x)) + (y3 * fma(y4, c, (-a * y5)))) * y;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * y) - Float64(z * t)) * Float64(Float64(a * b) - Float64(c * i))) - Float64(Float64(Float64(x * j) - Float64(z * k)) * Float64(Float64(y0 * b) - Float64(y1 * i)))) + Float64(Float64(Float64(x * y2) - Float64(z * y3)) * Float64(Float64(y0 * c) - Float64(y1 * a)))) + Float64(Float64(Float64(t * j) - Float64(y * k)) * Float64(Float64(y4 * b) - Float64(y5 * i)))) - Float64(Float64(Float64(t * y2) - Float64(y * y3)) * Float64(Float64(y4 * c) - Float64(y5 * a)))) + Float64(Float64(Float64(k * y2) - Float64(j * y3)) * Float64(Float64(y4 * y1) - Float64(y5 * y0))))
	tmp = 0.0
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = Float64(Float64(fma(Float64(-k), fma(y4, b, Float64(Float64(-i) * y5)), Float64(fma(b, a, Float64(Float64(-c) * i)) * x)) + Float64(y3 * fma(y4, c, Float64(Float64(-a) * y5)))) * y);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(N[(N[(N[(N[(N[(N[(x * y), $MachinePrecision] - N[(z * t), $MachinePrecision]), $MachinePrecision] * N[(N[(a * b), $MachinePrecision] - N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(x * j), $MachinePrecision] - N[(z * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * b), $MachinePrecision] - N[(y1 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * c), $MachinePrecision] - N[(y1 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * b), $MachinePrecision] - N[(y5 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * c), $MachinePrecision] - N[(y5 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * y1), $MachinePrecision] - N[(y5 * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, Infinity], t$95$1, N[(N[(N[((-k) * N[(y4 * b + N[((-i) * y5), $MachinePrecision]), $MachinePrecision] + N[(N[(b * a + N[((-c) * i), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision] + N[(y3 * N[(y4 * c + N[((-a) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (+.f64 (+.f64 (-.f64 (*.f64 (-.f64 (*.f64 x y) (*.f64 z t)) (-.f64 (*.f64 a b) (*.f64 c i))) (*.f64 (-.f64 (*.f64 x j) (*.f64 z k)) (-.f64 (*.f64 y0 b) (*.f64 y1 i)))) (*.f64 (-.f64 (*.f64 x y2) (*.f64 z y3)) (-.f64 (*.f64 y0 c) (*.f64 y1 a)))) (*.f64 (-.f64 (*.f64 t j) (*.f64 y k)) (-.f64 (*.f64 y4 b) (*.f64 y5 i)))) (*.f64 (-.f64 (*.f64 t y2) (*.f64 y y3)) (-.f64 (*.f64 y4 c) (*.f64 y5 a)))) (*.f64 (-.f64 (*.f64 k y2) (*.f64 j y3)) (-.f64 (*.f64 y4 y1) (*.f64 y5 y0)))) < +inf.0

   1. Initial program 90.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   if +inf.0 < (+.f64 (-.f64 (+.f64 (+.f64 (-.f64 (*.f64 (-.f64 (*.f64 x y) (*.f64 z t)) (-.f64 (*.f64 a b) (*.f64 c i))) (*.f64 (-.f64 (*.f64 x j) (*.f64 z k)) (-.f64 (*.f64 y0 b) (*.f64 y1 i)))) (*.f64 (-.f64 (*.f64 x y2) (*.f64 z y3)) (-.f64 (*.f64 y0 c) (*.f64 y1 a)))) (*.f64 (-.f64 (*.f64 t j) (*.f64 y k)) (-.f64 (*.f64 y4 b) (*.f64 y5 i)))) (*.f64 (-.f64 (*.f64 t y2) (*.f64 y y3)) (-.f64 (*.f64 y4 c) (*.f64 y5 a)))) (*.f64 (-.f64 (*.f64 k y2) (*.f64 j y3)) (-.f64 (*.f64 y4 y1) (*.f64 y5 y0))))

   1. Initial program 0.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

   5. Applied rewrites 45.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) - \left(-y3\right) \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y} \]
3. Recombined 2 regimes into one program.

4. Final simplification 61.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \leq \infty:\\ \;\;\;\;\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) + y3 \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 44.2% accurate, 2.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right)\\ t_2 := \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right)\\ t_3 := \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, t\_1 \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ t_4 := \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right)\\ t_5 := \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right)\\ \mathbf{if}\;x \leq -1.3 \cdot 10^{+79}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;x \leq -7.8 \cdot 10^{-106}:\\ \;\;\;\;\left(-y5\right) \cdot \left(\mathsf{fma}\left(t\_2, y0, t\_4 \cdot i\right) - t\_5 \cdot a\right)\\ \mathbf{elif}\;x \leq 3 \cdot 10^{-149}:\\ \;\;\;\;\left(\mathsf{fma}\left(t\_2, y1, t\_4 \cdot b\right) - t\_5 \cdot c\right) \cdot y4\\ \mathbf{elif}\;x \leq 4.6 \cdot 10^{-40}:\\ \;\;\;\;\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), t\_1 \cdot x\right) + y3 \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (fma b a (* (- c) i)))
        (t_2 (fma y2 k (* (- j) y3)))
        (t_3
         (*
          (-
           (fma (fma y0 c (* (- a) y1)) y2 (* t_1 y))
           (* (fma y0 b (* (- i) y1)) j))
          x))
        (t_4 (fma j t (* (- k) y)))
        (t_5 (fma y2 t (* (- y) y3))))
   (if (<= x -1.3e+79)
     t_3
     (if (<= x -7.8e-106)
       (* (- y5) (- (fma t_2 y0 (* t_4 i)) (* t_5 a)))
       (if (<= x 3e-149)
         (* (- (fma t_2 y1 (* t_4 b)) (* t_5 c)) y4)
         (if (<= x 4.6e-40)
           (*
            (+
             (fma (- k) (fma y4 b (* (- i) y5)) (* t_1 x))
             (* y3 (fma y4 c (* (- a) y5))))
            y)
           t_3))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = fma(b, a, (-c * i));
	double t_2 = fma(y2, k, (-j * y3));
	double t_3 = (fma(fma(y0, c, (-a * y1)), y2, (t_1 * y)) - (fma(y0, b, (-i * y1)) * j)) * x;
	double t_4 = fma(j, t, (-k * y));
	double t_5 = fma(y2, t, (-y * y3));
	double tmp;
	if (x <= -1.3e+79) {
		tmp = t_3;
	} else if (x <= -7.8e-106) {
		tmp = -y5 * (fma(t_2, y0, (t_4 * i)) - (t_5 * a));
	} else if (x <= 3e-149) {
		tmp = (fma(t_2, y1, (t_4 * b)) - (t_5 * c)) * y4;
	} else if (x <= 4.6e-40) {
		tmp = (fma(-k, fma(y4, b, (-i * y5)), (t_1 * x)) + (y3 * fma(y4, c, (-a * y5)))) * y;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = fma(b, a, Float64(Float64(-c) * i))
	t_2 = fma(y2, k, Float64(Float64(-j) * y3))
	t_3 = Float64(Float64(fma(fma(y0, c, Float64(Float64(-a) * y1)), y2, Float64(t_1 * y)) - Float64(fma(y0, b, Float64(Float64(-i) * y1)) * j)) * x)
	t_4 = fma(j, t, Float64(Float64(-k) * y))
	t_5 = fma(y2, t, Float64(Float64(-y) * y3))
	tmp = 0.0
	if (x <= -1.3e+79)
		tmp = t_3;
	elseif (x <= -7.8e-106)
		tmp = Float64(Float64(-y5) * Float64(fma(t_2, y0, Float64(t_4 * i)) - Float64(t_5 * a)));
	elseif (x <= 3e-149)
		tmp = Float64(Float64(fma(t_2, y1, Float64(t_4 * b)) - Float64(t_5 * c)) * y4);
	elseif (x <= 4.6e-40)
		tmp = Float64(Float64(fma(Float64(-k), fma(y4, b, Float64(Float64(-i) * y5)), Float64(t_1 * x)) + Float64(y3 * fma(y4, c, Float64(Float64(-a) * y5)))) * y);
	else
		tmp = t_3;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(b * a + N[((-c) * i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y2 * k + N[((-j) * y3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(N[(y0 * c + N[((-a) * y1), $MachinePrecision]), $MachinePrecision] * y2 + N[(t$95$1 * y), $MachinePrecision]), $MachinePrecision] - N[(N[(y0 * b + N[((-i) * y1), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, Block[{t$95$4 = N[(j * t + N[((-k) * y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(y2 * t + N[((-y) * y3), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.3e+79], t$95$3, If[LessEqual[x, -7.8e-106], N[((-y5) * N[(N[(t$95$2 * y0 + N[(t$95$4 * i), $MachinePrecision]), $MachinePrecision] - N[(t$95$5 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3e-149], N[(N[(N[(t$95$2 * y1 + N[(t$95$4 * b), $MachinePrecision]), $MachinePrecision] - N[(t$95$5 * c), $MachinePrecision]), $MachinePrecision] * y4), $MachinePrecision], If[LessEqual[x, 4.6e-40], N[(N[(N[((-k) * N[(y4 * b + N[((-i) * y5), $MachinePrecision]), $MachinePrecision] + N[(t$95$1 * x), $MachinePrecision]), $MachinePrecision] + N[(y3 * N[(y4 * c + N[((-a) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], t$95$3]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -1.30000000000000007e79 or 4.6e-40 < x

   1. Initial program 29.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 64.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   if -1.30000000000000007e79 < x < -7.80000000000000019e-106

   1. Initial program 32.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 62.8%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   if -7.80000000000000019e-106 < x < 3.0000000000000002e-149

   1. Initial program 42.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y4 around inf

      \[\leadsto \color{blue}{y4 \cdot \left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot y4} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot y4} \]

   5. Applied rewrites 55.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y1, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot b\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot c\right) \cdot y4} \]

   if 3.0000000000000002e-149 < x < 4.6e-40

   1. Initial program 17.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

   5. Applied rewrites 54.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) - \left(-y3\right) \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y} \]
3. Recombined 4 regimes into one program.

4. Final simplification 61.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.3 \cdot 10^{+79}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ \mathbf{elif}\;x \leq -7.8 \cdot 10^{-106}:\\ \;\;\;\;\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)\\ \mathbf{elif}\;x \leq 3 \cdot 10^{-149}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y1, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot b\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot c\right) \cdot y4\\ \mathbf{elif}\;x \leq 4.6 \cdot 10^{-40}:\\ \;\;\;\;\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) + y3 \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 43.9% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right)\\ t_2 := \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ t_3 := \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right)\\ t_4 := \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right)\\ \mathbf{if}\;x \leq -1.3 \cdot 10^{+79}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;x \leq -7.8 \cdot 10^{-106}:\\ \;\;\;\;\left(-y5\right) \cdot \left(\mathsf{fma}\left(t\_1, y0, t\_3 \cdot i\right) - t\_4 \cdot a\right)\\ \mathbf{elif}\;x \leq 3.3 \cdot 10^{-181}:\\ \;\;\;\;\left(\mathsf{fma}\left(t\_1, y1, t\_3 \cdot b\right) - t\_4 \cdot c\right) \cdot y4\\ \mathbf{elif}\;x \leq 2.4 \cdot 10^{-54}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, t\_3 \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (fma y2 k (* (- j) y3)))
        (t_2
         (*
          (-
           (fma (fma y0 c (* (- a) y1)) y2 (* (fma b a (* (- c) i)) y))
           (* (fma y0 b (* (- i) y1)) j))
          x))
        (t_3 (fma j t (* (- k) y)))
        (t_4 (fma y2 t (* (- y) y3))))
   (if (<= x -1.3e+79)
     t_2
     (if (<= x -7.8e-106)
       (* (- y5) (- (fma t_1 y0 (* t_3 i)) (* t_4 a)))
       (if (<= x 3.3e-181)
         (* (- (fma t_1 y1 (* t_3 b)) (* t_4 c)) y4)
         (if (<= x 2.4e-54)
           (*
            (-
             (fma (fma y x (* (- t) z)) a (* t_3 y4))
             (* (fma j x (* (- k) z)) y0))
            b)
           t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = fma(y2, k, (-j * y3));
	double t_2 = (fma(fma(y0, c, (-a * y1)), y2, (fma(b, a, (-c * i)) * y)) - (fma(y0, b, (-i * y1)) * j)) * x;
	double t_3 = fma(j, t, (-k * y));
	double t_4 = fma(y2, t, (-y * y3));
	double tmp;
	if (x <= -1.3e+79) {
		tmp = t_2;
	} else if (x <= -7.8e-106) {
		tmp = -y5 * (fma(t_1, y0, (t_3 * i)) - (t_4 * a));
	} else if (x <= 3.3e-181) {
		tmp = (fma(t_1, y1, (t_3 * b)) - (t_4 * c)) * y4;
	} else if (x <= 2.4e-54) {
		tmp = (fma(fma(y, x, (-t * z)), a, (t_3 * y4)) - (fma(j, x, (-k * z)) * y0)) * b;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = fma(y2, k, Float64(Float64(-j) * y3))
	t_2 = Float64(Float64(fma(fma(y0, c, Float64(Float64(-a) * y1)), y2, Float64(fma(b, a, Float64(Float64(-c) * i)) * y)) - Float64(fma(y0, b, Float64(Float64(-i) * y1)) * j)) * x)
	t_3 = fma(j, t, Float64(Float64(-k) * y))
	t_4 = fma(y2, t, Float64(Float64(-y) * y3))
	tmp = 0.0
	if (x <= -1.3e+79)
		tmp = t_2;
	elseif (x <= -7.8e-106)
		tmp = Float64(Float64(-y5) * Float64(fma(t_1, y0, Float64(t_3 * i)) - Float64(t_4 * a)));
	elseif (x <= 3.3e-181)
		tmp = Float64(Float64(fma(t_1, y1, Float64(t_3 * b)) - Float64(t_4 * c)) * y4);
	elseif (x <= 2.4e-54)
		tmp = Float64(Float64(fma(fma(y, x, Float64(Float64(-t) * z)), a, Float64(t_3 * y4)) - Float64(fma(j, x, Float64(Float64(-k) * z)) * y0)) * b);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(y2 * k + N[((-j) * y3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(y0 * c + N[((-a) * y1), $MachinePrecision]), $MachinePrecision] * y2 + N[(N[(b * a + N[((-c) * i), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] - N[(N[(y0 * b + N[((-i) * y1), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, Block[{t$95$3 = N[(j * t + N[((-k) * y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(y2 * t + N[((-y) * y3), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.3e+79], t$95$2, If[LessEqual[x, -7.8e-106], N[((-y5) * N[(N[(t$95$1 * y0 + N[(t$95$3 * i), $MachinePrecision]), $MachinePrecision] - N[(t$95$4 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.3e-181], N[(N[(N[(t$95$1 * y1 + N[(t$95$3 * b), $MachinePrecision]), $MachinePrecision] - N[(t$95$4 * c), $MachinePrecision]), $MachinePrecision] * y4), $MachinePrecision], If[LessEqual[x, 2.4e-54], N[(N[(N[(N[(y * x + N[((-t) * z), $MachinePrecision]), $MachinePrecision] * a + N[(t$95$3 * y4), $MachinePrecision]), $MachinePrecision] - N[(N[(j * x + N[((-k) * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], t$95$2]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -1.30000000000000007e79 or 2.40000000000000013e-54 < x

   1. Initial program 29.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 64.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   if -1.30000000000000007e79 < x < -7.80000000000000019e-106

   1. Initial program 32.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 62.8%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   if -7.80000000000000019e-106 < x < 3.30000000000000009e-181

   1. Initial program 42.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y4 around inf

      \[\leadsto \color{blue}{y4 \cdot \left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot y4} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot y4} \]

