Diagrams.Solve.Tridiagonal:solveCyclicTriDiagonal from diagrams-solve-0.1, B

Percentage Accurate: 75.0% → 91.2%

Time: 5.4s

Alternatives: 13

Speedup: 0.8×

Specification

Math

\[\begin{array}{l} \\ \frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (/ (+ x (/ (* y z) t)) (+ (+ a 1.0) (/ (* y b) t))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
}

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)
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
    code = (x + ((y * z) / t)) / ((a + 1.0d0) + ((y * b) / t))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
}

Python

def code(x, y, z, t, a, b):
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t))

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(Float64(a + 1.0) + Float64(Float64(y * b) / t)))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(N[(a + 1.0), $MachinePrecision] + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 75.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (/ (+ x (/ (* y z) t)) (+ (+ a 1.0) (/ (* y b) t))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
}

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)
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
    code = (x + ((y * z) / t)) / ((a + 1.0d0) + ((y * b) / t))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
}

Python

def code(x, y, z, t, a, b):
	return (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t))

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(Float64(a + 1.0) + Float64(Float64(y * b) / t)))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(N[(a + 1.0), $MachinePrecision] + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 91.2% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y \cdot z}{t}\\ t_2 := \frac{t\_1}{\left(a + 1\right) + \frac{y \cdot b}{t}}\\ t_3 := z \cdot \frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)}\\ \mathbf{if}\;t\_2 \leq -1 \cdot 10^{+297}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_2 \leq 10^{+308}:\\ \;\;\;\;\frac{t\_1}{\mathsf{fma}\left(b, \frac{y}{t}, a - -1\right)}\\ \mathbf{elif}\;t\_2 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (/ (* y z) t)))
        (t_2 (/ t_1 (+ (+ a 1.0) (/ (* y b) t))))
        (t_3 (* z (/ y (fma b y (fma a t t))))))
   (if (<= t_2 -1e+297)
     t_3
     (if (<= t_2 1e+308)
       (/ t_1 (fma b (/ y t) (- a -1.0)))
       (if (<= t_2 INFINITY) t_3 (/ (fma t (/ x y) z) b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + ((y * z) / t);
	double t_2 = t_1 / ((a + 1.0) + ((y * b) / t));
	double t_3 = z * (y / fma(b, y, fma(a, t, t)));
	double tmp;
	if (t_2 <= -1e+297) {
		tmp = t_3;
	} else if (t_2 <= 1e+308) {
		tmp = t_1 / fma(b, (y / t), (a - -1.0));
	} else if (t_2 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = fma(t, (x / y), z) / b;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(Float64(y * z) / t))
	t_2 = Float64(t_1 / Float64(Float64(a + 1.0) + Float64(Float64(y * b) / t)))
	t_3 = Float64(z * Float64(y / fma(b, y, fma(a, t, t))))
	tmp = 0.0
	if (t_2 <= -1e+297)
		tmp = t_3;
	elseif (t_2 <= 1e+308)
		tmp = Float64(t_1 / fma(b, Float64(y / t), Float64(a - -1.0)));
	elseif (t_2 <= Inf)
		tmp = t_3;
	else
		tmp = Float64(fma(t, Float64(x / y), z) / b);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 / N[(N[(a + 1.0), $MachinePrecision] + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(z * N[(y / N[(b * y + N[(a * t + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -1e+297], t$95$3, If[LessEqual[t$95$2, 1e+308], N[(t$95$1 / N[(b * N[(y / t), $MachinePrecision] + N[(a - -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, Infinity], t$95$3, N[(N[(t * N[(x / y), $MachinePrecision] + z), $MachinePrecision] / b), $MachinePrecision]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 x (/.f64 (*.f64 y z) t)) (+.f64 (+.f64 a #s(literal 1 binary64)) (/.f64 (*.f64 y b) t))) < -1e297 or 1e308 < (/.f64 (+.f64 x (/.f64 (*.f64 y z) t)) (+.f64 (+.f64 a #s(literal 1 binary64)) (/.f64 (*.f64 y b) t))) < +inf.0

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}}} \]

      2. lift-+.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\left(a + 1\right) + \frac{y \cdot b}{t}}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \color{blue}{\frac{y \cdot b}{t}}} \]

      4. add-to-fraction N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\frac{\left(a + 1\right) \cdot t + y \cdot b}{t}}} \]

      5. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) \cdot t + y \cdot b} \cdot t} \]

      6. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) \cdot t + y \cdot b} \cdot t} \]

   3. Applied rewrites 62.2%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\mathsf{fma}\left(b, y, \left(a - -1\right) \cdot t\right)} \cdot t} \]

   4. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{y \cdot z}{b \cdot y + t \cdot \left(1 + a\right)}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{b \cdot y + t \cdot \left(1 + a\right)}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{b \cdot y} + t \cdot \left(1 + a\right)} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, \color{blue}{y}, t \cdot \left(1 + a\right)\right)} \]

      4. lower-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      5. lower-+.f64 41.8

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

   6. Applied rewrites 41.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(\color{blue}{b}, y, t \cdot \left(1 + a\right)\right)} \]

      3. *-commutative N/A

         \[\leadsto \frac{z \cdot y}{\mathsf{fma}\left(\color{blue}{b}, y, t \cdot \left(1 + a\right)\right)} \]

      4. associate-/l* N/A

         \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      5. lower-*.f64 N/A

         \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      6. lower-/.f64 45.7

         \[\leadsto z \cdot \frac{y}{\color{blue}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      7. lift-*.f64 N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      8. lift-+.f64 N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      9. +-commutative N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(a + 1\right)\right)} \]