   5. Applied rewrites 53.4%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y1, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot b\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot c\right) \cdot y4} \]

   if 3.30000000000000009e-181 < x < 2.40000000000000013e-54

   1. Initial program 21.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 62.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 4: 43.4% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right)\\ t_2 := \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ \mathbf{if}\;x \leq -4 \cdot 10^{+78}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;x \leq -1.05:\\ \;\;\;\;\left(y5 \cdot \mathsf{fma}\left(-1, i \cdot t, y0 \cdot y3\right)\right) \cdot j\\ \mathbf{elif}\;x \leq 3.3 \cdot 10^{-181}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y1, t\_1 \cdot b\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot c\right) \cdot y4\\ \mathbf{elif}\;x \leq 2.4 \cdot 10^{-54}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, t\_1 \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (fma j t (* (- k) y)))
        (t_2
         (*
          (-
           (fma (fma y0 c (* (- a) y1)) y2 (* (fma b a (* (- c) i)) y))
           (* (fma y0 b (* (- i) y1)) j))
          x)))
   (if (<= x -4e+78)
     t_2
     (if (<= x -1.05)
       (* (* y5 (fma -1.0 (* i t) (* y0 y3))) j)
       (if (<= x 3.3e-181)
         (*
          (-
           (fma (fma y2 k (* (- j) y3)) y1 (* t_1 b))
           (* (fma y2 t (* (- y) y3)) c))
          y4)
         (if (<= x 2.4e-54)
           (*
            (-
             (fma (fma y x (* (- t) z)) a (* t_1 y4))
             (* (fma j x (* (- k) z)) y0))
            b)
           t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = fma(j, t, (-k * y));
	double t_2 = (fma(fma(y0, c, (-a * y1)), y2, (fma(b, a, (-c * i)) * y)) - (fma(y0, b, (-i * y1)) * j)) * x;
	double tmp;
	if (x <= -4e+78) {
		tmp = t_2;
	} else if (x <= -1.05) {
		tmp = (y5 * fma(-1.0, (i * t), (y0 * y3))) * j;
	} else if (x <= 3.3e-181) {
		tmp = (fma(fma(y2, k, (-j * y3)), y1, (t_1 * b)) - (fma(y2, t, (-y * y3)) * c)) * y4;
	} else if (x <= 2.4e-54) {
		tmp = (fma(fma(y, x, (-t * z)), a, (t_1 * y4)) - (fma(j, x, (-k * z)) * y0)) * b;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = fma(j, t, Float64(Float64(-k) * y))
	t_2 = Float64(Float64(fma(fma(y0, c, Float64(Float64(-a) * y1)), y2, Float64(fma(b, a, Float64(Float64(-c) * i)) * y)) - Float64(fma(y0, b, Float64(Float64(-i) * y1)) * j)) * x)
	tmp = 0.0
	if (x <= -4e+78)
		tmp = t_2;
	elseif (x <= -1.05)
		tmp = Float64(Float64(y5 * fma(-1.0, Float64(i * t), Float64(y0 * y3))) * j);
	elseif (x <= 3.3e-181)
		tmp = Float64(Float64(fma(fma(y2, k, Float64(Float64(-j) * y3)), y1, Float64(t_1 * b)) - Float64(fma(y2, t, Float64(Float64(-y) * y3)) * c)) * y4);
	elseif (x <= 2.4e-54)
		tmp = Float64(Float64(fma(fma(y, x, Float64(Float64(-t) * z)), a, Float64(t_1 * y4)) - Float64(fma(j, x, Float64(Float64(-k) * z)) * y0)) * b);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(j * t + N[((-k) * y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(y0 * c + N[((-a) * y1), $MachinePrecision]), $MachinePrecision] * y2 + N[(N[(b * a + N[((-c) * i), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] - N[(N[(y0 * b + N[((-i) * y1), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -4e+78], t$95$2, If[LessEqual[x, -1.05], N[(N[(y5 * N[(-1.0 * N[(i * t), $MachinePrecision] + N[(y0 * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision], If[LessEqual[x, 3.3e-181], N[(N[(N[(N[(y2 * k + N[((-j) * y3), $MachinePrecision]), $MachinePrecision] * y1 + N[(t$95$1 * b), $MachinePrecision]), $MachinePrecision] - N[(N[(y2 * t + N[((-y) * y3), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision]), $MachinePrecision] * y4), $MachinePrecision], If[LessEqual[x, 2.4e-54], N[(N[(N[(N[(y * x + N[((-t) * z), $MachinePrecision]), $MachinePrecision] * a + N[(t$95$1 * y4), $MachinePrecision]), $MachinePrecision] - N[(N[(j * x + N[((-k) * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], t$95$2]]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -4.00000000000000003e78 or 2.40000000000000013e-54 < x

   1. Initial program 29.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 64.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   if -4.00000000000000003e78 < x < -1.05000000000000004

   1. Initial program 33.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in j around inf

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

   5. Applied rewrites 55.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y3, \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot x\right) \cdot j} \]

   6. Taylor expanded in y5 around inf

      \[\leadsto \left(y5 \cdot \left(-1 \cdot \left(i \cdot t\right) + y0 \cdot y3\right)\right) \cdot j \]
   7. Step-by-step derivation

   8. Applied rewrites 67.3%

      \[\leadsto \left(y5 \cdot \mathsf{fma}\left(-1, i \cdot t, y0 \cdot y3\right)\right) \cdot j \]

   if -1.05000000000000004 < x < 3.30000000000000009e-181

   1. Initial program 40.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y4 around inf

      \[\leadsto \color{blue}{y4 \cdot \left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot y4} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot y4} \]

   5. Applied rewrites 49.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y1, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot b\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot c\right) \cdot y4} \]

   if 3.30000000000000009e-181 < x < 2.40000000000000013e-54

   1. Initial program 21.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 62.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 5: 41.7% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ \mathbf{if}\;x \leq -4 \cdot 10^{+78}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq -7.2 \cdot 10^{-77}:\\ \;\;\;\;\left(y5 \cdot \mathsf{fma}\left(-1, i \cdot t, y0 \cdot y3\right)\right) \cdot j\\ \mathbf{elif}\;x \leq 2.4 \cdot 10^{-54}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1
         (*
          (-
           (fma (fma y0 c (* (- a) y1)) y2 (* (fma b a (* (- c) i)) y))
           (* (fma y0 b (* (- i) y1)) j))
          x)))
   (if (<= x -4e+78)
     t_1
     (if (<= x -7.2e-77)
       (* (* y5 (fma -1.0 (* i t) (* y0 y3))) j)
       (if (<= x 2.4e-54)
         (*
          (-
           (fma (fma y x (* (- t) z)) a (* (fma j t (* (- k) y)) y4))
           (* (fma j x (* (- k) z)) y0))
          b)
         t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (fma(fma(y0, c, (-a * y1)), y2, (fma(b, a, (-c * i)) * y)) - (fma(y0, b, (-i * y1)) * j)) * x;
	double tmp;
	if (x <= -4e+78) {
		tmp = t_1;
	} else if (x <= -7.2e-77) {
		tmp = (y5 * fma(-1.0, (i * t), (y0 * y3))) * j;
	} else if (x <= 2.4e-54) {
		tmp = (fma(fma(y, x, (-t * z)), a, (fma(j, t, (-k * y)) * y4)) - (fma(j, x, (-k * z)) * y0)) * b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(fma(fma(y0, c, Float64(Float64(-a) * y1)), y2, Float64(fma(b, a, Float64(Float64(-c) * i)) * y)) - Float64(fma(y0, b, Float64(Float64(-i) * y1)) * j)) * x)
	tmp = 0.0
	if (x <= -4e+78)
		tmp = t_1;
	elseif (x <= -7.2e-77)
		tmp = Float64(Float64(y5 * fma(-1.0, Float64(i * t), Float64(y0 * y3))) * j);
	elseif (x <= 2.4e-54)
		tmp = Float64(Float64(fma(fma(y, x, Float64(Float64(-t) * z)), a, Float64(fma(j, t, Float64(Float64(-k) * y)) * y4)) - Float64(fma(j, x, Float64(Float64(-k) * z)) * y0)) * b);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(N[(N[(y0 * c + N[((-a) * y1), $MachinePrecision]), $MachinePrecision] * y2 + N[(N[(b * a + N[((-c) * i), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] - N[(N[(y0 * b + N[((-i) * y1), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -4e+78], t$95$1, If[LessEqual[x, -7.2e-77], N[(N[(y5 * N[(-1.0 * N[(i * t), $MachinePrecision] + N[(y0 * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision], If[LessEqual[x, 2.4e-54], N[(N[(N[(N[(y * x + N[((-t) * z), $MachinePrecision]), $MachinePrecision] * a + N[(N[(j * t + N[((-k) * y), $MachinePrecision]), $MachinePrecision] * y4), $MachinePrecision]), $MachinePrecision] - N[(N[(j * x + N[((-k) * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if x < -4.00000000000000003e78 or 2.40000000000000013e-54 < x

   1. Initial program 29.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 64.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   if -4.00000000000000003e78 < x < -7.2e-77

   1. Initial program 31.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in j around inf

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

   5. Applied rewrites 44.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y3, \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot x\right) \cdot j} \]

   6. Taylor expanded in y5 around inf

      \[\leadsto \left(y5 \cdot \left(-1 \cdot \left(i \cdot t\right) + y0 \cdot y3\right)\right) \cdot j \]
   7. Step-by-step derivation

   8. Applied rewrites 56.9%

      \[\leadsto \left(y5 \cdot \mathsf{fma}\left(-1, i \cdot t, y0 \cdot y3\right)\right) \cdot j \]

   if -7.2e-77 < x < 2.40000000000000013e-54

   1. Initial program 36.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 43.7%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 6: 39.3% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y \cdot \mathsf{fma}\left(-c, i, a \cdot b\right) - j \cdot \mathsf{fma}\left(b, y0, \left(-i\right) \cdot y1\right)\right) \cdot x\\ \mathbf{if}\;x \leq -4 \cdot 10^{+78}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq -7.2 \cdot 10^{-77}:\\ \;\;\;\;\left(y5 \cdot \mathsf{fma}\left(-1, i \cdot t, y0 \cdot y3\right)\right) \cdot j\\ \mathbf{elif}\;x \leq 2.5 \cdot 10^{-54}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1
         (* (- (* y (fma (- c) i (* a b))) (* j (fma b y0 (* (- i) y1)))) x)))
   (if (<= x -4e+78)
     t_1
     (if (<= x -7.2e-77)
       (* (* y5 (fma -1.0 (* i t) (* y0 y3))) j)
       (if (<= x 2.5e-54)
         (*
          (-
           (fma (fma y x (* (- t) z)) a (* (fma j t (* (- k) y)) y4))
           (* (fma j x (* (- k) z)) y0))
          b)
         t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = ((y * fma(-c, i, (a * b))) - (j * fma(b, y0, (-i * y1)))) * x;
	double tmp;
	if (x <= -4e+78) {
		tmp = t_1;
	} else if (x <= -7.2e-77) {
		tmp = (y5 * fma(-1.0, (i * t), (y0 * y3))) * j;
	} else if (x <= 2.5e-54) {
		tmp = (fma(fma(y, x, (-t * z)), a, (fma(j, t, (-k * y)) * y4)) - (fma(j, x, (-k * z)) * y0)) * b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(Float64(y * fma(Float64(-c), i, Float64(a * b))) - Float64(j * fma(b, y0, Float64(Float64(-i) * y1)))) * x)
	tmp = 0.0
	if (x <= -4e+78)
		tmp = t_1;
	elseif (x <= -7.2e-77)
		tmp = Float64(Float64(y5 * fma(-1.0, Float64(i * t), Float64(y0 * y3))) * j);
	elseif (x <= 2.5e-54)
		tmp = Float64(Float64(fma(fma(y, x, Float64(Float64(-t) * z)), a, Float64(fma(j, t, Float64(Float64(-k) * y)) * y4)) - Float64(fma(j, x, Float64(Float64(-k) * z)) * y0)) * b);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(N[(y * N[((-c) * i + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(j * N[(b * y0 + N[((-i) * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -4e+78], t$95$1, If[LessEqual[x, -7.2e-77], N[(N[(y5 * N[(-1.0 * N[(i * t), $MachinePrecision] + N[(y0 * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision], If[LessEqual[x, 2.5e-54], N[(N[(N[(N[(y * x + N[((-t) * z), $MachinePrecision]), $MachinePrecision] * a + N[(N[(j * t + N[((-k) * y), $MachinePrecision]), $MachinePrecision] * y4), $MachinePrecision]), $MachinePrecision] - N[(N[(j * x + N[((-k) * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if x < -4.00000000000000003e78 or 2.50000000000000008e-54 < x

   1. Initial program 29.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 64.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   6. Taylor expanded in y2 around 0

      \[\leadsto \left(y \cdot \left(-1 \cdot \left(c \cdot i\right) + a \cdot b\right) - j \cdot \left(-1 \cdot \left(i \cdot y1\right) + b \cdot y0\right)\right) \cdot x \]
   7. Step-by-step derivation

   8. Applied rewrites 53.6%

      \[\leadsto \left(y \cdot \mathsf{fma}\left(-c, i, a \cdot b\right) - j \cdot \mathsf{fma}\left(b, y0, -i \cdot y1\right)\right) \cdot x \]

   if -4.00000000000000003e78 < x < -7.2e-77

   1. Initial program 31.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in j around inf

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

   5. Applied rewrites 44.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y3, \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot x\right) \cdot j} \]

   6. Taylor expanded in y5 around inf

      \[\leadsto \left(y5 \cdot \left(-1 \cdot \left(i \cdot t\right) + y0 \cdot y3\right)\right) \cdot j \]
   7. Step-by-step derivation

   8. Applied rewrites 56.9%

      \[\leadsto \left(y5 \cdot \mathsf{fma}\left(-1, i \cdot t, y0 \cdot y3\right)\right) \cdot j \]

   if -7.2e-77 < x < 2.50000000000000008e-54

   1. Initial program 36.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 43.7%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]
3. Recombined 3 regimes into one program.

4. Final simplification 50.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{+78}:\\ \;\;\;\;\left(y \cdot \mathsf{fma}\left(-c, i, a \cdot b\right) - j \cdot \mathsf{fma}\left(b, y0, \left(-i\right) \cdot y1\right)\right) \cdot x\\ \mathbf{elif}\;x \leq -7.2 \cdot 10^{-77}:\\ \;\;\;\;\left(y5 \cdot \mathsf{fma}\left(-1, i \cdot t, y0 \cdot y3\right)\right) \cdot j\\ \mathbf{elif}\;x \leq 2.5 \cdot 10^{-54}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(y \cdot \mathsf{fma}\left(-c, i, a \cdot b\right) - j \cdot \mathsf{fma}\left(b, y0, \left(-i\right) \cdot y1\right)\right) \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 35.7% accurate, 3.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y \cdot \mathsf{fma}\left(-c, i, a \cdot b\right) - j \cdot \mathsf{fma}\left(b, y0, \left(-i\right) \cdot y1\right)\right) \cdot x\\ \mathbf{if}\;x \leq -4 \cdot 10^{+78}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq -1 \cdot 10^{-108}:\\ \;\;\;\;\left(y5 \cdot \mathsf{fma}\left(-1, i \cdot t, y0 \cdot y3\right)\right) \cdot j\\ \mathbf{elif}\;x \leq 4.9 \cdot 10^{-54}:\\ \;\;\;\;\left(\left(-y4\right) \cdot \mathsf{fma}\left(b, k, \left(-c\right) \cdot y3\right)\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1
         (* (- (* y (fma (- c) i (* a b))) (* j (fma b y0 (* (- i) y1)))) x)))
   (if (<= x -4e+78)
     t_1
     (if (<= x -1e-108)
       (* (* y5 (fma -1.0 (* i t) (* y0 y3))) j)
       (if (<= x 4.9e-54) (* (* (- y4) (fma b k (* (- c) y3))) y) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = ((y * fma(-c, i, (a * b))) - (j * fma(b, y0, (-i * y1)))) * x;
	double tmp;
	if (x <= -4e+78) {
		tmp = t_1;
	} else if (x <= -1e-108) {
		tmp = (y5 * fma(-1.0, (i * t), (y0 * y3))) * j;
	} else if (x <= 4.9e-54) {
		tmp = (-y4 * fma(b, k, (-c * y3))) * y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(Float64(y * fma(Float64(-c), i, Float64(a * b))) - Float64(j * fma(b, y0, Float64(Float64(-i) * y1)))) * x)
	tmp = 0.0
	if (x <= -4e+78)
		tmp = t_1;
	elseif (x <= -1e-108)
		tmp = Float64(Float64(y5 * fma(-1.0, Float64(i * t), Float64(y0 * y3))) * j);
	elseif (x <= 4.9e-54)
		tmp = Float64(Float64(Float64(-y4) * fma(b, k, Float64(Float64(-c) * y3))) * y);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(N[(y * N[((-c) * i + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(j * N[(b * y0 + N[((-i) * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -4e+78], t$95$1, If[LessEqual[x, -1e-108], N[(N[(y5 * N[(-1.0 * N[(i * t), $MachinePrecision] + N[(y0 * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision], If[LessEqual[x, 4.9e-54], N[(N[((-y4) * N[(b * k + N[((-c) * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if x < -4.00000000000000003e78 or 4.90000000000000021e-54 < x

   1. Initial program 29.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 64.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   6. Taylor expanded in y2 around 0

      \[\leadsto \left(y \cdot \left(-1 \cdot \left(c \cdot i\right) + a \cdot b\right) - j \cdot \left(-1 \cdot \left(i \cdot y1\right) + b \cdot y0\right)\right) \cdot x \]
   7. Step-by-step derivation

   8. Applied rewrites 53.6%

      \[\leadsto \left(y \cdot \mathsf{fma}\left(-c, i, a \cdot b\right) - j \cdot \mathsf{fma}\left(b, y0, -i \cdot y1\right)\right) \cdot x \]

   if -4.00000000000000003e78 < x < -1.00000000000000004e-108

   1. Initial program 32.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in j around inf

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

   5. Applied rewrites 43.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y3, \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot x\right) \cdot j} \]

   6. Taylor expanded in y5 around inf

      \[\leadsto \left(y5 \cdot \left(-1 \cdot \left(i \cdot t\right) + y0 \cdot y3\right)\right) \cdot j \]
   7. Step-by-step derivation

   8. Applied rewrites 50.7%

      \[\leadsto \left(y5 \cdot \mathsf{fma}\left(-1, i \cdot t, y0 \cdot y3\right)\right) \cdot j \]

   if -1.00000000000000004e-108 < x < 4.90000000000000021e-54

   1. Initial program 36.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

   5. Applied rewrites 46.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) - \left(-y3\right) \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y} \]

   6. Taylor expanded in y4 around -inf

      \[\leadsto \left(-1 \cdot \left(y4 \cdot \left(b \cdot k - c \cdot y3\right)\right)\right) \cdot y \]
   7. Step-by-step derivation

   8. Applied rewrites 37.9%

      \[\leadsto \left(-y4 \cdot \mathsf{fma}\left(b, k, \left(-c\right) \cdot y3\right)\right) \cdot y \]
3. Recombined 3 regimes into one program.