      10. distribute-rgt-in N/A

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, a \cdot t + 1 \cdot t\right)} \]

      11. *-lft-identity N/A

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, a \cdot t + t\right)} \]

      12. lower-fma.f64 45.7

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)} \]

   8. Applied rewrites 45.7%

      \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)}} \]

   if -1e297 < (/.f64 (+.f64 x (/.f64 (*.f64 y z) t)) (+.f64 (+.f64 a #s(literal 1 binary64)) (/.f64 (*.f64 y b) t))) < 1e308

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\left(a + 1\right) + \frac{y \cdot b}{t}}} \]

      2. +-commutative N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\frac{y \cdot b}{t} + \left(a + 1\right)}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\frac{y \cdot b}{t}} + \left(a + 1\right)} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\frac{\color{blue}{y \cdot b}}{t} + \left(a + 1\right)} \]

      5. *-commutative N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\frac{\color{blue}{b \cdot y}}{t} + \left(a + 1\right)} \]

      6. associate-/l* N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{b \cdot \frac{y}{t}} + \left(a + 1\right)} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\mathsf{fma}\left(b, \frac{y}{t}, a + 1\right)}} \]

      8. lower-/.f64 74.9

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\mathsf{fma}\left(b, \color{blue}{\frac{y}{t}}, a + 1\right)} \]

      9. lift-+.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\mathsf{fma}\left(b, \frac{y}{t}, \color{blue}{a + 1}\right)} \]

      10. add-flip N/A

          \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\mathsf{fma}\left(b, \frac{y}{t}, \color{blue}{a - \left(\mathsf{neg}\left(1\right)\right)}\right)} \]

      11. lower--.f64 N/A

          \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\mathsf{fma}\left(b, \frac{y}{t}, \color{blue}{a - \left(\mathsf{neg}\left(1\right)\right)}\right)} \]

      12. metadata-eval 74.9

          \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\mathsf{fma}\left(b, \frac{y}{t}, a - \color{blue}{-1}\right)} \]

   3. Applied rewrites 74.9%

      \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\mathsf{fma}\left(b, \frac{y}{t}, a - -1\right)}} \]

   if +inf.0 < (/.f64 (+.f64 x (/.f64 (*.f64 y z) t)) (+.f64 (+.f64 a #s(literal 1 binary64)) (/.f64 (*.f64 y b) t)))

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b} \cdot y} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      6. lower-*.f64 25.7

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

   4. Applied rewrites 25.7%

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

      3. *-commutative N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y \cdot \color{blue}{b}} \]

      4. associate-/r* N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

   6. Applied rewrites 34.1%

      \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{\color{blue}{b}} \]
   7. Step-by-step derivation

      1. associate-*l/ 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      2. *-commutative 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      3. +-commutative 34.1

         \[\leadsto \frac{\frac{\color{blue}{\mathsf{fma}\left(x, t, y \cdot z\right)}}{y}}{b} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{b} \]

      5. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t + y \cdot z}{y}}{b} \]

      6. +-commutative N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      8. *-commutative N/A

         \[\leadsto \frac{\frac{z \cdot y + x \cdot t}{y}}{b} \]

      9. add-to-fraction-rev N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      10. lower-+.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      11. lower-/.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      12. lower-*.f64 40.0

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

   8. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\frac{z + \frac{x \cdot t}{y}}{b}} \]
   9. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      2. +-commutative N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{t \cdot x}{y} + z}{b} \]

      6. associate-/l* N/A

         \[\leadsto \frac{t \cdot \frac{x}{y} + z}{b} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

      8. lower-/.f64 39.8

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   10. Applied rewrites 39.8%

       \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 2: 87.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}}\\ \mathbf{if}\;t\_1 \leq 10^{+308}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_1 \leq \infty:\\ \;\;\;\;z \cdot \frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (* y z) t)) (+ (+ a 1.0) (/ (* y b) t)))))
   (if (<= t_1 1e+308)
     t_1
     (if (<= t_1 INFINITY)
       (* z (/ y (fma b y (fma a t t))))
       (/ (fma t (/ x y) z) b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
	double tmp;
	if (t_1 <= 1e+308) {
		tmp = t_1;
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = z * (y / fma(b, y, fma(a, t, t)));
	} else {
		tmp = fma(t, (x / y), z) / b;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(Float64(a + 1.0) + Float64(Float64(y * b) / t)))
	tmp = 0.0
	if (t_1 <= 1e+308)
		tmp = t_1;
	elseif (t_1 <= Inf)
		tmp = Float64(z * Float64(y / fma(b, y, fma(a, t, t))));
	else
		tmp = Float64(fma(t, Float64(x / y), z) / b);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(N[(a + 1.0), $MachinePrecision] + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 1e+308], t$95$1, If[LessEqual[t$95$1, Infinity], N[(z * N[(y / N[(b * y + N[(a * t + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(t * N[(x / y), $MachinePrecision] + z), $MachinePrecision] / b), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 x (/.f64 (*.f64 y z) t)) (+.f64 (+.f64 a #s(literal 1 binary64)) (/.f64 (*.f64 y b) t))) < 1e308

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   if 1e308 < (/.f64 (+.f64 x (/.f64 (*.f64 y z) t)) (+.f64 (+.f64 a #s(literal 1 binary64)) (/.f64 (*.f64 y b) t))) < +inf.0

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}}} \]

      2. lift-+.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\left(a + 1\right) + \frac{y \cdot b}{t}}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \color{blue}{\frac{y \cdot b}{t}}} \]