4. Final simplification 47.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{+78}:\\ \;\;\;\;\left(y \cdot \mathsf{fma}\left(-c, i, a \cdot b\right) - j \cdot \mathsf{fma}\left(b, y0, \left(-i\right) \cdot y1\right)\right) \cdot x\\ \mathbf{elif}\;x \leq -1 \cdot 10^{-108}:\\ \;\;\;\;\left(y5 \cdot \mathsf{fma}\left(-1, i \cdot t, y0 \cdot y3\right)\right) \cdot j\\ \mathbf{elif}\;x \leq 4.9 \cdot 10^{-54}:\\ \;\;\;\;\left(\left(-y4\right) \cdot \mathsf{fma}\left(b, k, \left(-c\right) \cdot y3\right)\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\left(y \cdot \mathsf{fma}\left(-c, i, a \cdot b\right) - j \cdot \mathsf{fma}\left(b, y0, \left(-i\right) \cdot y1\right)\right) \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 30.3% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{if}\;y5 \leq -1.55 \cdot 10^{+72}:\\ \;\;\;\;\left(y5 \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\right) \cdot y\\ \mathbf{elif}\;y5 \leq -1.7 \cdot 10^{-136}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y5 \leq 1.2 \cdot 10^{-248}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{elif}\;y5 \leq 3.8 \cdot 10^{-189}:\\ \;\;\;\;\left(b \cdot x\right) \cdot \mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)\\ \mathbf{elif}\;y5 \leq 2.5 \cdot 10^{-7}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y5 \leq 1.65 \cdot 10^{+98}:\\ \;\;\;\;\left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* y1 y3) (fma a z (* (- j) y4)))))
   (if (<= y5 -1.55e+72)
     (* (* y5 (fma i k (* (- a) y3))) y)
     (if (<= y5 -1.7e-136)
       t_1
       (if (<= y5 1.2e-248)
         (* (* c x) (fma y0 y2 (* (- i) y)))
         (if (<= y5 3.8e-189)
           (* (* b x) (fma a y (* (- j) y0)))
           (if (<= y5 2.5e-7)
             t_1
             (if (<= y5 1.65e+98)
               (* (* a (fma b x (* (- y3) y5))) y)
               (* (* (* y5 y) i) k)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y1 * y3) * fma(a, z, (-j * y4));
	double tmp;
	if (y5 <= -1.55e+72) {
		tmp = (y5 * fma(i, k, (-a * y3))) * y;
	} else if (y5 <= -1.7e-136) {
		tmp = t_1;
	} else if (y5 <= 1.2e-248) {
		tmp = (c * x) * fma(y0, y2, (-i * y));
	} else if (y5 <= 3.8e-189) {
		tmp = (b * x) * fma(a, y, (-j * y0));
	} else if (y5 <= 2.5e-7) {
		tmp = t_1;
	} else if (y5 <= 1.65e+98) {
		tmp = (a * fma(b, x, (-y3 * y5))) * y;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y1 * y3) * fma(a, z, Float64(Float64(-j) * y4)))
	tmp = 0.0
	if (y5 <= -1.55e+72)
		tmp = Float64(Float64(y5 * fma(i, k, Float64(Float64(-a) * y3))) * y);
	elseif (y5 <= -1.7e-136)
		tmp = t_1;
	elseif (y5 <= 1.2e-248)
		tmp = Float64(Float64(c * x) * fma(y0, y2, Float64(Float64(-i) * y)));
	elseif (y5 <= 3.8e-189)
		tmp = Float64(Float64(b * x) * fma(a, y, Float64(Float64(-j) * y0)));
	elseif (y5 <= 2.5e-7)
		tmp = t_1;
	elseif (y5 <= 1.65e+98)
		tmp = Float64(Float64(a * fma(b, x, Float64(Float64(-y3) * y5))) * y);
	else
		tmp = Float64(Float64(Float64(y5 * y) * i) * k);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y1 * y3), $MachinePrecision] * N[(a * z + N[((-j) * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y5, -1.55e+72], N[(N[(y5 * N[(i * k + N[((-a) * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], If[LessEqual[y5, -1.7e-136], t$95$1, If[LessEqual[y5, 1.2e-248], N[(N[(c * x), $MachinePrecision] * N[(y0 * y2 + N[((-i) * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 3.8e-189], N[(N[(b * x), $MachinePrecision] * N[(a * y + N[((-j) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 2.5e-7], t$95$1, If[LessEqual[y5, 1.65e+98], N[(N[(a * N[(b * x + N[((-y3) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if y5 < -1.54999999999999994e72

   1. Initial program 29.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

   5. Applied rewrites 56.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) - \left(-y3\right) \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y} \]

   6. Taylor expanded in y5 around inf

      \[\leadsto \left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right) \cdot y \]
   7. Step-by-step derivation

   8. Applied rewrites 59.0%

      \[\leadsto \left(y5 \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\right) \cdot y \]

   if -1.54999999999999994e72 < y5 < -1.7e-136 or 3.80000000000000022e-189 < y5 < 2.49999999999999989e-7

   1. Initial program 39.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 54.5%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(y3 \cdot \left(-1 \cdot \left(j \cdot y4\right) + a \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.8%

      \[\leadsto \left(y1 \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(a, z, -j \cdot y4\right)} \]

   if -1.7e-136 < y5 < 1.20000000000000002e-248

   1. Initial program 31.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 46.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   6. Taylor expanded in c around inf

      \[\leadsto c \cdot \color{blue}{\left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 37.7%

      \[\leadsto \left(c \cdot x\right) \cdot \color{blue}{\mathsf{fma}\left(y0, y2, -i \cdot y\right)} \]

   if 1.20000000000000002e-248 < y5 < 3.80000000000000022e-189

   1. Initial program 17.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 59.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in x around inf

      \[\leadsto b \cdot \color{blue}{\left(x \cdot \left(a \cdot y - j \cdot y0\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 67.5%

      \[\leadsto \left(b \cdot x\right) \cdot \color{blue}{\mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)} \]

   if 2.49999999999999989e-7 < y5 < 1.65000000000000014e98

   1. Initial program 37.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

   5. Applied rewrites 49.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) - \left(-y3\right) \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y} \]

   6. Taylor expanded in a around inf

      \[\leadsto \left(a \cdot \left(b \cdot x - y3 \cdot y5\right)\right) \cdot y \]
   7. Step-by-step derivation

   8. Applied rewrites 45.6%

      \[\leadsto \left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y \]

   if 1.65000000000000014e98 < y5

   1. Initial program 27.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 64.5%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.2%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 39.7%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 50.0%

       \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
3. Recombined 6 regimes into one program.

4. Final simplification 48.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y5 \leq -1.55 \cdot 10^{+72}:\\ \;\;\;\;\left(y5 \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\right) \cdot y\\ \mathbf{elif}\;y5 \leq -1.7 \cdot 10^{-136}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{elif}\;y5 \leq 1.2 \cdot 10^{-248}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{elif}\;y5 \leq 3.8 \cdot 10^{-189}:\\ \;\;\;\;\left(b \cdot x\right) \cdot \mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)\\ \mathbf{elif}\;y5 \leq 2.5 \cdot 10^{-7}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{elif}\;y5 \leq 1.65 \cdot 10^{+98}:\\ \;\;\;\;\left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 28.7% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{if}\;y5 \leq -4.7 \cdot 10^{+166}:\\ \;\;\;\;\left(y \cdot y5\right) \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -4 \cdot 10^{+75}:\\ \;\;\;\;\left(y1 \cdot y2\right) \cdot \mathsf{fma}\left(-a, x, k \cdot y4\right)\\ \mathbf{elif}\;y5 \leq -1.7 \cdot 10^{-136}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y5 \leq 1.2 \cdot 10^{-248}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{elif}\;y5 \leq 3.8 \cdot 10^{-189}:\\ \;\;\;\;\left(b \cdot x\right) \cdot \mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)\\ \mathbf{elif}\;y5 \leq 2.9 \cdot 10^{+97}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* y1 y3) (fma a z (* (- j) y4)))))
   (if (<= y5 -4.7e+166)
     (* (* y y5) (fma i k (* (- a) y3)))
     (if (<= y5 -4e+75)
       (* (* y1 y2) (fma (- a) x (* k y4)))
       (if (<= y5 -1.7e-136)
         t_1
         (if (<= y5 1.2e-248)
           (* (* c x) (fma y0 y2 (* (- i) y)))
           (if (<= y5 3.8e-189)
             (* (* b x) (fma a y (* (- j) y0)))
             (if (<= y5 2.9e+97) t_1 (* (* (* y5 y) i) k)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y1 * y3) * fma(a, z, (-j * y4));
	double tmp;
	if (y5 <= -4.7e+166) {
		tmp = (y * y5) * fma(i, k, (-a * y3));
	} else if (y5 <= -4e+75) {
		tmp = (y1 * y2) * fma(-a, x, (k * y4));
	} else if (y5 <= -1.7e-136) {
		tmp = t_1;
	} else if (y5 <= 1.2e-248) {
		tmp = (c * x) * fma(y0, y2, (-i * y));
	} else if (y5 <= 3.8e-189) {
		tmp = (b * x) * fma(a, y, (-j * y0));
	} else if (y5 <= 2.9e+97) {
		tmp = t_1;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y1 * y3) * fma(a, z, Float64(Float64(-j) * y4)))
	tmp = 0.0
	if (y5 <= -4.7e+166)
		tmp = Float64(Float64(y * y5) * fma(i, k, Float64(Float64(-a) * y3)));
	elseif (y5 <= -4e+75)
		tmp = Float64(Float64(y1 * y2) * fma(Float64(-a), x, Float64(k * y4)));
	elseif (y5 <= -1.7e-136)
		tmp = t_1;
	elseif (y5 <= 1.2e-248)
		tmp = Float64(Float64(c * x) * fma(y0, y2, Float64(Float64(-i) * y)));
	elseif (y5 <= 3.8e-189)
		tmp = Float64(Float64(b * x) * fma(a, y, Float64(Float64(-j) * y0)));
	elseif (y5 <= 2.9e+97)
		tmp = t_1;
	else
		tmp = Float64(Float64(Float64(y5 * y) * i) * k);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y1 * y3), $MachinePrecision] * N[(a * z + N[((-j) * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y5, -4.7e+166], N[(N[(y * y5), $MachinePrecision] * N[(i * k + N[((-a) * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, -4e+75], N[(N[(y1 * y2), $MachinePrecision] * N[((-a) * x + N[(k * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, -1.7e-136], t$95$1, If[LessEqual[y5, 1.2e-248], N[(N[(c * x), $MachinePrecision] * N[(y0 * y2 + N[((-i) * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 3.8e-189], N[(N[(b * x), $MachinePrecision] * N[(a * y + N[((-j) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 2.9e+97], t$95$1, N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if y5 < -4.7e166

   1. Initial program 24.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 54.7%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 67.3%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   if -4.7e166 < y5 < -3.99999999999999971e75

   1. Initial program 33.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 53.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(a \cdot y1\right) + c \cdot y0\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 30.4%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(-1, a \cdot y1, c \cdot y0\right)\right) \cdot y2 \]

   9. Taylor expanded in y1 around inf

      \[\leadsto y1 \cdot \color{blue}{\left(y2 \cdot \left(-1 \cdot \left(a \cdot x\right) + k \cdot y4\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 58.0%

       \[\leadsto \left(y1 \cdot y2\right) \cdot \color{blue}{\mathsf{fma}\left(-a, x, k \cdot y4\right)} \]

   if -3.99999999999999971e75 < y5 < -1.7e-136 or 3.80000000000000022e-189 < y5 < 2.89999999999999987e97

   1. Initial program 39.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 53.3%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(y3 \cdot \left(-1 \cdot \left(j \cdot y4\right) + a \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 39.2%

      \[\leadsto \left(y1 \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(a, z, -j \cdot y4\right)} \]

   if -1.7e-136 < y5 < 1.20000000000000002e-248

   1. Initial program 31.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 46.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   6. Taylor expanded in c around inf

      \[\leadsto c \cdot \color{blue}{\left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 37.7%

      \[\leadsto \left(c \cdot x\right) \cdot \color{blue}{\mathsf{fma}\left(y0, y2, -i \cdot y\right)} \]

   if 1.20000000000000002e-248 < y5 < 3.80000000000000022e-189

   1. Initial program 17.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 59.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in x around inf

      \[\leadsto b \cdot \color{blue}{\left(x \cdot \left(a \cdot y - j \cdot y0\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 67.5%

      \[\leadsto \left(b \cdot x\right) \cdot \color{blue}{\mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)} \]

   if 2.89999999999999987e97 < y5

   1. Initial program 27.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 64.5%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.2%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 39.7%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 50.0%

       \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
3. Recombined 6 regimes into one program.

4. Final simplification 47.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y5 \leq -4.7 \cdot 10^{+166}:\\ \;\;\;\;\left(y \cdot y5\right) \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -4 \cdot 10^{+75}:\\ \;\;\;\;\left(y1 \cdot y2\right) \cdot \mathsf{fma}\left(-a, x, k \cdot y4\right)\\ \mathbf{elif}\;y5 \leq -1.7 \cdot 10^{-136}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{elif}\;y5 \leq 1.2 \cdot 10^{-248}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{elif}\;y5 \leq 3.8 \cdot 10^{-189}:\\ \;\;\;\;\left(b \cdot x\right) \cdot \mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)\\ \mathbf{elif}\;y5 \leq 2.9 \cdot 10^{+97}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 29.2% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{if}\;y5 \leq -1.4 \cdot 10^{+170}:\\ \;\;\;\;\left(y \cdot y5\right) \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -2.25 \cdot 10^{+72}:\\ \;\;\;\;\left(a \cdot y5\right) \cdot \mathsf{fma}\left(t, y2, \left(-y\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -1.7 \cdot 10^{-136}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y5 \leq 1.2 \cdot 10^{-248}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{elif}\;y5 \leq 3.8 \cdot 10^{-189}:\\ \;\;\;\;\left(b \cdot x\right) \cdot \mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)\\ \mathbf{elif}\;y5 \leq 2.9 \cdot 10^{+97}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* y1 y3) (fma a z (* (- j) y4)))))
   (if (<= y5 -1.4e+170)
     (* (* y y5) (fma i k (* (- a) y3)))
     (if (<= y5 -2.25e+72)
       (* (* a y5) (fma t y2 (* (- y) y3)))
       (if (<= y5 -1.7e-136)
         t_1
         (if (<= y5 1.2e-248)
           (* (* c x) (fma y0 y2 (* (- i) y)))
           (if (<= y5 3.8e-189)
             (* (* b x) (fma a y (* (- j) y0)))
             (if (<= y5 2.9e+97) t_1 (* (* (* y5 y) i) k)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y1 * y3) * fma(a, z, (-j * y4));
	double tmp;
	if (y5 <= -1.4e+170) {
		tmp = (y * y5) * fma(i, k, (-a * y3));
	} else if (y5 <= -2.25e+72) {
		tmp = (a * y5) * fma(t, y2, (-y * y3));
	} else if (y5 <= -1.7e-136) {
		tmp = t_1;
	} else if (y5 <= 1.2e-248) {
		tmp = (c * x) * fma(y0, y2, (-i * y));
	} else if (y5 <= 3.8e-189) {
		tmp = (b * x) * fma(a, y, (-j * y0));
	} else if (y5 <= 2.9e+97) {
		tmp = t_1;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y1 * y3) * fma(a, z, Float64(Float64(-j) * y4)))
	tmp = 0.0
	if (y5 <= -1.4e+170)
		tmp = Float64(Float64(y * y5) * fma(i, k, Float64(Float64(-a) * y3)));
	elseif (y5 <= -2.25e+72)
		tmp = Float64(Float64(a * y5) * fma(t, y2, Float64(Float64(-y) * y3)));
	elseif (y5 <= -1.7e-136)
		tmp = t_1;
	elseif (y5 <= 1.2e-248)
		tmp = Float64(Float64(c * x) * fma(y0, y2, Float64(Float64(-i) * y)));
	elseif (y5 <= 3.8e-189)
		tmp = Float64(Float64(b * x) * fma(a, y, Float64(Float64(-j) * y0)));
	elseif (y5 <= 2.9e+97)
		tmp = t_1;
	else
		tmp = Float64(Float64(Float64(y5 * y) * i) * k);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y1 * y3), $MachinePrecision] * N[(a * z + N[((-j) * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y5, -1.4e+170], N[(N[(y * y5), $MachinePrecision] * N[(i * k + N[((-a) * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, -2.25e+72], N[(N[(a * y5), $MachinePrecision] * N[(t * y2 + N[((-y) * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, -1.7e-136], t$95$1, If[LessEqual[y5, 1.2e-248], N[(N[(c * x), $MachinePrecision] * N[(y0 * y2 + N[((-i) * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 3.8e-189], N[(N[(b * x), $MachinePrecision] * N[(a * y + N[((-j) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 2.9e+97], t$95$1, N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if y5 < -1.40000000000000008e170

   1. Initial program 21.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 56.4%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 69.3%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   if -1.40000000000000008e170 < y5 < -2.2499999999999999e72

   1. Initial program 39.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 62.1%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 27.8%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around inf

      \[\leadsto a \cdot \color{blue}{\left(y5 \cdot \left(-1 \cdot \left(y \cdot y3\right) + t \cdot y2\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 48.8%

       \[\leadsto \left(a \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(t, y2, -y \cdot y3\right)} \]

   if -2.2499999999999999e72 < y5 < -1.7e-136 or 3.80000000000000022e-189 < y5 < 2.89999999999999987e97

   1. Initial program 38.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 53.9%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(y3 \cdot \left(-1 \cdot \left(j \cdot y4\right) + a \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 39.6%

      \[\leadsto \left(y1 \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(a, z, -j \cdot y4\right)} \]

   if -1.7e-136 < y5 < 1.20000000000000002e-248

   1. Initial program 31.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 46.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   6. Taylor expanded in c around inf

      \[\leadsto c \cdot \color{blue}{\left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 37.7%

      \[\leadsto \left(c \cdot x\right) \cdot \color{blue}{\mathsf{fma}\left(y0, y2, -i \cdot y\right)} \]

   if 1.20000000000000002e-248 < y5 < 3.80000000000000022e-189

   1. Initial program 17.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 59.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in x around inf

      \[\leadsto b \cdot \color{blue}{\left(x \cdot \left(a \cdot y - j \cdot y0\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 67.5%

      \[\leadsto \left(b \cdot x\right) \cdot \color{blue}{\mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)} \]

   if 2.89999999999999987e97 < y5

   1. Initial program 27.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 64.5%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.2%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 39.7%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 50.0%

       \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
3. Recombined 6 regimes into one program.

4. Final simplification 47.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y5 \leq -1.4 \cdot 10^{+170}:\\ \;\;\;\;\left(y \cdot y5\right) \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -2.25 \cdot 10^{+72}:\\ \;\;\;\;\left(a \cdot y5\right) \cdot \mathsf{fma}\left(t, y2, \left(-y\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -1.7 \cdot 10^{-136}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{elif}\;y5 \leq 1.2 \cdot 10^{-248}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{elif}\;y5 \leq 3.8 \cdot 10^{-189}:\\ \;\;\;\;\left(b \cdot x\right) \cdot \mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)\\ \mathbf{elif}\;y5 \leq 2.9 \cdot 10^{+97}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 30.9% accurate, 3.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y5 \leq -1.55 \cdot 10^{+72}:\\ \;\;\;\;\left(y5 \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\right) \cdot y\\ \mathbf{elif}\;y5 \leq -1.7 \cdot 10^{-136}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{elif}\;y5 \leq 1.7 \cdot 10^{-219}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{elif}\;y5 \leq 6.6 \cdot 10^{-8}:\\ \;\;\;\;\left(y4 \cdot \mathsf{fma}\left(-y1, y3, b \cdot t\right)\right) \cdot j\\ \mathbf{elif}\;y5 \leq 1.65 \cdot 10^{+98}:\\ \;\;\;\;\left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y5 -1.55e+72)
   (* (* y5 (fma i k (* (- a) y3))) y)
   (if (<= y5 -1.7e-136)
     (* (* y1 y3) (fma a z (* (- j) y4)))
     (if (<= y5 1.7e-219)
       (* (* c x) (fma y0 y2 (* (- i) y)))
       (if (<= y5 6.6e-8)
         (* (* y4 (fma (- y1) y3 (* b t))) j)
         (if (<= y5 1.65e+98)
           (* (* a (fma b x (* (- y3) y5))) y)
           (* (* (* y5 y) i) k)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (y5 <= -1.55e+72) {
		tmp = (y5 * fma(i, k, (-a * y3))) * y;
	} else if (y5 <= -1.7e-136) {
		tmp = (y1 * y3) * fma(a, z, (-j * y4));
	} else if (y5 <= 1.7e-219) {
		tmp = (c * x) * fma(y0, y2, (-i * y));
	} else if (y5 <= 6.6e-8) {
		tmp = (y4 * fma(-y1, y3, (b * t))) * j;
	} else if (y5 <= 1.65e+98) {
		tmp = (a * fma(b, x, (-y3 * y5))) * y;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (y5 <= -1.55e+72)
		tmp = Float64(Float64(y5 * fma(i, k, Float64(Float64(-a) * y3))) * y);
	elseif (y5 <= -1.7e-136)
		tmp = Float64(Float64(y1 * y3) * fma(a, z, Float64(Float64(-j) * y4)));
	elseif (y5 <= 1.7e-219)
		tmp = Float64(Float64(c * x) * fma(y0, y2, Float64(Float64(-i) * y)));
	elseif (y5 <= 6.6e-8)
		tmp = Float64(Float64(y4 * fma(Float64(-y1), y3, Float64(b * t))) * j);
	elseif (y5 <= 1.65e+98)
		tmp = Float64(Float64(a * fma(b, x, Float64(Float64(-y3) * y5))) * y);
	else
		tmp = Float64(Float64(Float64(y5 * y) * i) * k);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[y5, -1.55e+72], N[(N[(y5 * N[(i * k + N[((-a) * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], If[LessEqual[y5, -1.7e-136], N[(N[(y1 * y3), $MachinePrecision] * N[(a * z + N[((-j) * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 1.7e-219], N[(N[(c * x), $MachinePrecision] * N[(y0 * y2 + N[((-i) * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 6.6e-8], N[(N[(y4 * N[((-y1) * y3 + N[(b * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision], If[LessEqual[y5, 1.65e+98], N[(N[(a * N[(b * x + N[((-y3) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]]]]]]

Derivation

1. Split input into 6 regimes

2. if y5 < -1.54999999999999994e72

   1. Initial program 29.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

   5. Applied rewrites 56.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) - \left(-y3\right) \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y} \]

   6. Taylor expanded in y5 around inf

      \[\leadsto \left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right) \cdot y \]
   7. Step-by-step derivation

   8. Applied rewrites 59.0%

      \[\leadsto \left(y5 \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\right) \cdot y \]

   if -1.54999999999999994e72 < y5 < -1.7e-136

   1. Initial program 43.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 61.7%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(y3 \cdot \left(-1 \cdot \left(j \cdot y4\right) + a \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 41.7%

      \[\leadsto \left(y1 \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(a, z, -j \cdot y4\right)} \]

   if -1.7e-136 < y5 < 1.6999999999999999e-219

   1. Initial program 32.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 47.4%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   6. Taylor expanded in c around inf

      \[\leadsto c \cdot \color{blue}{\left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 38.1%

      \[\leadsto \left(c \cdot x\right) \cdot \color{blue}{\mathsf{fma}\left(y0, y2, -i \cdot y\right)} \]

   if 1.6999999999999999e-219 < y5 < 6.59999999999999954e-8

   1. Initial program 28.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in j around inf

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

   5. Applied rewrites 45.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y3, \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot x\right) \cdot j} \]

   6. Taylor expanded in y0 around inf

      \[\leadsto \left(y0 \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot j \]
   7. Step-by-step derivation

   8. Applied rewrites 20.9%

      \[\leadsto \left(y0 \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot j \]

   9. Taylor expanded in y4 around inf

      \[\leadsto \left(y4 \cdot \left(-1 \cdot \left(y1 \cdot y3\right) + b \cdot t\right)\right) \cdot j \]
   10. Step-by-step derivation

   11. Applied rewrites 50.9%

       \[\leadsto \left(y4 \cdot \mathsf{fma}\left(-y1, y3, b \cdot t\right)\right) \cdot j \]

   if 6.59999999999999954e-8 < y5 < 1.65000000000000014e98

   1. Initial program 37.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

   5. Applied rewrites 49.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) - \left(-y3\right) \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y} \]

   6. Taylor expanded in a around inf

      \[\leadsto \left(a \cdot \left(b \cdot x - y3 \cdot y5\right)\right) \cdot y \]
   7. Step-by-step derivation

   8. Applied rewrites 45.6%

      \[\leadsto \left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y \]

   if 1.65000000000000014e98 < y5

   1. Initial program 27.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 64.5%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.2%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 39.7%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 50.0%

       \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
3. Recombined 6 regimes into one program.

4. Final simplification 47.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y5 \leq -1.55 \cdot 10^{+72}:\\ \;\;\;\;\left(y5 \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\right) \cdot y\\ \mathbf{elif}\;y5 \leq -1.7 \cdot 10^{-136}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{elif}\;y5 \leq 1.7 \cdot 10^{-219}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{elif}\;y5 \leq 6.6 \cdot 10^{-8}:\\ \;\;\;\;\left(y4 \cdot \mathsf{fma}\left(-y1, y3, b \cdot t\right)\right) \cdot j\\ \mathbf{elif}\;y5 \leq 1.65 \cdot 10^{+98}:\\ \;\;\;\;\left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 28.7% accurate, 3.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{if}\;y5 \leq -1.4 \cdot 10^{+170}:\\ \;\;\;\;\left(y \cdot y5\right) \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -2.25 \cdot 10^{+72}:\\ \;\;\;\;\left(a \cdot y5\right) \cdot \mathsf{fma}\left(t, y2, \left(-y\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -9.5 \cdot 10^{-92}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y5 \leq 3.8 \cdot 10^{-189}:\\ \;\;\;\;\left(b \cdot x\right) \cdot \mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)\\ \mathbf{elif}\;y5 \leq 2.9 \cdot 10^{+97}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* y1 y3) (fma a z (* (- j) y4)))))
   (if (<= y5 -1.4e+170)
     (* (* y y5) (fma i k (* (- a) y3)))
     (if (<= y5 -2.25e+72)
       (* (* a y5) (fma t y2 (* (- y) y3)))
       (if (<= y5 -9.5e-92)
         t_1
         (if (<= y5 3.8e-189)
           (* (* b x) (fma a y (* (- j) y0)))
           (if (<= y5 2.9e+97) t_1 (* (* (* y5 y) i) k))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y1 * y3) * fma(a, z, (-j * y4));
	double tmp;
	if (y5 <= -1.4e+170) {
		tmp = (y * y5) * fma(i, k, (-a * y3));
	} else if (y5 <= -2.25e+72) {
		tmp = (a * y5) * fma(t, y2, (-y * y3));
	} else if (y5 <= -9.5e-92) {
		tmp = t_1;
	} else if (y5 <= 3.8e-189) {
		tmp = (b * x) * fma(a, y, (-j * y0));
	} else if (y5 <= 2.9e+97) {
		tmp = t_1;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y1 * y3) * fma(a, z, Float64(Float64(-j) * y4)))
	tmp = 0.0
	if (y5 <= -1.4e+170)
		tmp = Float64(Float64(y * y5) * fma(i, k, Float64(Float64(-a) * y3)));
	elseif (y5 <= -2.25e+72)
		tmp = Float64(Float64(a * y5) * fma(t, y2, Float64(Float64(-y) * y3)));
	elseif (y5 <= -9.5e-92)
		tmp = t_1;
	elseif (y5 <= 3.8e-189)
		tmp = Float64(Float64(b * x) * fma(a, y, Float64(Float64(-j) * y0)));
	elseif (y5 <= 2.9e+97)
		tmp = t_1;
	else
		tmp = Float64(Float64(Float64(y5 * y) * i) * k);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y1 * y3), $MachinePrecision] * N[(a * z + N[((-j) * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y5, -1.4e+170], N[(N[(y * y5), $MachinePrecision] * N[(i * k + N[((-a) * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, -2.25e+72], N[(N[(a * y5), $MachinePrecision] * N[(t * y2 + N[((-y) * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, -9.5e-92], t$95$1, If[LessEqual[y5, 3.8e-189], N[(N[(b * x), $MachinePrecision] * N[(a * y + N[((-j) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 2.9e+97], t$95$1, N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]]]]]]]

Derivation

1. Split input into 5 regimes

2. if y5 < -1.40000000000000008e170

   1. Initial program 21.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 56.4%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 69.3%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   if -1.40000000000000008e170 < y5 < -2.2499999999999999e72

   1. Initial program 39.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 62.1%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 27.8%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around inf

      \[\leadsto a \cdot \color{blue}{\left(y5 \cdot \left(-1 \cdot \left(y \cdot y3\right) + t \cdot y2\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 48.8%

       \[\leadsto \left(a \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(t, y2, -y \cdot y3\right)} \]

   if -2.2499999999999999e72 < y5 < -9.49999999999999946e-92 or 3.80000000000000022e-189 < y5 < 2.89999999999999987e97

   1. Initial program 39.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 54.2%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(y3 \cdot \left(-1 \cdot \left(j \cdot y4\right) + a \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 40.9%

      \[\leadsto \left(y1 \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(a, z, -j \cdot y4\right)} \]

   if -9.49999999999999946e-92 < y5 < 3.80000000000000022e-189

   1. Initial program 29.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 44.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in x around inf

      \[\leadsto b \cdot \color{blue}{\left(x \cdot \left(a \cdot y - j \cdot y0\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 34.4%

      \[\leadsto \left(b \cdot x\right) \cdot \color{blue}{\mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)} \]

   if 2.89999999999999987e97 < y5

   1. Initial program 27.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 64.5%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.2%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 39.7%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 50.0%

       \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
3. Recombined 5 regimes into one program.

4. Final simplification 45.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y5 \leq -1.4 \cdot 10^{+170}:\\ \;\;\;\;\left(y \cdot y5\right) \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -2.25 \cdot 10^{+72}:\\ \;\;\;\;\left(a \cdot y5\right) \cdot \mathsf{fma}\left(t, y2, \left(-y\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -9.5 \cdot 10^{-92}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{elif}\;y5 \leq 3.8 \cdot 10^{-189}:\\ \;\;\;\;\left(b \cdot x\right) \cdot \mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)\\ \mathbf{elif}\;y5 \leq 2.9 \cdot 10^{+97}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 32.8% accurate, 4.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq -2.2 \cdot 10^{+85}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{elif}\;c \leq 8.2 \cdot 10^{-256}:\\ \;\;\;\;\left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y\\ \mathbf{elif}\;c \leq 1.55 \cdot 10^{+36}:\\ \;\;\;\;\left(\left(-z\right) \cdot \mathsf{fma}\left(a, t, \left(-k\right) \cdot y0\right)\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-c\right) \cdot \mathsf{fma}\left(i, x, \left(-y3\right) \cdot y4\right)\right) \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= c -2.2e+85)
   (* (* c x) (fma y0 y2 (* (- i) y)))
   (if (<= c 8.2e-256)
     (* (* a (fma b x (* (- y3) y5))) y)
     (if (<= c 1.55e+36)
       (* (* (- z) (fma a t (* (- k) y0))) b)
       (* (* (- c) (fma i x (* (- y3) y4))) y)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (c <= -2.2e+85) {
		tmp = (c * x) * fma(y0, y2, (-i * y));
	} else if (c <= 8.2e-256) {
		tmp = (a * fma(b, x, (-y3 * y5))) * y;
	} else if (c <= 1.55e+36) {
		tmp = (-z * fma(a, t, (-k * y0))) * b;
	} else {
		tmp = (-c * fma(i, x, (-y3 * y4))) * y;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (c <= -2.2e+85)
		tmp = Float64(Float64(c * x) * fma(y0, y2, Float64(Float64(-i) * y)));
	elseif (c <= 8.2e-256)
		tmp = Float64(Float64(a * fma(b, x, Float64(Float64(-y3) * y5))) * y);
	elseif (c <= 1.55e+36)
		tmp = Float64(Float64(Float64(-z) * fma(a, t, Float64(Float64(-k) * y0))) * b);
	else
		tmp = Float64(Float64(Float64(-c) * fma(i, x, Float64(Float64(-y3) * y4))) * y);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[c, -2.2e+85], N[(N[(c * x), $MachinePrecision] * N[(y0 * y2 + N[((-i) * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 8.2e-256], N[(N[(a * N[(b * x + N[((-y3) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], If[LessEqual[c, 1.55e+36], N[(N[((-z) * N[(a * t + N[((-k) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], N[(N[((-c) * N[(i * x + N[((-y3) * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if c < -2.2000000000000002e85

   1. Initial program 21.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 52.4%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   6. Taylor expanded in c around inf

      \[\leadsto c \cdot \color{blue}{\left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 54.3%

      \[\leadsto \left(c \cdot x\right) \cdot \color{blue}{\mathsf{fma}\left(y0, y2, -i \cdot y\right)} \]

   if -2.2000000000000002e85 < c < 8.2e-256

   1. Initial program 39.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

   5. Applied rewrites 52.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) - \left(-y3\right) \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y} \]

   6. Taylor expanded in a around inf

      \[\leadsto \left(a \cdot \left(b \cdot x - y3 \cdot y5\right)\right) \cdot y \]
   7. Step-by-step derivation

   8. Applied rewrites 47.3%

      \[\leadsto \left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y \]

   if 8.2e-256 < c < 1.55e36

   1. Initial program 35.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 44.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in z around -inf

      \[\leadsto \left(-1 \cdot \left(z \cdot \left(a \cdot t - k \cdot y0\right)\right)\right) \cdot b \]
   7. Step-by-step derivation

   8. Applied rewrites 43.1%

      \[\leadsto \left(-z \cdot \mathsf{fma}\left(a, t, \left(-k\right) \cdot y0\right)\right) \cdot b \]

   if 1.55e36 < c

   1. Initial program 28.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

   5. Applied rewrites 38.7%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) - \left(-y3\right) \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y} \]

   6. Taylor expanded in c around -inf

      \[\leadsto \left(-1 \cdot \left(c \cdot \left(i \cdot x - y3 \cdot y4\right)\right)\right) \cdot y \]
   7. Step-by-step derivation

   8. Applied rewrites 44.2%

      \[\leadsto \left(-c \cdot \mathsf{fma}\left(i, x, \left(-y3\right) \cdot y4\right)\right) \cdot y \]
3. Recombined 4 regimes into one program.