      4. add-to-fraction N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\frac{\left(a + 1\right) \cdot t + y \cdot b}{t}}} \]

      5. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) \cdot t + y \cdot b} \cdot t} \]

      6. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) \cdot t + y \cdot b} \cdot t} \]

   3. Applied rewrites 62.2%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\mathsf{fma}\left(b, y, \left(a - -1\right) \cdot t\right)} \cdot t} \]

   4. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{y \cdot z}{b \cdot y + t \cdot \left(1 + a\right)}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{b \cdot y + t \cdot \left(1 + a\right)}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{b \cdot y} + t \cdot \left(1 + a\right)} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, \color{blue}{y}, t \cdot \left(1 + a\right)\right)} \]

      4. lower-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      5. lower-+.f64 41.8

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

   6. Applied rewrites 41.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(\color{blue}{b}, y, t \cdot \left(1 + a\right)\right)} \]

      3. *-commutative N/A

         \[\leadsto \frac{z \cdot y}{\mathsf{fma}\left(\color{blue}{b}, y, t \cdot \left(1 + a\right)\right)} \]

      4. associate-/l* N/A

         \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      5. lower-*.f64 N/A

         \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      6. lower-/.f64 45.7

         \[\leadsto z \cdot \frac{y}{\color{blue}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      7. lift-*.f64 N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      8. lift-+.f64 N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      9. +-commutative N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(a + 1\right)\right)} \]

      10. distribute-rgt-in N/A

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, a \cdot t + 1 \cdot t\right)} \]

      11. *-lft-identity N/A

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, a \cdot t + t\right)} \]

      12. lower-fma.f64 45.7

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)} \]

   8. Applied rewrites 45.7%

      \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)}} \]

   if +inf.0 < (/.f64 (+.f64 x (/.f64 (*.f64 y z) t)) (+.f64 (+.f64 a #s(literal 1 binary64)) (/.f64 (*.f64 y b) t)))

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b} \cdot y} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      6. lower-*.f64 25.7

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

   4. Applied rewrites 25.7%

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

      3. *-commutative N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y \cdot \color{blue}{b}} \]

      4. associate-/r* N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

   6. Applied rewrites 34.1%

      \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{\color{blue}{b}} \]
   7. Step-by-step derivation

      1. associate-*l/ 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      2. *-commutative 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      3. +-commutative 34.1

         \[\leadsto \frac{\frac{\color{blue}{\mathsf{fma}\left(x, t, y \cdot z\right)}}{y}}{b} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{b} \]

      5. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t + y \cdot z}{y}}{b} \]

      6. +-commutative N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      8. *-commutative N/A

         \[\leadsto \frac{\frac{z \cdot y + x \cdot t}{y}}{b} \]

      9. add-to-fraction-rev N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      10. lower-+.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      11. lower-/.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      12. lower-*.f64 40.0

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

   8. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\frac{z + \frac{x \cdot t}{y}}{b}} \]
   9. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      2. +-commutative N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{t \cdot x}{y} + z}{b} \]

      6. associate-/l* N/A

         \[\leadsto \frac{t \cdot \frac{x}{y} + z}{b} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

      8. lower-/.f64 39.8

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   10. Applied rewrites 39.8%

       \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 84.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}}\\ \mathbf{if}\;t\_1 \leq 10^{+308}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\mathsf{fma}\left(\frac{b}{t}, y, a - -1\right)}\\ \mathbf{elif}\;t\_1 \leq \infty:\\ \;\;\;\;z \cdot \frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (* y z) t)) (+ (+ a 1.0) (/ (* y b) t)))))
   (if (<= t_1 1e+308)
     (/ (fma (/ z t) y x) (fma (/ b t) y (- a -1.0)))
     (if (<= t_1 INFINITY)
       (* z (/ y (fma b y (fma a t t))))
       (/ (fma t (/ x y) z) b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + ((y * z) / t)) / ((a + 1.0) + ((y * b) / t));
	double tmp;
	if (t_1 <= 1e+308) {
		tmp = fma((z / t), y, x) / fma((b / t), y, (a - -1.0));
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = z * (y / fma(b, y, fma(a, t, t)));
	} else {
		tmp = fma(t, (x / y), z) / b;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + Float64(Float64(y * z) / t)) / Float64(Float64(a + 1.0) + Float64(Float64(y * b) / t)))
	tmp = 0.0
	if (t_1 <= 1e+308)
		tmp = Float64(fma(Float64(z / t), y, x) / fma(Float64(b / t), y, Float64(a - -1.0)));
	elseif (t_1 <= Inf)
		tmp = Float64(z * Float64(y / fma(b, y, fma(a, t, t))));
	else
		tmp = Float64(fma(t, Float64(x / y), z) / b);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(N[(a + 1.0), $MachinePrecision] + N[(N[(y * b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 1e+308], N[(N[(N[(z / t), $MachinePrecision] * y + x), $MachinePrecision] / N[(N[(b / t), $MachinePrecision] * y + N[(a - -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, Infinity], N[(z * N[(y / N[(b * y + N[(a * t + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(t * N[(x / y), $MachinePrecision] + z), $MachinePrecision] / b), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 x (/.f64 (*.f64 y z) t)) (+.f64 (+.f64 a #s(literal 1 binary64)) (/.f64 (*.f64 y b) t))) < 1e308

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{y \cdot \frac{z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. *-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{z}{t} \cdot y} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 74.2

         \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\frac{z}{t}}, y, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      9. lift-+.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\color{blue}{\left(a + 1\right) + \frac{y \cdot b}{t}}} \]