4. Final simplification 47.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;c \leq -2.2 \cdot 10^{+85}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{elif}\;c \leq 8.2 \cdot 10^{-256}:\\ \;\;\;\;\left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y\\ \mathbf{elif}\;c \leq 1.55 \cdot 10^{+36}:\\ \;\;\;\;\left(\left(-z\right) \cdot \mathsf{fma}\left(a, t, \left(-k\right) \cdot y0\right)\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-c\right) \cdot \mathsf{fma}\left(i, x, \left(-y3\right) \cdot y4\right)\right) \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 31.6% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{if}\;c \leq -2.2 \cdot 10^{+85}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;c \leq 1.4 \cdot 10^{+72}:\\ \;\;\;\;\left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y\\ \mathbf{elif}\;c \leq 3.2 \cdot 10^{+134}:\\ \;\;\;\;\left(y1 \cdot y2\right) \cdot \mathsf{fma}\left(-a, x, k \cdot y4\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* c x) (fma y0 y2 (* (- i) y)))))
   (if (<= c -2.2e+85)
     t_1
     (if (<= c 1.4e+72)
       (* (* a (fma b x (* (- y3) y5))) y)
       (if (<= c 3.2e+134) (* (* y1 y2) (fma (- a) x (* k y4))) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (c * x) * fma(y0, y2, (-i * y));
	double tmp;
	if (c <= -2.2e+85) {
		tmp = t_1;
	} else if (c <= 1.4e+72) {
		tmp = (a * fma(b, x, (-y3 * y5))) * y;
	} else if (c <= 3.2e+134) {
		tmp = (y1 * y2) * fma(-a, x, (k * y4));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(c * x) * fma(y0, y2, Float64(Float64(-i) * y)))
	tmp = 0.0
	if (c <= -2.2e+85)
		tmp = t_1;
	elseif (c <= 1.4e+72)
		tmp = Float64(Float64(a * fma(b, x, Float64(Float64(-y3) * y5))) * y);
	elseif (c <= 3.2e+134)
		tmp = Float64(Float64(y1 * y2) * fma(Float64(-a), x, Float64(k * y4)));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(c * x), $MachinePrecision] * N[(y0 * y2 + N[((-i) * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -2.2e+85], t$95$1, If[LessEqual[c, 1.4e+72], N[(N[(a * N[(b * x + N[((-y3) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], If[LessEqual[c, 3.2e+134], N[(N[(y1 * y2), $MachinePrecision] * N[((-a) * x + N[(k * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if c < -2.2000000000000002e85 or 3.2000000000000001e134 < c

   1. Initial program 23.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 45.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   6. Taylor expanded in c around inf

      \[\leadsto c \cdot \color{blue}{\left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 49.1%

      \[\leadsto \left(c \cdot x\right) \cdot \color{blue}{\mathsf{fma}\left(y0, y2, -i \cdot y\right)} \]

   if -2.2000000000000002e85 < c < 1.4e72

   1. Initial program 37.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right) \cdot y} \]

   5. Applied rewrites 47.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-k, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot x\right) - \left(-y3\right) \cdot \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\right) \cdot y} \]

   6. Taylor expanded in a around inf

      \[\leadsto \left(a \cdot \left(b \cdot x - y3 \cdot y5\right)\right) \cdot y \]
   7. Step-by-step derivation

   8. Applied rewrites 36.9%

      \[\leadsto \left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y \]

   if 1.4e72 < c < 3.2000000000000001e134

   1. Initial program 27.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 64.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(a \cdot y1\right) + c \cdot y0\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 29.4%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(-1, a \cdot y1, c \cdot y0\right)\right) \cdot y2 \]

   9. Taylor expanded in y1 around inf

      \[\leadsto y1 \cdot \color{blue}{\left(y2 \cdot \left(-1 \cdot \left(a \cdot x\right) + k \cdot y4\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 80.7%

       \[\leadsto \left(y1 \cdot y2\right) \cdot \color{blue}{\mathsf{fma}\left(-a, x, k \cdot y4\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 43.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;c \leq -2.2 \cdot 10^{+85}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \mathbf{elif}\;c \leq 1.4 \cdot 10^{+72}:\\ \;\;\;\;\left(a \cdot \mathsf{fma}\left(b, x, \left(-y3\right) \cdot y5\right)\right) \cdot y\\ \mathbf{elif}\;c \leq 3.2 \cdot 10^{+134}:\\ \;\;\;\;\left(y1 \cdot y2\right) \cdot \mathsf{fma}\left(-a, x, k \cdot y4\right)\\ \mathbf{else}:\\ \;\;\;\;\left(c \cdot x\right) \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 15: 29.0% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y5 \leq -1.4 \cdot 10^{+170}:\\ \;\;\;\;\left(y \cdot y5\right) \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -2.25 \cdot 10^{+72}:\\ \;\;\;\;\left(a \cdot y5\right) \cdot \mathsf{fma}\left(t, y2, \left(-y\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq 2.9 \cdot 10^{+97}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y5 -1.4e+170)
   (* (* y y5) (fma i k (* (- a) y3)))
   (if (<= y5 -2.25e+72)
     (* (* a y5) (fma t y2 (* (- y) y3)))
     (if (<= y5 2.9e+97)
       (* (* y1 y3) (fma a z (* (- j) y4)))
       (* (* (* y5 y) i) k)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (y5 <= -1.4e+170) {
		tmp = (y * y5) * fma(i, k, (-a * y3));
	} else if (y5 <= -2.25e+72) {
		tmp = (a * y5) * fma(t, y2, (-y * y3));
	} else if (y5 <= 2.9e+97) {
		tmp = (y1 * y3) * fma(a, z, (-j * y4));
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (y5 <= -1.4e+170)
		tmp = Float64(Float64(y * y5) * fma(i, k, Float64(Float64(-a) * y3)));
	elseif (y5 <= -2.25e+72)
		tmp = Float64(Float64(a * y5) * fma(t, y2, Float64(Float64(-y) * y3)));
	elseif (y5 <= 2.9e+97)
		tmp = Float64(Float64(y1 * y3) * fma(a, z, Float64(Float64(-j) * y4)));
	else
		tmp = Float64(Float64(Float64(y5 * y) * i) * k);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[y5, -1.4e+170], N[(N[(y * y5), $MachinePrecision] * N[(i * k + N[((-a) * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, -2.25e+72], N[(N[(a * y5), $MachinePrecision] * N[(t * y2 + N[((-y) * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 2.9e+97], N[(N[(y1 * y3), $MachinePrecision] * N[(a * z + N[((-j) * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if y5 < -1.40000000000000008e170

   1. Initial program 21.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 56.4%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 69.3%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   if -1.40000000000000008e170 < y5 < -2.2499999999999999e72

   1. Initial program 39.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 62.1%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 27.8%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around inf

      \[\leadsto a \cdot \color{blue}{\left(y5 \cdot \left(-1 \cdot \left(y \cdot y3\right) + t \cdot y2\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 48.8%

       \[\leadsto \left(a \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(t, y2, -y \cdot y3\right)} \]

   if -2.2499999999999999e72 < y5 < 2.89999999999999987e97

   1. Initial program 34.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 44.6%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(y3 \cdot \left(-1 \cdot \left(j \cdot y4\right) + a \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 30.9%

      \[\leadsto \left(y1 \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(a, z, -j \cdot y4\right)} \]

   if 2.89999999999999987e97 < y5

   1. Initial program 27.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 64.5%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.2%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 39.7%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 50.0%

       \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
3. Recombined 4 regimes into one program.

4. Final simplification 40.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y5 \leq -1.4 \cdot 10^{+170}:\\ \;\;\;\;\left(y \cdot y5\right) \cdot \mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq -2.25 \cdot 10^{+72}:\\ \;\;\;\;\left(a \cdot y5\right) \cdot \mathsf{fma}\left(t, y2, \left(-y\right) \cdot y3\right)\\ \mathbf{elif}\;y5 \leq 2.9 \cdot 10^{+97}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 16: 27.2% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y5 \leq -2.05 \cdot 10^{+240}:\\ \;\;\;\;\left(-a\right) \cdot \left(\left(y \cdot y3\right) \cdot y5\right)\\ \mathbf{elif}\;y5 \leq -1.35 \cdot 10^{+58}:\\ \;\;\;\;\left(\left(-i\right) \cdot \left(t \cdot y5\right)\right) \cdot j\\ \mathbf{elif}\;y5 \leq 2.9 \cdot 10^{+97}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y5 -2.05e+240)
   (* (- a) (* (* y y3) y5))
   (if (<= y5 -1.35e+58)
     (* (* (- i) (* t y5)) j)
     (if (<= y5 2.9e+97)
       (* (* y1 y3) (fma a z (* (- j) y4)))
       (* (* (* y5 y) i) k)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (y5 <= -2.05e+240) {
		tmp = -a * ((y * y3) * y5);
	} else if (y5 <= -1.35e+58) {
		tmp = (-i * (t * y5)) * j;
	} else if (y5 <= 2.9e+97) {
		tmp = (y1 * y3) * fma(a, z, (-j * y4));
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (y5 <= -2.05e+240)
		tmp = Float64(Float64(-a) * Float64(Float64(y * y3) * y5));
	elseif (y5 <= -1.35e+58)
		tmp = Float64(Float64(Float64(-i) * Float64(t * y5)) * j);
	elseif (y5 <= 2.9e+97)
		tmp = Float64(Float64(y1 * y3) * fma(a, z, Float64(Float64(-j) * y4)));
	else
		tmp = Float64(Float64(Float64(y5 * y) * i) * k);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[y5, -2.05e+240], N[((-a) * N[(N[(y * y3), $MachinePrecision] * y5), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, -1.35e+58], N[(N[((-i) * N[(t * y5), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision], If[LessEqual[y5, 2.9e+97], N[(N[(y1 * y3), $MachinePrecision] * N[(a * z + N[((-j) * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if y5 < -2.05000000000000015e240

   1. Initial program 11.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 77.8%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 89.4%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around inf

      \[\leadsto -1 \cdot \left(a \cdot \color{blue}{\left(y \cdot \left(y3 \cdot y5\right)\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 83.6%

       \[\leadsto \left(-a\right) \cdot \left(\left(y \cdot y3\right) \cdot \color{blue}{y5}\right) \]

   if -2.05000000000000015e240 < y5 < -1.3500000000000001e58

   1. Initial program 35.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in j around inf

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

   5. Applied rewrites 40.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y3, \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot x\right) \cdot j} \]

   6. Taylor expanded in y0 around inf

      \[\leadsto \left(y0 \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot j \]
   7. Step-by-step derivation

   8. Applied rewrites 31.3%

      \[\leadsto \left(y0 \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot j \]

   9. Taylor expanded in i around -inf

      \[\leadsto \left(-1 \cdot \left(i \cdot \left(t \cdot y5 - x \cdot y1\right)\right)\right) \cdot j \]
   10. Step-by-step derivation

   11. Applied rewrites 38.7%

       \[\leadsto \left(\left(-i\right) \cdot \mathsf{fma}\left(t, y5, \left(-x\right) \cdot y1\right)\right) \cdot j \]

   12. Taylor expanded in x around 0

       \[\leadsto \left(-1 \cdot \left(i \cdot \left(t \cdot y5\right)\right)\right) \cdot j \]
   13. Step-by-step derivation

   14. Applied rewrites 36.1%

       \[\leadsto \left(-i \cdot \left(t \cdot y5\right)\right) \cdot j \]

   if -1.3500000000000001e58 < y5 < 2.89999999999999987e97

   1. Initial program 35.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 44.2%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(y3 \cdot \left(-1 \cdot \left(j \cdot y4\right) + a \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 30.9%

      \[\leadsto \left(y1 \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(a, z, -j \cdot y4\right)} \]

   if 2.89999999999999987e97 < y5

   1. Initial program 27.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 64.5%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.2%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 39.7%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 50.0%

       \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
3. Recombined 4 regimes into one program.

4. Final simplification 38.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y5 \leq -2.05 \cdot 10^{+240}:\\ \;\;\;\;\left(-a\right) \cdot \left(\left(y \cdot y3\right) \cdot y5\right)\\ \mathbf{elif}\;y5 \leq -1.35 \cdot 10^{+58}:\\ \;\;\;\;\left(\left(-i\right) \cdot \left(t \cdot y5\right)\right) \cdot j\\ \mathbf{elif}\;y5 \leq 2.9 \cdot 10^{+97}:\\ \;\;\;\;\left(y1 \cdot y3\right) \cdot \mathsf{fma}\left(a, z, \left(-j\right) \cdot y4\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 17: 25.1% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.5 \cdot 10^{-21}:\\ \;\;\;\;\left(y \cdot y3\right) \cdot \mathsf{fma}\left(-a, y5, c \cdot y4\right)\\ \mathbf{elif}\;a \leq -1.45 \cdot 10^{-100}:\\ \;\;\;\;\left(i \cdot \left(x \cdot y1\right)\right) \cdot j\\ \mathbf{elif}\;a \leq 1.2 \cdot 10^{-70}:\\ \;\;\;\;i \cdot \left(\left(y5 \cdot k\right) \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= a -1.5e-21)
   (* (* y y3) (fma (- a) y5 (* c y4)))
   (if (<= a -1.45e-100)
     (* (* i (* x y1)) j)
     (if (<= a 1.2e-70)
       (* i (* (* y5 k) y))
       (* (* a y3) (fma y1 z (* (- y) y5)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (a <= -1.5e-21) {
		tmp = (y * y3) * fma(-a, y5, (c * y4));
	} else if (a <= -1.45e-100) {
		tmp = (i * (x * y1)) * j;
	} else if (a <= 1.2e-70) {
		tmp = i * ((y5 * k) * y);
	} else {
		tmp = (a * y3) * fma(y1, z, (-y * y5));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (a <= -1.5e-21)
		tmp = Float64(Float64(y * y3) * fma(Float64(-a), y5, Float64(c * y4)));
	elseif (a <= -1.45e-100)
		tmp = Float64(Float64(i * Float64(x * y1)) * j);
	elseif (a <= 1.2e-70)
		tmp = Float64(i * Float64(Float64(y5 * k) * y));
	else
		tmp = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[a, -1.5e-21], N[(N[(y * y3), $MachinePrecision] * N[((-a) * y5 + N[(c * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -1.45e-100], N[(N[(i * N[(x * y1), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision], If[LessEqual[a, 1.2e-70], N[(i * N[(N[(y5 * k), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if a < -1.49999999999999996e-21

   1. Initial program 24.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 42.2%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y around inf

      \[\leadsto y \cdot \color{blue}{\left(y3 \cdot \left(-1 \cdot \left(a \cdot y5\right) + c \cdot y4\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 42.8%

      \[\leadsto \left(y \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(-a, y5, c \cdot y4\right)} \]

   if -1.49999999999999996e-21 < a < -1.44999999999999988e-100

   1. Initial program 27.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in j around inf

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

   5. Applied rewrites 41.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y3, \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot x\right) \cdot j} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto \left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right) \cdot j \]
   7. Step-by-step derivation

   8. Applied rewrites 33.0%

      \[\leadsto \left(-y1 \cdot \mathsf{fma}\left(y3, y4, \left(-i\right) \cdot x\right)\right) \cdot j \]

   9. Taylor expanded in x around inf

      \[\leadsto \left(i \cdot \left(x \cdot y1\right)\right) \cdot j \]
   10. Step-by-step derivation

   11. Applied rewrites 41.8%

       \[\leadsto \left(i \cdot \left(x \cdot y1\right)\right) \cdot j \]

   if -1.44999999999999988e-100 < a < 1.2000000000000001e-70

   1. Initial program 40.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 33.3%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 26.6%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 26.3%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 28.2%

       \[\leadsto i \cdot \left(\left(y5 \cdot k\right) \cdot y\right) \]

   if 1.2000000000000001e-70 < a

   1. Initial program 29.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 45.3%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.1%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 18: 26.7% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \mathbf{if}\;a \leq -3 \cdot 10^{-48}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq -1.45 \cdot 10^{-100}:\\ \;\;\;\;\left(i \cdot \left(x \cdot y1\right)\right) \cdot j\\ \mathbf{elif}\;a \leq 1.2 \cdot 10^{-70}:\\ \;\;\;\;i \cdot \left(\left(y5 \cdot k\right) \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* a y3) (fma y1 z (* (- y) y5)))))
   (if (<= a -3e-48)
     t_1
     (if (<= a -1.45e-100)
       (* (* i (* x y1)) j)
       (if (<= a 1.2e-70) (* i (* (* y5 k) y)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (a * y3) * fma(y1, z, (-y * y5));
	double tmp;
	if (a <= -3e-48) {
		tmp = t_1;
	} else if (a <= -1.45e-100) {
		tmp = (i * (x * y1)) * j;
	} else if (a <= 1.2e-70) {
		tmp = i * ((y5 * k) * y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)))
	tmp = 0.0
	if (a <= -3e-48)
		tmp = t_1;
	elseif (a <= -1.45e-100)
		tmp = Float64(Float64(i * Float64(x * y1)) * j);
	elseif (a <= 1.2e-70)
		tmp = Float64(i * Float64(Float64(y5 * k) * y));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -3e-48], t$95$1, If[LessEqual[a, -1.45e-100], N[(N[(i * N[(x * y1), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision], If[LessEqual[a, 1.2e-70], N[(i * N[(N[(y5 * k), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if a < -2.9999999999999999e-48 or 1.2000000000000001e-70 < a

   1. Initial program 27.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 44.1%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 42.5%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]

   if -2.9999999999999999e-48 < a < -1.44999999999999988e-100

   1. Initial program 25.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in j around inf

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

   5. Applied rewrites 38.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y3, \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot x\right) \cdot j} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto \left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right) \cdot j \]
   7. Step-by-step derivation

   8. Applied rewrites 32.8%

      \[\leadsto \left(-y1 \cdot \mathsf{fma}\left(y3, y4, \left(-i\right) \cdot x\right)\right) \cdot j \]

   9. Taylor expanded in x around inf

      \[\leadsto \left(i \cdot \left(x \cdot y1\right)\right) \cdot j \]
   10. Step-by-step derivation

   11. Applied rewrites 38.6%

       \[\leadsto \left(i \cdot \left(x \cdot y1\right)\right) \cdot j \]

   if -1.44999999999999988e-100 < a < 1.2000000000000001e-70

   1. Initial program 40.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 33.3%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 26.6%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 26.3%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 28.2%

       \[\leadsto i \cdot \left(\left(y5 \cdot k\right) \cdot y\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 19: 22.7% accurate, 5.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.35 \cdot 10^{+167}:\\ \;\;\;\;\left(i \cdot \left(x \cdot y1\right)\right) \cdot j\\ \mathbf{elif}\;x \leq -9.6 \cdot 10^{+20} \lor \neg \left(x \leq 6.8 \cdot 10^{+39}\right):\\ \;\;\;\;\left(c \cdot \left(x \cdot y0\right)\right) \cdot y2\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= x -2.35e+167)
   (* (* i (* x y1)) j)
   (if (or (<= x -9.6e+20) (not (<= x 6.8e+39)))
     (* (* c (* x y0)) y2)
     (* (* (* y5 y) i) k))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (x <= -2.35e+167) {
		tmp = (i * (x * y1)) * j;
	} else if ((x <= -9.6e+20) || !(x <= 6.8e+39)) {
		tmp = (c * (x * y0)) * y2;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: tmp
    if (x <= (-2.35d+167)) then
        tmp = (i * (x * y1)) * j
    else if ((x <= (-9.6d+20)) .or. (.not. (x <= 6.8d+39))) then
        tmp = (c * (x * y0)) * y2
    else
        tmp = ((y5 * y) * i) * k
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (x <= -2.35e+167) {
		tmp = (i * (x * y1)) * j;
	} else if ((x <= -9.6e+20) || !(x <= 6.8e+39)) {
		tmp = (c * (x * y0)) * y2;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if x <= -2.35e+167:
		tmp = (i * (x * y1)) * j
	elif (x <= -9.6e+20) or not (x <= 6.8e+39):
		tmp = (c * (x * y0)) * y2
	else:
		tmp = ((y5 * y) * i) * k
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (x <= -2.35e+167)
		tmp = Float64(Float64(i * Float64(x * y1)) * j);
	elseif ((x <= -9.6e+20) || !(x <= 6.8e+39))
		tmp = Float64(Float64(c * Float64(x * y0)) * y2);
	else
		tmp = Float64(Float64(Float64(y5 * y) * i) * k);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0;
	if (x <= -2.35e+167)
		tmp = (i * (x * y1)) * j;
	elseif ((x <= -9.6e+20) || ~((x <= 6.8e+39)))
		tmp = (c * (x * y0)) * y2;
	else
		tmp = ((y5 * y) * i) * k;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[x, -2.35e+167], N[(N[(i * N[(x * y1), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision], If[Or[LessEqual[x, -9.6e+20], N[Not[LessEqual[x, 6.8e+39]], $MachinePrecision]], N[(N[(c * N[(x * y0), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision], N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -2.35000000000000006e167

   1. Initial program 31.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in j around inf

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

   5. Applied rewrites 46.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y3, \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot x\right) \cdot j} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto \left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right) \cdot j \]
   7. Step-by-step derivation

   8. Applied rewrites 48.2%

      \[\leadsto \left(-y1 \cdot \mathsf{fma}\left(y3, y4, \left(-i\right) \cdot x\right)\right) \cdot j \]

   9. Taylor expanded in x around inf

      \[\leadsto \left(i \cdot \left(x \cdot y1\right)\right) \cdot j \]
   10. Step-by-step derivation

   11. Applied rewrites 40.7%

       \[\leadsto \left(i \cdot \left(x \cdot y1\right)\right) \cdot j \]

   if -2.35000000000000006e167 < x < -9.6e20 or 6.7999999999999998e39 < x

   1. Initial program 28.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 43.7%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(a \cdot y1\right) + c \cdot y0\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 39.5%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(-1, a \cdot y1, c \cdot y0\right)\right) \cdot y2 \]

   9. Taylor expanded in a around 0

      \[\leadsto \left(c \cdot \left(x \cdot y0\right)\right) \cdot y2 \]
   10. Step-by-step derivation

   11. Applied rewrites 35.7%

       \[\leadsto \left(c \cdot \left(x \cdot y0\right)\right) \cdot y2 \]

   if -9.6e20 < x < 6.7999999999999998e39

   1. Initial program 34.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 37.6%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 32.9%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 24.8%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 29.0%

       \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
3. Recombined 3 regimes into one program.

4. Final simplification 32.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.35 \cdot 10^{+167}:\\ \;\;\;\;\left(i \cdot \left(x \cdot y1\right)\right) \cdot j\\ \mathbf{elif}\;x \leq -9.6 \cdot 10^{+20} \lor \neg \left(x \leq 6.8 \cdot 10^{+39}\right):\\ \;\;\;\;\left(c \cdot \left(x \cdot y0\right)\right) \cdot y2\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 20: 22.4% accurate, 7.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -8.2 \cdot 10^{+130} \lor \neg \left(x \leq 2.9 \cdot 10^{+52}\right):\\ \;\;\;\;\left(i \cdot \left(x \cdot y1\right)\right) \cdot j\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (or (<= x -8.2e+130) (not (<= x 2.9e+52)))
   (* (* i (* x y1)) j)
   (* (* (* y5 y) i) k)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if ((x <= -8.2e+130) || !(x <= 2.9e+52)) {
		tmp = (i * (x * y1)) * j;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: tmp
    if ((x <= (-8.2d+130)) .or. (.not. (x <= 2.9d+52))) then
        tmp = (i * (x * y1)) * j
    else
        tmp = ((y5 * y) * i) * k
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if ((x <= -8.2e+130) || !(x <= 2.9e+52)) {
		tmp = (i * (x * y1)) * j;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if (x <= -8.2e+130) or not (x <= 2.9e+52):
		tmp = (i * (x * y1)) * j
	else:
		tmp = ((y5 * y) * i) * k
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if ((x <= -8.2e+130) || !(x <= 2.9e+52))
		tmp = Float64(Float64(i * Float64(x * y1)) * j);
	else
		tmp = Float64(Float64(Float64(y5 * y) * i) * k);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0;
	if ((x <= -8.2e+130) || ~((x <= 2.9e+52)))
		tmp = (i * (x * y1)) * j;
	else
		tmp = ((y5 * y) * i) * k;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[Or[LessEqual[x, -8.2e+130], N[Not[LessEqual[x, 2.9e+52]], $MachinePrecision]], N[(N[(i * N[(x * y1), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision], N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -8.19999999999999955e130 or 2.9e52 < x

   1. Initial program 28.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in j around inf

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot j} \]

   5. Applied rewrites 39.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y3, \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot x\right) \cdot j} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto \left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right) \cdot j \]
   7. Step-by-step derivation

   8. Applied rewrites 41.1%

      \[\leadsto \left(-y1 \cdot \mathsf{fma}\left(y3, y4, \left(-i\right) \cdot x\right)\right) \cdot j \]

   9. Taylor expanded in x around inf

      \[\leadsto \left(i \cdot \left(x \cdot y1\right)\right) \cdot j \]
   10. Step-by-step derivation

   11. Applied rewrites 38.0%

       \[\leadsto \left(i \cdot \left(x \cdot y1\right)\right) \cdot j \]

   if -8.19999999999999955e130 < x < 2.9e52

   1. Initial program 34.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 33.1%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 24.5%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 27.5%

       \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
3. Recombined 2 regimes into one program.

4. Final simplification 31.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -8.2 \cdot 10^{+130} \lor \neg \left(x \leq 2.9 \cdot 10^{+52}\right):\\ \;\;\;\;\left(i \cdot \left(x \cdot y1\right)\right) \cdot j\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 21: 21.2% accurate, 7.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y5 \leq -6 \cdot 10^{+223}:\\ \;\;\;\;\left(-a\right) \cdot \left(\left(y \cdot y3\right) \cdot y5\right)\\ \mathbf{elif}\;y5 \leq 3.25 \cdot 10^{-83}:\\ \;\;\;\;\left(x \cdot \left(c \cdot y0\right)\right) \cdot y2\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y5 -6e+223)
   (* (- a) (* (* y y3) y5))
   (if (<= y5 3.25e-83) (* (* x (* c y0)) y2) (* (* (* y5 y) i) k))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (y5 <= -6e+223) {
		tmp = -a * ((y * y3) * y5);
	} else if (y5 <= 3.25e-83) {
		tmp = (x * (c * y0)) * y2;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: tmp
    if (y5 <= (-6d+223)) then
        tmp = -a * ((y * y3) * y5)
    else if (y5 <= 3.25d-83) then
        tmp = (x * (c * y0)) * y2
    else
        tmp = ((y5 * y) * i) * k
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (y5 <= -6e+223) {
		tmp = -a * ((y * y3) * y5);
	} else if (y5 <= 3.25e-83) {
		tmp = (x * (c * y0)) * y2;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y5 <= -6e+223:
		tmp = -a * ((y * y3) * y5)
	elif y5 <= 3.25e-83:
		tmp = (x * (c * y0)) * y2
	else:
		tmp = ((y5 * y) * i) * k
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (y5 <= -6e+223)
		tmp = Float64(Float64(-a) * Float64(Float64(y * y3) * y5));
	elseif (y5 <= 3.25e-83)
		tmp = Float64(Float64(x * Float64(c * y0)) * y2);
	else
		tmp = Float64(Float64(Float64(y5 * y) * i) * k);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0;
	if (y5 <= -6e+223)
		tmp = -a * ((y * y3) * y5);
	elseif (y5 <= 3.25e-83)
		tmp = (x * (c * y0)) * y2;
	else
		tmp = ((y5 * y) * i) * k;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[y5, -6e+223], N[((-a) * N[(N[(y * y3), $MachinePrecision] * y5), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 3.25e-83], N[(N[(x * N[(c * y0), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision], N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y5 < -6.00000000000000002e223

   1. Initial program 17.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 65.2%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 78.8%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around inf

      \[\leadsto -1 \cdot \left(a \cdot \color{blue}{\left(y \cdot \left(y3 \cdot y5\right)\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 70.1%

       \[\leadsto \left(-a\right) \cdot \left(\left(y \cdot y3\right) \cdot \color{blue}{y5}\right) \]

   if -6.00000000000000002e223 < y5 < 3.25e-83

   1. Initial program 35.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 41.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(a \cdot y1\right) + c \cdot y0\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 32.7%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(-1, a \cdot y1, c \cdot y0\right)\right) \cdot y2 \]

   9. Taylor expanded in a around 0

      \[\leadsto \left(x \cdot \left(c \cdot y0\right)\right) \cdot y2 \]
   10. Step-by-step derivation

   11. Applied rewrites 23.6%

       \[\leadsto \left(x \cdot \left(c \cdot y0\right)\right) \cdot y2 \]

   if 3.25e-83 < y5

   1. Initial program 31.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 49.4%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 36.0%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 26.4%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 34.7%

       \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 22: 22.1% accurate, 7.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y5 \leq -8.5 \cdot 10^{+108}:\\ \;\;\;\;i \cdot \left(\left(y5 \cdot k\right) \cdot y\right)\\ \mathbf{elif}\;y5 \leq 3.25 \cdot 10^{-83}:\\ \;\;\;\;\left(x \cdot \left(c \cdot y0\right)\right) \cdot y2\\ \mathbf{else}:\\ \;\;\;\;\left(\left(y5 \cdot y\right) \cdot i\right) \cdot k\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y5 -8.5e+108)
   (* i (* (* y5 k) y))
   (if (<= y5 3.25e-83) (* (* x (* c y0)) y2) (* (* (* y5 y) i) k))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (y5 <= -8.5e+108) {
		tmp = i * ((y5 * k) * y);
	} else if (y5 <= 3.25e-83) {
		tmp = (x * (c * y0)) * y2;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: tmp
    if (y5 <= (-8.5d+108)) then
        tmp = i * ((y5 * k) * y)
    else if (y5 <= 3.25d-83) then
        tmp = (x * (c * y0)) * y2
    else
        tmp = ((y5 * y) * i) * k
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (y5 <= -8.5e+108) {
		tmp = i * ((y5 * k) * y);
	} else if (y5 <= 3.25e-83) {
		tmp = (x * (c * y0)) * y2;
	} else {
		tmp = ((y5 * y) * i) * k;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y5 <= -8.5e+108:
		tmp = i * ((y5 * k) * y)
	elif y5 <= 3.25e-83:
		tmp = (x * (c * y0)) * y2
	else:
		tmp = ((y5 * y) * i) * k
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (y5 <= -8.5e+108)
		tmp = Float64(i * Float64(Float64(y5 * k) * y));
	elseif (y5 <= 3.25e-83)
		tmp = Float64(Float64(x * Float64(c * y0)) * y2);
	else
		tmp = Float64(Float64(Float64(y5 * y) * i) * k);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0;
	if (y5 <= -8.5e+108)
		tmp = i * ((y5 * k) * y);
	elseif (y5 <= 3.25e-83)
		tmp = (x * (c * y0)) * y2;
	else
		tmp = ((y5 * y) * i) * k;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[y5, -8.5e+108], N[(i * N[(N[(y5 * k), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 3.25e-83], N[(N[(x * N[(c * y0), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision], N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y5 < -8.50000000000000016e108

   1. Initial program 29.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 57.3%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 55.5%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 37.4%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 46.3%

       \[\leadsto i \cdot \left(\left(y5 \cdot k\right) \cdot y\right) \]

   if -8.50000000000000016e108 < y5 < 3.25e-83

   1. Initial program 34.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 41.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(a \cdot y1\right) + c \cdot y0\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 33.2%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(-1, a \cdot y1, c \cdot y0\right)\right) \cdot y2 \]

   9. Taylor expanded in a around 0

      \[\leadsto \left(x \cdot \left(c \cdot y0\right)\right) \cdot y2 \]
   10. Step-by-step derivation

   11. Applied rewrites 23.2%

       \[\leadsto \left(x \cdot \left(c \cdot y0\right)\right) \cdot y2 \]

   if 3.25e-83 < y5

   1. Initial program 31.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y5 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Applied rewrites 49.4%

      \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 36.0%

      \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

   9. Taylor expanded in a around 0

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 26.4%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 34.7%

       \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 23: 17.0% accurate, 12.6× speedup

Math

\[\begin{array}{l} \\ \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (* (* (* y5 y) i) k))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return ((y5 * y) * i) * k;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    code = ((y5 * y) * i) * k
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return ((y5 * y) * i) * k;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	return ((y5 * y) * i) * k

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	return Float64(Float64(Float64(y5 * y) * i) * k)
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = ((y5 * y) * i) * k;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(N[(N[(y5 * y), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision]

Derivation

1. Initial program 32.2%

   \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
2. Add Preprocessing

3. Taylor expanded in y5 around -inf

   \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

   2. distribute-lft-neg-in N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   3. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   4. lower-neg.f64 N/A

      \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

   5. lower--.f64 N/A

      \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

5. Applied rewrites 38.4%

   \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

6. Taylor expanded in y around -inf

   \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
7. Step-by-step derivation

8. Applied rewrites 29.1%

   \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

9. Taylor expanded in a around 0

   \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
10. Step-by-step derivation

11. Applied rewrites 19.8%

    \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
12. Step-by-step derivation

13. Applied rewrites 22.5%

    \[\leadsto \left(\left(y5 \cdot y\right) \cdot i\right) \cdot k \]
14. Add Preprocessing

Alternative 24: 16.7% accurate, 12.6× speedup

Math

\[\begin{array}{l} \\ i \cdot \left(\left(y5 \cdot k\right) \cdot y\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (* i (* (* y5 k) y)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return i * ((y5 * k) * y);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    code = i * ((y5 * k) * y)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return i * ((y5 * k) * y);
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	return i * ((y5 * k) * y)

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	return Float64(i * Float64(Float64(y5 * k) * y))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = i * ((y5 * k) * y);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(i * N[(N[(y5 * k), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 32.2%

   \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
2. Add Preprocessing

3. Taylor expanded in y5 around -inf

   \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

   2. distribute-lft-neg-in N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   3. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   4. lower-neg.f64 N/A

      \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

   5. lower--.f64 N/A

      \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

5. Applied rewrites 38.4%

   \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

6. Taylor expanded in y around -inf

   \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
7. Step-by-step derivation