      10. +-commutative N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\color{blue}{\frac{y \cdot b}{t} + \left(a + 1\right)}} \]

      11. lift-/.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\color{blue}{\frac{y \cdot b}{t}} + \left(a + 1\right)} \]

      12. lift-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\frac{\color{blue}{y \cdot b}}{t} + \left(a + 1\right)} \]

      13. associate-/l* N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\color{blue}{y \cdot \frac{b}{t}} + \left(a + 1\right)} \]

      14. *-commutative N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\color{blue}{\frac{b}{t} \cdot y} + \left(a + 1\right)} \]

      15. lower-fma.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\color{blue}{\mathsf{fma}\left(\frac{b}{t}, y, a + 1\right)}} \]

      16. lower-/.f64 74.9

          \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\mathsf{fma}\left(\color{blue}{\frac{b}{t}}, y, a + 1\right)} \]

      17. lift-+.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\mathsf{fma}\left(\frac{b}{t}, y, \color{blue}{a + 1}\right)} \]

      18. add-flip N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\mathsf{fma}\left(\frac{b}{t}, y, \color{blue}{a - \left(\mathsf{neg}\left(1\right)\right)}\right)} \]

      19. lower--.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\mathsf{fma}\left(\frac{b}{t}, y, \color{blue}{a - \left(\mathsf{neg}\left(1\right)\right)}\right)} \]

      20. metadata-eval 74.9

          \[\leadsto \frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\mathsf{fma}\left(\frac{b}{t}, y, a - \color{blue}{-1}\right)} \]

   3. Applied rewrites 74.9%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\mathsf{fma}\left(\frac{b}{t}, y, a - -1\right)}} \]

   if 1e308 < (/.f64 (+.f64 x (/.f64 (*.f64 y z) t)) (+.f64 (+.f64 a #s(literal 1 binary64)) (/.f64 (*.f64 y b) t))) < +inf.0

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}}} \]

      2. lift-+.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\left(a + 1\right) + \frac{y \cdot b}{t}}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \color{blue}{\frac{y \cdot b}{t}}} \]

      4. add-to-fraction N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\frac{\left(a + 1\right) \cdot t + y \cdot b}{t}}} \]

      5. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) \cdot t + y \cdot b} \cdot t} \]

      6. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) \cdot t + y \cdot b} \cdot t} \]

   3. Applied rewrites 62.2%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\mathsf{fma}\left(b, y, \left(a - -1\right) \cdot t\right)} \cdot t} \]

   4. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{y \cdot z}{b \cdot y + t \cdot \left(1 + a\right)}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{b \cdot y + t \cdot \left(1 + a\right)}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{b \cdot y} + t \cdot \left(1 + a\right)} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, \color{blue}{y}, t \cdot \left(1 + a\right)\right)} \]

      4. lower-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      5. lower-+.f64 41.8

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

   6. Applied rewrites 41.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(\color{blue}{b}, y, t \cdot \left(1 + a\right)\right)} \]

      3. *-commutative N/A

         \[\leadsto \frac{z \cdot y}{\mathsf{fma}\left(\color{blue}{b}, y, t \cdot \left(1 + a\right)\right)} \]

      4. associate-/l* N/A

         \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      5. lower-*.f64 N/A

         \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      6. lower-/.f64 45.7

         \[\leadsto z \cdot \frac{y}{\color{blue}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      7. lift-*.f64 N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      8. lift-+.f64 N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      9. +-commutative N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(a + 1\right)\right)} \]

      10. distribute-rgt-in N/A

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, a \cdot t + 1 \cdot t\right)} \]

      11. *-lft-identity N/A

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, a \cdot t + t\right)} \]

      12. lower-fma.f64 45.7

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)} \]

   8. Applied rewrites 45.7%

      \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)}} \]

   if +inf.0 < (/.f64 (+.f64 x (/.f64 (*.f64 y z) t)) (+.f64 (+.f64 a #s(literal 1 binary64)) (/.f64 (*.f64 y b) t)))

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b} \cdot y} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      6. lower-*.f64 25.7

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

   4. Applied rewrites 25.7%

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

      3. *-commutative N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y \cdot \color{blue}{b}} \]

      4. associate-/r* N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

   6. Applied rewrites 34.1%

      \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{\color{blue}{b}} \]
   7. Step-by-step derivation

      1. associate-*l/ 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      2. *-commutative 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      3. +-commutative 34.1

         \[\leadsto \frac{\frac{\color{blue}{\mathsf{fma}\left(x, t, y \cdot z\right)}}{y}}{b} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{b} \]

      5. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t + y \cdot z}{y}}{b} \]

      6. +-commutative N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      8. *-commutative N/A

         \[\leadsto \frac{\frac{z \cdot y + x \cdot t}{y}}{b} \]

      9. add-to-fraction-rev N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      10. lower-+.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      11. lower-/.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      12. lower-*.f64 40.0

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

   8. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\frac{z + \frac{x \cdot t}{y}}{b}} \]
   9. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      2. +-commutative N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{t \cdot x}{y} + z}{b} \]

      6. associate-/l* N/A

         \[\leadsto \frac{t \cdot \frac{x}{y} + z}{b} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

      8. lower-/.f64 39.8

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   10. Applied rewrites 39.8%