8. Applied rewrites 29.1%

   \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

9. Taylor expanded in a around 0

   \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
10. Step-by-step derivation

11. Applied rewrites 19.8%

    \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
12. Step-by-step derivation

13. Applied rewrites 21.0%

    \[\leadsto i \cdot \left(\left(y5 \cdot k\right) \cdot y\right) \]
14. Add Preprocessing

Alternative 25: 16.8% accurate, 12.6× speedup

Math

\[\begin{array}{l} \\ i \cdot \left(\left(k \cdot y\right) \cdot y5\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (* i (* (* k y) y5)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return i * ((k * y) * y5);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    code = i * ((k * y) * y5)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return i * ((k * y) * y5);
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	return i * ((k * y) * y5)

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	return Float64(i * Float64(Float64(k * y) * y5))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = i * ((k * y) * y5);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(i * N[(N[(k * y), $MachinePrecision] * y5), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 32.2%

   \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
2. Add Preprocessing

3. Taylor expanded in y5 around -inf

   \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

   2. distribute-lft-neg-in N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   3. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y5\right)\right) \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   4. lower-neg.f64 N/A

      \[\leadsto \color{blue}{\left(-y5\right)} \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \]

   5. lower--.f64 N/A

      \[\leadsto \left(-y5\right) \cdot \color{blue}{\left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

5. Applied rewrites 38.4%

   \[\leadsto \color{blue}{\left(-y5\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y0, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot i\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot a\right)} \]

6. Taylor expanded in y around -inf

   \[\leadsto y \cdot \color{blue}{\left(y5 \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]
7. Step-by-step derivation

8. Applied rewrites 29.1%

   \[\leadsto \left(y \cdot y5\right) \cdot \color{blue}{\mathsf{fma}\left(i, k, \left(-a\right) \cdot y3\right)} \]

9. Taylor expanded in a around 0

   \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
10. Step-by-step derivation

11. Applied rewrites 19.8%

    \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
12. Add Preprocessing

Developer Target 1: 28.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y4 \cdot c - y5 \cdot a\\ t_2 := x \cdot y2 - z \cdot y3\\ t_3 := y2 \cdot t - y3 \cdot y\\ t_4 := k \cdot y2 - j \cdot y3\\ t_5 := y4 \cdot b - y5 \cdot i\\ t_6 := \left(j \cdot t - k \cdot y\right) \cdot t\_5\\ t_7 := b \cdot a - i \cdot c\\ t_8 := t\_7 \cdot \left(y \cdot x - t \cdot z\right)\\ t_9 := j \cdot x - k \cdot z\\ t_10 := \left(b \cdot y0 - i \cdot y1\right) \cdot t\_9\\ t_11 := t\_9 \cdot \left(y0 \cdot b - i \cdot y1\right)\\ t_12 := y4 \cdot y1 - y5 \cdot y0\\ t_13 := t\_4 \cdot t\_12\\ t_14 := \left(y2 \cdot k - y3 \cdot j\right) \cdot t\_12\\ t_15 := \left(\left(\left(\left(k \cdot y\right) \cdot \left(y5 \cdot i\right) - \left(y \cdot b\right) \cdot \left(y4 \cdot k\right)\right) - \left(y5 \cdot t\right) \cdot \left(i \cdot j\right)\right) - \left(t\_3 \cdot t\_1 - t\_14\right)\right) + \left(t\_8 - \left(t\_11 - \left(y2 \cdot x - y3 \cdot z\right) \cdot \left(c \cdot y0 - y1 \cdot a\right)\right)\right)\\ t_16 := \left(\left(t\_6 - \left(y3 \cdot y\right) \cdot \left(y5 \cdot a - y4 \cdot c\right)\right) + \left(\left(y5 \cdot a\right) \cdot \left(t \cdot y2\right) + t\_13\right)\right) + \left(t\_2 \cdot \left(c \cdot y0 - a \cdot y1\right) - \left(t\_10 - \left(y \cdot x - z \cdot t\right) \cdot t\_7\right)\right)\\ t_17 := t \cdot y2 - y \cdot y3\\ \mathbf{if}\;y4 < -7.206256231996481 \cdot 10^{+60}:\\ \;\;\;\;\left(t\_8 - \left(t\_11 - t\_6\right)\right) - \left(\frac{t\_3}{\frac{1}{t\_1}} - t\_14\right)\\ \mathbf{elif}\;y4 < -3.364603505246317 \cdot 10^{-66}:\\ \;\;\;\;\left(\left(\left(\left(t \cdot c\right) \cdot \left(i \cdot z\right) - \left(a \cdot t\right) \cdot \left(b \cdot z\right)\right) - \left(y \cdot c\right) \cdot \left(i \cdot x\right)\right) - t\_10\right) + \left(\left(y0 \cdot c - a \cdot y1\right) \cdot t\_2 - \left(t\_17 \cdot \left(y4 \cdot c - a \cdot y5\right) - \left(y1 \cdot y4 - y5 \cdot y0\right) \cdot t\_4\right)\right)\\ \mathbf{elif}\;y4 < -1.2000065055686116 \cdot 10^{-105}:\\ \;\;\;\;t\_16\\ \mathbf{elif}\;y4 < 6.718963124057495 \cdot 10^{-279}:\\ \;\;\;\;t\_15\\ \mathbf{elif}\;y4 < 4.77962681403792 \cdot 10^{-222}:\\ \;\;\;\;t\_16\\ \mathbf{elif}\;y4 < 2.2852241541266835 \cdot 10^{-175}:\\ \;\;\;\;t\_15\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(k \cdot \left(i \cdot \left(z \cdot y1\right)\right) - \left(j \cdot \left(i \cdot \left(x \cdot y1\right)\right) + y0 \cdot \left(k \cdot \left(z \cdot b\right)\right)\right)\right)\right) + \left(z \cdot \left(y3 \cdot \left(a \cdot y1\right)\right) - \left(y2 \cdot \left(x \cdot \left(a \cdot y1\right)\right) + y0 \cdot \left(z \cdot \left(c \cdot y3\right)\right)\right)\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot t\_5\right) - t\_17 \cdot t\_1\right) + t\_13\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (- (* y4 c) (* y5 a)))
        (t_2 (- (* x y2) (* z y3)))
        (t_3 (- (* y2 t) (* y3 y)))
        (t_4 (- (* k y2) (* j y3)))
        (t_5 (- (* y4 b) (* y5 i)))
        (t_6 (* (- (* j t) (* k y)) t_5))
        (t_7 (- (* b a) (* i c)))
        (t_8 (* t_7 (- (* y x) (* t z))))
        (t_9 (- (* j x) (* k z)))
        (t_10 (* (- (* b y0) (* i y1)) t_9))
        (t_11 (* t_9 (- (* y0 b) (* i y1))))
        (t_12 (- (* y4 y1) (* y5 y0)))
        (t_13 (* t_4 t_12))
        (t_14 (* (- (* y2 k) (* y3 j)) t_12))
        (t_15
         (+
          (-
           (-
            (- (* (* k y) (* y5 i)) (* (* y b) (* y4 k)))
            (* (* y5 t) (* i j)))
           (- (* t_3 t_1) t_14))
          (- t_8 (- t_11 (* (- (* y2 x) (* y3 z)) (- (* c y0) (* y1 a)))))))
        (t_16
         (+
          (+
           (- t_6 (* (* y3 y) (- (* y5 a) (* y4 c))))
           (+ (* (* y5 a) (* t y2)) t_13))
          (-
           (* t_2 (- (* c y0) (* a y1)))
           (- t_10 (* (- (* y x) (* z t)) t_7)))))
        (t_17 (- (* t y2) (* y y3))))
   (if (< y4 -7.206256231996481e+60)
     (- (- t_8 (- t_11 t_6)) (- (/ t_3 (/ 1.0 t_1)) t_14))
     (if (< y4 -3.364603505246317e-66)
       (+
        (-
         (- (- (* (* t c) (* i z)) (* (* a t) (* b z))) (* (* y c) (* i x)))
         t_10)
        (-
         (* (- (* y0 c) (* a y1)) t_2)
         (- (* t_17 (- (* y4 c) (* a y5))) (* (- (* y1 y4) (* y5 y0)) t_4))))
       (if (< y4 -1.2000065055686116e-105)
         t_16
         (if (< y4 6.718963124057495e-279)
           t_15
           (if (< y4 4.77962681403792e-222)
             t_16
             (if (< y4 2.2852241541266835e-175)
               t_15
               (+
                (-
                 (+
                  (+
                   (-
                    (* (- (* x y) (* z t)) (- (* a b) (* c i)))
                    (-
                     (* k (* i (* z y1)))
                     (+ (* j (* i (* x y1))) (* y0 (* k (* z b))))))
                   (-
                    (* z (* y3 (* a y1)))
                    (+ (* y2 (* x (* a y1))) (* y0 (* z (* c y3))))))
                  (* (- (* t j) (* y k)) t_5))
                 (* t_17 t_1))
                t_13)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y4 * c) - (y5 * a);
	double t_2 = (x * y2) - (z * y3);
	double t_3 = (y2 * t) - (y3 * y);
	double t_4 = (k * y2) - (j * y3);
	double t_5 = (y4 * b) - (y5 * i);
	double t_6 = ((j * t) - (k * y)) * t_5;
	double t_7 = (b * a) - (i * c);
	double t_8 = t_7 * ((y * x) - (t * z));
	double t_9 = (j * x) - (k * z);
	double t_10 = ((b * y0) - (i * y1)) * t_9;
	double t_11 = t_9 * ((y0 * b) - (i * y1));
	double t_12 = (y4 * y1) - (y5 * y0);
	double t_13 = t_4 * t_12;
	double t_14 = ((y2 * k) - (y3 * j)) * t_12;
	double t_15 = (((((k * y) * (y5 * i)) - ((y * b) * (y4 * k))) - ((y5 * t) * (i * j))) - ((t_3 * t_1) - t_14)) + (t_8 - (t_11 - (((y2 * x) - (y3 * z)) * ((c * y0) - (y1 * a)))));
	double t_16 = ((t_6 - ((y3 * y) * ((y5 * a) - (y4 * c)))) + (((y5 * a) * (t * y2)) + t_13)) + ((t_2 * ((c * y0) - (a * y1))) - (t_10 - (((y * x) - (z * t)) * t_7)));
	double t_17 = (t * y2) - (y * y3);
	double tmp;
	if (y4 < -7.206256231996481e+60) {
		tmp = (t_8 - (t_11 - t_6)) - ((t_3 / (1.0 / t_1)) - t_14);
	} else if (y4 < -3.364603505246317e-66) {
		tmp = (((((t * c) * (i * z)) - ((a * t) * (b * z))) - ((y * c) * (i * x))) - t_10) + ((((y0 * c) - (a * y1)) * t_2) - ((t_17 * ((y4 * c) - (a * y5))) - (((y1 * y4) - (y5 * y0)) * t_4)));
	} else if (y4 < -1.2000065055686116e-105) {
		tmp = t_16;
	} else if (y4 < 6.718963124057495e-279) {
		tmp = t_15;
	} else if (y4 < 4.77962681403792e-222) {
		tmp = t_16;
	} else if (y4 < 2.2852241541266835e-175) {
		tmp = t_15;
	} else {
		tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - ((k * (i * (z * y1))) - ((j * (i * (x * y1))) + (y0 * (k * (z * b)))))) + ((z * (y3 * (a * y1))) - ((y2 * (x * (a * y1))) + (y0 * (z * (c * y3)))))) + (((t * j) - (y * k)) * t_5)) - (t_17 * t_1)) + t_13;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: t_1
    real(8) :: t_10
    real(8) :: t_11
    real(8) :: t_12
    real(8) :: t_13
    real(8) :: t_14
    real(8) :: t_15
    real(8) :: t_16
    real(8) :: t_17
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: t_8
    real(8) :: t_9
    real(8) :: tmp
    t_1 = (y4 * c) - (y5 * a)
    t_2 = (x * y2) - (z * y3)
    t_3 = (y2 * t) - (y3 * y)
    t_4 = (k * y2) - (j * y3)
    t_5 = (y4 * b) - (y5 * i)
    t_6 = ((j * t) - (k * y)) * t_5
    t_7 = (b * a) - (i * c)
    t_8 = t_7 * ((y * x) - (t * z))
    t_9 = (j * x) - (k * z)
    t_10 = ((b * y0) - (i * y1)) * t_9
    t_11 = t_9 * ((y0 * b) - (i * y1))
    t_12 = (y4 * y1) - (y5 * y0)
    t_13 = t_4 * t_12
    t_14 = ((y2 * k) - (y3 * j)) * t_12
    t_15 = (((((k * y) * (y5 * i)) - ((y * b) * (y4 * k))) - ((y5 * t) * (i * j))) - ((t_3 * t_1) - t_14)) + (t_8 - (t_11 - (((y2 * x) - (y3 * z)) * ((c * y0) - (y1 * a)))))
    t_16 = ((t_6 - ((y3 * y) * ((y5 * a) - (y4 * c)))) + (((y5 * a) * (t * y2)) + t_13)) + ((t_2 * ((c * y0) - (a * y1))) - (t_10 - (((y * x) - (z * t)) * t_7)))
    t_17 = (t * y2) - (y * y3)
    if (y4 < (-7.206256231996481d+60)) then
        tmp = (t_8 - (t_11 - t_6)) - ((t_3 / (1.0d0 / t_1)) - t_14)
    else if (y4 < (-3.364603505246317d-66)) then
        tmp = (((((t * c) * (i * z)) - ((a * t) * (b * z))) - ((y * c) * (i * x))) - t_10) + ((((y0 * c) - (a * y1)) * t_2) - ((t_17 * ((y4 * c) - (a * y5))) - (((y1 * y4) - (y5 * y0)) * t_4)))
    else if (y4 < (-1.2000065055686116d-105)) then
        tmp = t_16
    else if (y4 < 6.718963124057495d-279) then
        tmp = t_15
    else if (y4 < 4.77962681403792d-222) then
        tmp = t_16
    else if (y4 < 2.2852241541266835d-175) then
        tmp = t_15
    else
        tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - ((k * (i * (z * y1))) - ((j * (i * (x * y1))) + (y0 * (k * (z * b)))))) + ((z * (y3 * (a * y1))) - ((y2 * (x * (a * y1))) + (y0 * (z * (c * y3)))))) + (((t * j) - (y * k)) * t_5)) - (t_17 * t_1)) + t_13
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y4 * c) - (y5 * a);
	double t_2 = (x * y2) - (z * y3);
	double t_3 = (y2 * t) - (y3 * y);
	double t_4 = (k * y2) - (j * y3);
	double t_5 = (y4 * b) - (y5 * i);
	double t_6 = ((j * t) - (k * y)) * t_5;
	double t_7 = (b * a) - (i * c);
	double t_8 = t_7 * ((y * x) - (t * z));
	double t_9 = (j * x) - (k * z);
	double t_10 = ((b * y0) - (i * y1)) * t_9;
	double t_11 = t_9 * ((y0 * b) - (i * y1));
	double t_12 = (y4 * y1) - (y5 * y0);
	double t_13 = t_4 * t_12;
	double t_14 = ((y2 * k) - (y3 * j)) * t_12;
	double t_15 = (((((k * y) * (y5 * i)) - ((y * b) * (y4 * k))) - ((y5 * t) * (i * j))) - ((t_3 * t_1) - t_14)) + (t_8 - (t_11 - (((y2 * x) - (y3 * z)) * ((c * y0) - (y1 * a)))));
	double t_16 = ((t_6 - ((y3 * y) * ((y5 * a) - (y4 * c)))) + (((y5 * a) * (t * y2)) + t_13)) + ((t_2 * ((c * y0) - (a * y1))) - (t_10 - (((y * x) - (z * t)) * t_7)));
	double t_17 = (t * y2) - (y * y3);
	double tmp;
	if (y4 < -7.206256231996481e+60) {
		tmp = (t_8 - (t_11 - t_6)) - ((t_3 / (1.0 / t_1)) - t_14);
	} else if (y4 < -3.364603505246317e-66) {
		tmp = (((((t * c) * (i * z)) - ((a * t) * (b * z))) - ((y * c) * (i * x))) - t_10) + ((((y0 * c) - (a * y1)) * t_2) - ((t_17 * ((y4 * c) - (a * y5))) - (((y1 * y4) - (y5 * y0)) * t_4)));
	} else if (y4 < -1.2000065055686116e-105) {
		tmp = t_16;
	} else if (y4 < 6.718963124057495e-279) {
		tmp = t_15;
	} else if (y4 < 4.77962681403792e-222) {
		tmp = t_16;
	} else if (y4 < 2.2852241541266835e-175) {
		tmp = t_15;
	} else {
		tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - ((k * (i * (z * y1))) - ((j * (i * (x * y1))) + (y0 * (k * (z * b)))))) + ((z * (y3 * (a * y1))) - ((y2 * (x * (a * y1))) + (y0 * (z * (c * y3)))))) + (((t * j) - (y * k)) * t_5)) - (t_17 * t_1)) + t_13;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = (y4 * c) - (y5 * a)
	t_2 = (x * y2) - (z * y3)
	t_3 = (y2 * t) - (y3 * y)
	t_4 = (k * y2) - (j * y3)
	t_5 = (y4 * b) - (y5 * i)
	t_6 = ((j * t) - (k * y)) * t_5
	t_7 = (b * a) - (i * c)
	t_8 = t_7 * ((y * x) - (t * z))
	t_9 = (j * x) - (k * z)
	t_10 = ((b * y0) - (i * y1)) * t_9
	t_11 = t_9 * ((y0 * b) - (i * y1))
	t_12 = (y4 * y1) - (y5 * y0)
	t_13 = t_4 * t_12
	t_14 = ((y2 * k) - (y3 * j)) * t_12
	t_15 = (((((k * y) * (y5 * i)) - ((y * b) * (y4 * k))) - ((y5 * t) * (i * j))) - ((t_3 * t_1) - t_14)) + (t_8 - (t_11 - (((y2 * x) - (y3 * z)) * ((c * y0) - (y1 * a)))))
	t_16 = ((t_6 - ((y3 * y) * ((y5 * a) - (y4 * c)))) + (((y5 * a) * (t * y2)) + t_13)) + ((t_2 * ((c * y0) - (a * y1))) - (t_10 - (((y * x) - (z * t)) * t_7)))
	t_17 = (t * y2) - (y * y3)
	tmp = 0
	if y4 < -7.206256231996481e+60:
		tmp = (t_8 - (t_11 - t_6)) - ((t_3 / (1.0 / t_1)) - t_14)
	elif y4 < -3.364603505246317e-66:
		tmp = (((((t * c) * (i * z)) - ((a * t) * (b * z))) - ((y * c) * (i * x))) - t_10) + ((((y0 * c) - (a * y1)) * t_2) - ((t_17 * ((y4 * c) - (a * y5))) - (((y1 * y4) - (y5 * y0)) * t_4)))
	elif y4 < -1.2000065055686116e-105:
		tmp = t_16
	elif y4 < 6.718963124057495e-279:
		tmp = t_15
	elif y4 < 4.77962681403792e-222:
		tmp = t_16
	elif y4 < 2.2852241541266835e-175:
		tmp = t_15
	else:
		tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - ((k * (i * (z * y1))) - ((j * (i * (x * y1))) + (y0 * (k * (z * b)))))) + ((z * (y3 * (a * y1))) - ((y2 * (x * (a * y1))) + (y0 * (z * (c * y3)))))) + (((t * j) - (y * k)) * t_5)) - (t_17 * t_1)) + t_13
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y4 * c) - Float64(y5 * a))
	t_2 = Float64(Float64(x * y2) - Float64(z * y3))
	t_3 = Float64(Float64(y2 * t) - Float64(y3 * y))
	t_4 = Float64(Float64(k * y2) - Float64(j * y3))
	t_5 = Float64(Float64(y4 * b) - Float64(y5 * i))
	t_6 = Float64(Float64(Float64(j * t) - Float64(k * y)) * t_5)
	t_7 = Float64(Float64(b * a) - Float64(i * c))
	t_8 = Float64(t_7 * Float64(Float64(y * x) - Float64(t * z)))
	t_9 = Float64(Float64(j * x) - Float64(k * z))
	t_10 = Float64(Float64(Float64(b * y0) - Float64(i * y1)) * t_9)
	t_11 = Float64(t_9 * Float64(Float64(y0 * b) - Float64(i * y1)))
	t_12 = Float64(Float64(y4 * y1) - Float64(y5 * y0))
	t_13 = Float64(t_4 * t_12)
	t_14 = Float64(Float64(Float64(y2 * k) - Float64(y3 * j)) * t_12)
	t_15 = Float64(Float64(Float64(Float64(Float64(Float64(k * y) * Float64(y5 * i)) - Float64(Float64(y * b) * Float64(y4 * k))) - Float64(Float64(y5 * t) * Float64(i * j))) - Float64(Float64(t_3 * t_1) - t_14)) + Float64(t_8 - Float64(t_11 - Float64(Float64(Float64(y2 * x) - Float64(y3 * z)) * Float64(Float64(c * y0) - Float64(y1 * a))))))
	t_16 = Float64(Float64(Float64(t_6 - Float64(Float64(y3 * y) * Float64(Float64(y5 * a) - Float64(y4 * c)))) + Float64(Float64(Float64(y5 * a) * Float64(t * y2)) + t_13)) + Float64(Float64(t_2 * Float64(Float64(c * y0) - Float64(a * y1))) - Float64(t_10 - Float64(Float64(Float64(y * x) - Float64(z * t)) * t_7))))
	t_17 = Float64(Float64(t * y2) - Float64(y * y3))
	tmp = 0.0
	if (y4 < -7.206256231996481e+60)
		tmp = Float64(Float64(t_8 - Float64(t_11 - t_6)) - Float64(Float64(t_3 / Float64(1.0 / t_1)) - t_14));
	elseif (y4 < -3.364603505246317e-66)
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(t * c) * Float64(i * z)) - Float64(Float64(a * t) * Float64(b * z))) - Float64(Float64(y * c) * Float64(i * x))) - t_10) + Float64(Float64(Float64(Float64(y0 * c) - Float64(a * y1)) * t_2) - Float64(Float64(t_17 * Float64(Float64(y4 * c) - Float64(a * y5))) - Float64(Float64(Float64(y1 * y4) - Float64(y5 * y0)) * t_4))));
	elseif (y4 < -1.2000065055686116e-105)
		tmp = t_16;
	elseif (y4 < 6.718963124057495e-279)
		tmp = t_15;
	elseif (y4 < 4.77962681403792e-222)
		tmp = t_16;
	elseif (y4 < 2.2852241541266835e-175)
		tmp = t_15;
	else
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * y) - Float64(z * t)) * Float64(Float64(a * b) - Float64(c * i))) - Float64(Float64(k * Float64(i * Float64(z * y1))) - Float64(Float64(j * Float64(i * Float64(x * y1))) + Float64(y0 * Float64(k * Float64(z * b)))))) + Float64(Float64(z * Float64(y3 * Float64(a * y1))) - Float64(Float64(y2 * Float64(x * Float64(a * y1))) + Float64(y0 * Float64(z * Float64(c * y3)))))) + Float64(Float64(Float64(t * j) - Float64(y * k)) * t_5)) - Float64(t_17 * t_1)) + t_13);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = (y4 * c) - (y5 * a);
	t_2 = (x * y2) - (z * y3);
	t_3 = (y2 * t) - (y3 * y);
	t_4 = (k * y2) - (j * y3);
	t_5 = (y4 * b) - (y5 * i);
	t_6 = ((j * t) - (k * y)) * t_5;
	t_7 = (b * a) - (i * c);
	t_8 = t_7 * ((y * x) - (t * z));
	t_9 = (j * x) - (k * z);
	t_10 = ((b * y0) - (i * y1)) * t_9;
	t_11 = t_9 * ((y0 * b) - (i * y1));
	t_12 = (y4 * y1) - (y5 * y0);
	t_13 = t_4 * t_12;
	t_14 = ((y2 * k) - (y3 * j)) * t_12;
	t_15 = (((((k * y) * (y5 * i)) - ((y * b) * (y4 * k))) - ((y5 * t) * (i * j))) - ((t_3 * t_1) - t_14)) + (t_8 - (t_11 - (((y2 * x) - (y3 * z)) * ((c * y0) - (y1 * a)))));
	t_16 = ((t_6 - ((y3 * y) * ((y5 * a) - (y4 * c)))) + (((y5 * a) * (t * y2)) + t_13)) + ((t_2 * ((c * y0) - (a * y1))) - (t_10 - (((y * x) - (z * t)) * t_7)));
	t_17 = (t * y2) - (y * y3);
	tmp = 0.0;
	if (y4 < -7.206256231996481e+60)
		tmp = (t_8 - (t_11 - t_6)) - ((t_3 / (1.0 / t_1)) - t_14);
	elseif (y4 < -3.364603505246317e-66)
		tmp = (((((t * c) * (i * z)) - ((a * t) * (b * z))) - ((y * c) * (i * x))) - t_10) + ((((y0 * c) - (a * y1)) * t_2) - ((t_17 * ((y4 * c) - (a * y5))) - (((y1 * y4) - (y5 * y0)) * t_4)));
	elseif (y4 < -1.2000065055686116e-105)
		tmp = t_16;
	elseif (y4 < 6.718963124057495e-279)
		tmp = t_15;
	elseif (y4 < 4.77962681403792e-222)
		tmp = t_16;
	elseif (y4 < 2.2852241541266835e-175)
		tmp = t_15;
	else
		tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - ((k * (i * (z * y1))) - ((j * (i * (x * y1))) + (y0 * (k * (z * b)))))) + ((z * (y3 * (a * y1))) - ((y2 * (x * (a * y1))) + (y0 * (z * (c * y3)))))) + (((t * j) - (y * k)) * t_5)) - (t_17 * t_1)) + t_13;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y4 * c), $MachinePrecision] - N[(y5 * a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(y2 * t), $MachinePrecision] - N[(y3 * y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(N[(y4 * b), $MachinePrecision] - N[(y5 * i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(N[(N[(j * t), $MachinePrecision] - N[(k * y), $MachinePrecision]), $MachinePrecision] * t$95$5), $MachinePrecision]}, Block[{t$95$7 = N[(N[(b * a), $MachinePrecision] - N[(i * c), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$8 = N[(t$95$7 * N[(N[(y * x), $MachinePrecision] - N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$9 = N[(N[(j * x), $MachinePrecision] - N[(k * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$10 = N[(N[(N[(b * y0), $MachinePrecision] - N[(i * y1), $MachinePrecision]), $MachinePrecision] * t$95$9), $MachinePrecision]}, Block[{t$95$11 = N[(t$95$9 * N[(N[(y0 * b), $MachinePrecision] - N[(i * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$12 = N[(N[(y4 * y1), $MachinePrecision] - N[(y5 * y0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$13 = N[(t$95$4 * t$95$12), $MachinePrecision]}, Block[{t$95$14 = N[(N[(N[(y2 * k), $MachinePrecision] - N[(y3 * j), $MachinePrecision]), $MachinePrecision] * t$95$12), $MachinePrecision]}, Block[{t$95$15 = N[(N[(N[(N[(N[(N[(k * y), $MachinePrecision] * N[(y5 * i), $MachinePrecision]), $MachinePrecision] - N[(N[(y * b), $MachinePrecision] * N[(y4 * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(y5 * t), $MachinePrecision] * N[(i * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(t$95$3 * t$95$1), $MachinePrecision] - t$95$14), $MachinePrecision]), $MachinePrecision] + N[(t$95$8 - N[(t$95$11 - N[(N[(N[(y2 * x), $MachinePrecision] - N[(y3 * z), $MachinePrecision]), $MachinePrecision] * N[(N[(c * y0), $MachinePrecision] - N[(y1 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$16 = N[(N[(N[(t$95$6 - N[(N[(y3 * y), $MachinePrecision] * N[(N[(y5 * a), $MachinePrecision] - N[(y4 * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y5 * a), $MachinePrecision] * N[(t * y2), $MachinePrecision]), $MachinePrecision] + t$95$13), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$2 * N[(N[(c * y0), $MachinePrecision] - N[(a * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(t$95$10 - N[(N[(N[(y * x), $MachinePrecision] - N[(z * t), $MachinePrecision]), $MachinePrecision] * t$95$7), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$17 = N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision]}, If[Less[y4, -7.206256231996481e+60], N[(N[(t$95$8 - N[(t$95$11 - t$95$6), $MachinePrecision]), $MachinePrecision] - N[(N[(t$95$3 / N[(1.0 / t$95$1), $MachinePrecision]), $MachinePrecision] - t$95$14), $MachinePrecision]), $MachinePrecision], If[Less[y4, -3.364603505246317e-66], N[(N[(N[(N[(N[(N[(t * c), $MachinePrecision] * N[(i * z), $MachinePrecision]), $MachinePrecision] - N[(N[(a * t), $MachinePrecision] * N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(y * c), $MachinePrecision] * N[(i * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$10), $MachinePrecision] + N[(N[(N[(N[(y0 * c), $MachinePrecision] - N[(a * y1), $MachinePrecision]), $MachinePrecision] * t$95$2), $MachinePrecision] - N[(N[(t$95$17 * N[(N[(y4 * c), $MachinePrecision] - N[(a * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(y1 * y4), $MachinePrecision] - N[(y5 * y0), $MachinePrecision]), $MachinePrecision] * t$95$4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Less[y4, -1.2000065055686116e-105], t$95$16, If[Less[y4, 6.718963124057495e-279], t$95$15, If[Less[y4, 4.77962681403792e-222], t$95$16, If[Less[y4, 2.2852241541266835e-175], t$95$15, N[(N[(N[(N[(N[(N[(N[(N[(x * y), $MachinePrecision] - N[(z * t), $MachinePrecision]), $MachinePrecision] * N[(N[(a * b), $MachinePrecision] - N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(k * N[(i * N[(z * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(j * N[(i * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y0 * N[(k * N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(z * N[(y3 * N[(a * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(y2 * N[(x * N[(a * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y0 * N[(z * N[(c * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision] * t$95$5), $MachinePrecision]), $MachinePrecision] - N[(t$95$17 * t$95$1), $MachinePrecision]), $MachinePrecision] + t$95$13), $MachinePrecision]]]]]]]]]]]]]]]]]]]]]]]]

Reproduce

herbie shell --seed 2024366 
(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
  :name "Linear.Matrix:det44 from linear-1.19.1.3"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< y4 -7206256231996481000000000000000000000000000000000000000000000) (- (- (* (- (* b a) (* i c)) (- (* y x) (* t z))) (- (* (- (* j x) (* k z)) (- (* y0 b) (* i y1))) (* (- (* j t) (* k y)) (- (* y4 b) (* y5 i))))) (- (/ (- (* y2 t) (* y3 y)) (/ 1 (- (* y4 c) (* y5 a)))) (* (- (* y2 k) (* y3 j)) (- (* y4 y1) (* y5 y0))))) (if (< y4 -3364603505246317/1000000000000000000000000000000000000000000000000000000000000000000000000000000000) (+ (- (- (- (* (* t c) (* i z)) (* (* a t) (* b z))) (* (* y c) (* i x))) (* (- (* b y0) (* i y1)) (- (* j x) (* k z)))) (- (* (- (* y0 c) (* a y1)) (- (* x y2) (* z y3))) (- (* (- (* t y2) (* y y3)) (- (* y4 c) (* a y5))) (* (- (* y1 y4) (* y5 y0)) (- (* k y2) (* j y3)))))) (if (< y4 -3000016263921529/2500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (+ (+ (- (* (- (* j t) (* k y)) (- (* y4 b) (* y5 i))) (* (* y3 y) (- (* y5 a) (* y4 c)))) (+ (* (* y5 a) (* t y2)) (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0))))) (- (* (- (* x y2) (* z y3)) (- (* c y0) (* a y1))) (- (* (- (* b y0) (* i y1)) (- (* j x) (* k z))) (* (- (* y x) (* z t)) (- (* b a) (* i c)))))) (if (< y4 1343792624811499/200000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (+ (- (- (- (* (* k y) (* y5 i)) (* (* y b) (* y4 k))) (* (* y5 t) (* i j))) (- (* (- (* y2 t) (* y3 y)) (- (* y4 c) (* y5 a))) (* (- (* y2 k) (* y3 j)) (- (* y4 y1) (* y5 y0))))) (- (* (- (* b a) (* i c)) (- (* y x) (* t z))) (- (* (- (* j x) (* k z)) (- (* y0 b) (* i y1))) (* (- (* y2 x) (* y3 z)) (- (* c y0) (* y1 a)))))) (if (< y4 29872667587737/6250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (+ (+ (- (* (- (* j t) (* k y)) (- (* y4 b) (* y5 i))) (* (* y3 y) (- (* y5 a) (* y4 c)))) (+ (* (* y5 a) (* t y2)) (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0))))) (- (* (- (* x y2) (* z y3)) (- (* c y0) (* a y1))) (- (* (- (* b y0) (* i y1)) (- (* j x) (* k z))) (* (- (* y x) (* z t)) (- (* b a) (* i c)))))) (if (< y4 4570448308253367/20000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (+ (- (- (- (* (* k y) (* y5 i)) (* (* y b) (* y4 k))) (* (* y5 t) (* i j))) (- (* (- (* y2 t) (* y3 y)) (- (* y4 c) (* y5 a))) (* (- (* y2 k) (* y3 j)) (- (* y4 y1) (* y5 y0))))) (- (* (- (* b a) (* i c)) (- (* y x) (* t z))) (- (* (- (* j x) (* k z)) (- (* y0 b) (* i y1))) (* (- (* y2 x) (* y3 z)) (- (* c y0) (* y1 a)))))) (+ (- (+ (+ (- (* (- (* x y) (* z t)) (- (* a b) (* c i))) (- (* k (* i (* z y1))) (+ (* j (* i (* x y1))) (* y0 (* k (* z b)))))) (- (* z (* y3 (* a y1))) (+ (* y2 (* x (* a y1))) (* y0 (* z (* c y3)))))) (* (- (* t j) (* y k)) (- (* y4 b) (* y5 i)))) (* (- (* t y2) (* y y3)) (- (* y4 c) (* y5 a)))) (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0)))))))))))

  (+ (- (+ (+ (- (* (- (* x y) (* z t)) (- (* a b) (* c i))) (* (- (* x j) (* z k)) (- (* y0 b) (* y1 i)))) (* (- (* x y2) (* z y3)) (- (* y0 c) (* y1 a)))) (* (- (* t j) (* y k)) (- (* y4 b) (* y5 i)))) (* (- (* t y2) (* y y3)) (- (* y4 c) (* y5 a)))) (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0)))))