       \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 81.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{1 + a}\\ \mathbf{if}\;t \leq -7.8 \cdot 10^{+154}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 3.6 \cdot 10^{+55}:\\ \;\;\;\;\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (fma z (/ y t) x) (+ 1.0 a))))
   (if (<= t -7.8e+154)
     t_1
     (if (<= t 3.6e+55) (/ (fma x t (* y z)) (fma b y (fma a t t))) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = fma(z, (y / t), x) / (1.0 + a);
	double tmp;
	if (t <= -7.8e+154) {
		tmp = t_1;
	} else if (t <= 3.6e+55) {
		tmp = fma(x, t, (y * z)) / fma(b, y, fma(a, t, t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(fma(z, Float64(y / t), x) / Float64(1.0 + a))
	tmp = 0.0
	if (t <= -7.8e+154)
		tmp = t_1;
	elseif (t <= 3.6e+55)
		tmp = Float64(fma(x, t, Float64(y * z)) / fma(b, y, fma(a, t, t)));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z * N[(y / t), $MachinePrecision] + x), $MachinePrecision] / N[(1.0 + a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -7.8e+154], t$95$1, If[LessEqual[t, 3.6e+55], N[(N[(x * t + N[(y * z), $MachinePrecision]), $MachinePrecision] / N[(b * y + N[(a * t + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if t < -7.8000000000000006e154 or 3.59999999999999987e55 < t

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in y around 0

      \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{\color{blue}{1 + a}} \]
   5. Step-by-step derivation

      1. lower-+.f64 58.4

         \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{1 + \color{blue}{a}} \]

   6. Applied rewrites 58.4%

      \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{\color{blue}{1 + a}} \]

   if -7.8000000000000006e154 < t < 3.59999999999999987e55

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   4. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. div-flip N/A

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{1}{\frac{t}{y}}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. associate-/r/ N/A

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{1}{t} \cdot y}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lower-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{1}{t} \cdot y}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{1}{t}} \cdot y, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   5. Applied rewrites 75.1%

      \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{1}{t} \cdot y}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   7. Applied rewrites 69.8%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 69.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b}\\ \mathbf{if}\;y \leq -1.45 \cdot 10^{+156}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq -9.4 \cdot 10^{-101}:\\ \;\;\;\;z \cdot \frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)}\\ \mathbf{elif}\;y \leq 3 \cdot 10^{-222}:\\ \;\;\;\;\frac{x}{1 + \left(a + \frac{b \cdot y}{t}\right)}\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{+76}:\\ \;\;\;\;\frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{1 + a}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (fma t (/ x y) z) b)))
   (if (<= y -1.45e+156)
     t_1
     (if (<= y -9.4e-101)
       (* z (/ y (fma b y (fma a t t))))
       (if (<= y 3e-222)
         (/ x (+ 1.0 (+ a (/ (* b y) t))))
         (if (<= y 2.2e+76) (/ (fma z (/ y t) x) (+ 1.0 a)) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = fma(t, (x / y), z) / b;
	double tmp;
	if (y <= -1.45e+156) {
		tmp = t_1;
	} else if (y <= -9.4e-101) {
		tmp = z * (y / fma(b, y, fma(a, t, t)));
	} else if (y <= 3e-222) {
		tmp = x / (1.0 + (a + ((b * y) / t)));
	} else if (y <= 2.2e+76) {
		tmp = fma(z, (y / t), x) / (1.0 + a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(fma(t, Float64(x / y), z) / b)
	tmp = 0.0
	if (y <= -1.45e+156)
		tmp = t_1;
	elseif (y <= -9.4e-101)
		tmp = Float64(z * Float64(y / fma(b, y, fma(a, t, t))));
	elseif (y <= 3e-222)
		tmp = Float64(x / Float64(1.0 + Float64(a + Float64(Float64(b * y) / t))));
	elseif (y <= 2.2e+76)
		tmp = Float64(fma(z, Float64(y / t), x) / Float64(1.0 + a));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(t * N[(x / y), $MachinePrecision] + z), $MachinePrecision] / b), $MachinePrecision]}, If[LessEqual[y, -1.45e+156], t$95$1, If[LessEqual[y, -9.4e-101], N[(z * N[(y / N[(b * y + N[(a * t + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3e-222], N[(x / N[(1.0 + N[(a + N[(N[(b * y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.2e+76], N[(N[(z * N[(y / t), $MachinePrecision] + x), $MachinePrecision] / N[(1.0 + a), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -1.45000000000000005e156 or 2.2e76 < y

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b} \cdot y} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      6. lower-*.f64 25.7

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

   4. Applied rewrites 25.7%

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

      3. *-commutative N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y \cdot \color{blue}{b}} \]

      4. associate-/r* N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

   6. Applied rewrites 34.1%

      \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{\color{blue}{b}} \]
   7. Step-by-step derivation

      1. associate-*l/ 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      2. *-commutative 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      3. +-commutative 34.1

         \[\leadsto \frac{\frac{\color{blue}{\mathsf{fma}\left(x, t, y \cdot z\right)}}{y}}{b} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{b} \]

      5. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t + y \cdot z}{y}}{b} \]

      6. +-commutative N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      8. *-commutative N/A

         \[\leadsto \frac{\frac{z \cdot y + x \cdot t}{y}}{b} \]

      9. add-to-fraction-rev N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      10. lower-+.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      11. lower-/.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      12. lower-*.f64 40.0

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

   8. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\frac{z + \frac{x \cdot t}{y}}{b}} \]
   9. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      2. +-commutative N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{t \cdot x}{y} + z}{b} \]

      6. associate-/l* N/A

         \[\leadsto \frac{t \cdot \frac{x}{y} + z}{b} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

      8. lower-/.f64 39.8

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   10. Applied rewrites 39.8%

       \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   if -1.45000000000000005e156 < y < -9.3999999999999999e-101

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}}} \]

      2. lift-+.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\left(a + 1\right) + \frac{y \cdot b}{t}}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \color{blue}{\frac{y \cdot b}{t}}} \]

      4. add-to-fraction N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{\frac{\left(a + 1\right) \cdot t + y \cdot b}{t}}} \]

      5. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) \cdot t + y \cdot b} \cdot t} \]

      6. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) \cdot t + y \cdot b} \cdot t} \]

   3. Applied rewrites 62.2%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{z}{t}, y, x\right)}{\mathsf{fma}\left(b, y, \left(a - -1\right) \cdot t\right)} \cdot t} \]

   4. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{y \cdot z}{b \cdot y + t \cdot \left(1 + a\right)}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{b \cdot y + t \cdot \left(1 + a\right)}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{b \cdot y} + t \cdot \left(1 + a\right)} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, \color{blue}{y}, t \cdot \left(1 + a\right)\right)} \]

      4. lower-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      5. lower-+.f64 41.8

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

   6. Applied rewrites 41.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{\color{blue}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{\mathsf{fma}\left(\color{blue}{b}, y, t \cdot \left(1 + a\right)\right)} \]

      3. *-commutative N/A

         \[\leadsto \frac{z \cdot y}{\mathsf{fma}\left(\color{blue}{b}, y, t \cdot \left(1 + a\right)\right)} \]

      4. associate-/l* N/A

         \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      5. lower-*.f64 N/A

         \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      6. lower-/.f64 45.7

         \[\leadsto z \cdot \frac{y}{\color{blue}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)}} \]

      7. lift-*.f64 N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      8. lift-+.f64 N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(1 + a\right)\right)} \]

      9. +-commutative N/A

         \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, t \cdot \left(a + 1\right)\right)} \]

      10. distribute-rgt-in N/A

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, a \cdot t + 1 \cdot t\right)} \]

      11. *-lft-identity N/A

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, a \cdot t + t\right)} \]

      12. lower-fma.f64 45.7

          \[\leadsto z \cdot \frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)} \]

   8. Applied rewrites 45.7%

      \[\leadsto z \cdot \color{blue}{\frac{y}{\mathsf{fma}\left(b, y, \mathsf{fma}\left(a, t, t\right)\right)}} \]

   if -9.3999999999999999e-101 < y < 3.0000000000000003e-222

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{x}{1 + \left(a + \frac{b \cdot y}{t}\right)}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{1 + \left(a + \frac{b \cdot y}{t}\right)}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{x}{1 + \color{blue}{\left(a + \frac{b \cdot y}{t}\right)}} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{x}{1 + \left(a + \color{blue}{\frac{b \cdot y}{t}}\right)} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{x}{1 + \left(a + \frac{b \cdot y}{\color{blue}{t}}\right)} \]

      5. lower-*.f64 52.7

         \[\leadsto \frac{x}{1 + \left(a + \frac{b \cdot y}{t}\right)} \]

   4. Applied rewrites 52.7%

      \[\leadsto \color{blue}{\frac{x}{1 + \left(a + \frac{b \cdot y}{t}\right)}} \]

   if 3.0000000000000003e-222 < y < 2.2e76

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in y around 0

      \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{\color{blue}{1 + a}} \]
   5. Step-by-step derivation

      1. lower-+.f64 58.4

         \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{1 + \color{blue}{a}} \]

   6. Applied rewrites 58.4%

      \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{\color{blue}{1 + a}} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 6: 67.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b}\\ \mathbf{if}\;y \leq -8.7 \cdot 10^{+52}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 3 \cdot 10^{-222}:\\ \;\;\;\;\frac{x}{1 + \left(a + \frac{b \cdot y}{t}\right)}\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{+76}:\\ \;\;\;\;\frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{1 + a}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (fma t (/ x y) z) b)))
   (if (<= y -8.7e+52)
     t_1
     (if (<= y 3e-222)
       (/ x (+ 1.0 (+ a (/ (* b y) t))))
       (if (<= y 2.2e+76) (/ (fma z (/ y t) x) (+ 1.0 a)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = fma(t, (x / y), z) / b;
	double tmp;
	if (y <= -8.7e+52) {
		tmp = t_1;
	} else if (y <= 3e-222) {
		tmp = x / (1.0 + (a + ((b * y) / t)));
	} else if (y <= 2.2e+76) {
		tmp = fma(z, (y / t), x) / (1.0 + a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(fma(t, Float64(x / y), z) / b)
	tmp = 0.0
	if (y <= -8.7e+52)
		tmp = t_1;
	elseif (y <= 3e-222)
		tmp = Float64(x / Float64(1.0 + Float64(a + Float64(Float64(b * y) / t))));
	elseif (y <= 2.2e+76)
		tmp = Float64(fma(z, Float64(y / t), x) / Float64(1.0 + a));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(t * N[(x / y), $MachinePrecision] + z), $MachinePrecision] / b), $MachinePrecision]}, If[LessEqual[y, -8.7e+52], t$95$1, If[LessEqual[y, 3e-222], N[(x / N[(1.0 + N[(a + N[(N[(b * y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.2e+76], N[(N[(z * N[(y / t), $MachinePrecision] + x), $MachinePrecision] / N[(1.0 + a), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y < -8.69999999999999994e52 or 2.2e76 < y

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b} \cdot y} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      6. lower-*.f64 25.7

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

   4. Applied rewrites 25.7%

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

      3. *-commutative N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y \cdot \color{blue}{b}} \]

      4. associate-/r* N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

   6. Applied rewrites 34.1%

      \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{\color{blue}{b}} \]
   7. Step-by-step derivation

      1. associate-*l/ 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      2. *-commutative 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      3. +-commutative 34.1

         \[\leadsto \frac{\frac{\color{blue}{\mathsf{fma}\left(x, t, y \cdot z\right)}}{y}}{b} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{b} \]

      5. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t + y \cdot z}{y}}{b} \]

      6. +-commutative N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      8. *-commutative N/A

         \[\leadsto \frac{\frac{z \cdot y + x \cdot t}{y}}{b} \]

      9. add-to-fraction-rev N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      10. lower-+.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      11. lower-/.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      12. lower-*.f64 40.0

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

   8. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\frac{z + \frac{x \cdot t}{y}}{b}} \]
   9. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      2. +-commutative N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{t \cdot x}{y} + z}{b} \]

      6. associate-/l* N/A

         \[\leadsto \frac{t \cdot \frac{x}{y} + z}{b} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

      8. lower-/.f64 39.8

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   10. Applied rewrites 39.8%

       \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   if -8.69999999999999994e52 < y < 3.0000000000000003e-222

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{x}{1 + \left(a + \frac{b \cdot y}{t}\right)}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{1 + \left(a + \frac{b \cdot y}{t}\right)}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{x}{1 + \color{blue}{\left(a + \frac{b \cdot y}{t}\right)}} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{x}{1 + \left(a + \color{blue}{\frac{b \cdot y}{t}}\right)} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{x}{1 + \left(a + \frac{b \cdot y}{\color{blue}{t}}\right)} \]

      5. lower-*.f64 52.7

         \[\leadsto \frac{x}{1 + \left(a + \frac{b \cdot y}{t}\right)} \]

   4. Applied rewrites 52.7%

      \[\leadsto \color{blue}{\frac{x}{1 + \left(a + \frac{b \cdot y}{t}\right)}} \]

   if 3.0000000000000003e-222 < y < 2.2e76

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in y around 0

      \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{\color{blue}{1 + a}} \]
   5. Step-by-step derivation

      1. lower-+.f64 58.4

         \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{1 + \color{blue}{a}} \]

   6. Applied rewrites 58.4%

      \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{\color{blue}{1 + a}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 7: 66.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b}\\ \mathbf{if}\;y \leq -3.1 \cdot 10^{+53}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{+76}:\\ \;\;\;\;\frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{1 + a}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (fma t (/ x y) z) b)))
   (if (<= y -3.1e+53)
     t_1
     (if (<= y 2.2e+76) (/ (fma z (/ y t) x) (+ 1.0 a)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = fma(t, (x / y), z) / b;
	double tmp;
	if (y <= -3.1e+53) {
		tmp = t_1;
	} else if (y <= 2.2e+76) {
		tmp = fma(z, (y / t), x) / (1.0 + a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(fma(t, Float64(x / y), z) / b)
	tmp = 0.0
	if (y <= -3.1e+53)
		tmp = t_1;
	elseif (y <= 2.2e+76)
		tmp = Float64(fma(z, Float64(y / t), x) / Float64(1.0 + a));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(t * N[(x / y), $MachinePrecision] + z), $MachinePrecision] / b), $MachinePrecision]}, If[LessEqual[y, -3.1e+53], t$95$1, If[LessEqual[y, 2.2e+76], N[(N[(z * N[(y / t), $MachinePrecision] + x), $MachinePrecision] / N[(1.0 + a), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -3.10000000000000019e53 or 2.2e76 < y

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b} \cdot y} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      6. lower-*.f64 25.7

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

   4. Applied rewrites 25.7%

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

      3. *-commutative N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y \cdot \color{blue}{b}} \]

      4. associate-/r* N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

   6. Applied rewrites 34.1%

      \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{\color{blue}{b}} \]
   7. Step-by-step derivation

      1. associate-*l/ 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      2. *-commutative 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      3. +-commutative 34.1

         \[\leadsto \frac{\frac{\color{blue}{\mathsf{fma}\left(x, t, y \cdot z\right)}}{y}}{b} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{b} \]

      5. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t + y \cdot z}{y}}{b} \]

      6. +-commutative N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      8. *-commutative N/A

         \[\leadsto \frac{\frac{z \cdot y + x \cdot t}{y}}{b} \]

      9. add-to-fraction-rev N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      10. lower-+.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      11. lower-/.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      12. lower-*.f64 40.0

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

   8. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\frac{z + \frac{x \cdot t}{y}}{b}} \]
   9. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      2. +-commutative N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{t \cdot x}{y} + z}{b} \]

      6. associate-/l* N/A

         \[\leadsto \frac{t \cdot \frac{x}{y} + z}{b} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

      8. lower-/.f64 39.8

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   10. Applied rewrites 39.8%

       \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   if -3.10000000000000019e53 < y < 2.2e76

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in y around 0

      \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{\color{blue}{1 + a}} \]
   5. Step-by-step derivation

      1. lower-+.f64 58.4

         \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{1 + \color{blue}{a}} \]

   6. Applied rewrites 58.4%

      \[\leadsto \frac{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}{\color{blue}{1 + a}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 58.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b}\\ \mathbf{if}\;y \leq -1.8 \cdot 10^{-100}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 1.1 \cdot 10^{-75}:\\ \;\;\;\;\frac{x}{a - -1}\\ \mathbf{elif}\;y \leq 1.65 \cdot 10^{+59}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\frac{y}{t}, z, x\right)}{a}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (fma t (/ x y) z) b)))
   (if (<= y -1.8e-100)
     t_1
     (if (<= y 1.1e-75)
       (/ x (- a -1.0))
       (if (<= y 1.65e+59) (/ (fma (/ y t) z x) a) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = fma(t, (x / y), z) / b;
	double tmp;
	if (y <= -1.8e-100) {
		tmp = t_1;
	} else if (y <= 1.1e-75) {
		tmp = x / (a - -1.0);
	} else if (y <= 1.65e+59) {
		tmp = fma((y / t), z, x) / a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(fma(t, Float64(x / y), z) / b)
	tmp = 0.0
	if (y <= -1.8e-100)
		tmp = t_1;
	elseif (y <= 1.1e-75)
		tmp = Float64(x / Float64(a - -1.0));
	elseif (y <= 1.65e+59)
		tmp = Float64(fma(Float64(y / t), z, x) / a);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(t * N[(x / y), $MachinePrecision] + z), $MachinePrecision] / b), $MachinePrecision]}, If[LessEqual[y, -1.8e-100], t$95$1, If[LessEqual[y, 1.1e-75], N[(x / N[(a - -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.65e+59], N[(N[(N[(y / t), $MachinePrecision] * z + x), $MachinePrecision] / a), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.7999999999999999e-100 or 1.65e59 < y

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b} \cdot y} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      6. lower-*.f64 25.7

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

   4. Applied rewrites 25.7%

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

      3. *-commutative N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y \cdot \color{blue}{b}} \]

      4. associate-/r* N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

   6. Applied rewrites 34.1%

      \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{\color{blue}{b}} \]
   7. Step-by-step derivation

      1. associate-*l/ 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      2. *-commutative 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      3. +-commutative 34.1

         \[\leadsto \frac{\frac{\color{blue}{\mathsf{fma}\left(x, t, y \cdot z\right)}}{y}}{b} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{b} \]

      5. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t + y \cdot z}{y}}{b} \]

      6. +-commutative N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      8. *-commutative N/A

         \[\leadsto \frac{\frac{z \cdot y + x \cdot t}{y}}{b} \]

      9. add-to-fraction-rev N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      10. lower-+.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      11. lower-/.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      12. lower-*.f64 40.0

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

   8. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\frac{z + \frac{x \cdot t}{y}}{b}} \]
   9. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      2. +-commutative N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{t \cdot x}{y} + z}{b} \]

      6. associate-/l* N/A

         \[\leadsto \frac{t \cdot \frac{x}{y} + z}{b} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

      8. lower-/.f64 39.8

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   10. Applied rewrites 39.8%

       \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   if -1.7999999999999999e-100 < y < 1.10000000000000003e-75

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in y around 0

      \[\leadsto \color{blue}{\frac{x}{1 + a}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{1 + a}} \]

      2. lower-+.f64 42.8

         \[\leadsto \frac{x}{1 + \color{blue}{a}} \]

   6. Applied rewrites 42.8%

      \[\leadsto \color{blue}{\frac{x}{1 + a}} \]
   7. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{x}{1 + \color{blue}{a}} \]

      2. +-commutative N/A

         \[\leadsto \frac{x}{a + \color{blue}{1}} \]

      3. add-flip N/A

         \[\leadsto \frac{x}{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{x}{a - -1} \]

      5. lower--.f64 42.8

         \[\leadsto \frac{x}{a - \color{blue}{-1}} \]

   8. Applied rewrites 42.8%

      \[\leadsto \color{blue}{\frac{x}{a - -1}} \]

   if 1.10000000000000003e-75 < y < 1.65e59

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in a around inf

      \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{a}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{a}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

      4. lower-*.f64 34.2

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

   6. Applied rewrites 34.2%

      \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{a}} \]
   7. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

      4. associate-*l/ N/A

         \[\leadsto \frac{x + \frac{y}{t} \cdot z}{a} \]

      5. *-commutative N/A

         \[\leadsto \frac{x + z \cdot \frac{y}{t}}{a} \]

      6. +-commutative N/A

         \[\leadsto \frac{z \cdot \frac{y}{t} + x}{a} \]

      7. *-commutative N/A

         \[\leadsto \frac{\frac{y}{t} \cdot z + x}{a} \]

      8. mult-flip N/A

         \[\leadsto \frac{\left(y \cdot \frac{1}{t}\right) \cdot z + x}{a} \]

      9. lift-/.f64 N/A

         \[\leadsto \frac{\left(y \cdot \frac{1}{t}\right) \cdot z + x}{a} \]

      10. *-commutative N/A

          \[\leadsto \frac{\left(\frac{1}{t} \cdot y\right) \cdot z + x}{a} \]

      11. lift-*.f64 N/A

          \[\leadsto \frac{\left(\frac{1}{t} \cdot y\right) \cdot z + x}{a} \]

      12. lower-fma.f64 35.3

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{t} \cdot y, z, x\right)}{a} \]

      13. lift-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{t} \cdot y, z, x\right)}{a} \]

      14. *-commutative N/A

          \[\leadsto \frac{\mathsf{fma}\left(y \cdot \frac{1}{t}, z, x\right)}{a} \]

      15. lift-/.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(y \cdot \frac{1}{t}, z, x\right)}{a} \]

      16. mult-flip N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{y}{t}, z, x\right)}{a} \]

      17. lower-/.f64 35.3

          \[\leadsto \frac{\mathsf{fma}\left(\frac{y}{t}, z, x\right)}{a} \]

   8. Applied rewrites 35.3%

      \[\leadsto \frac{\mathsf{fma}\left(\frac{y}{t}, z, x\right)}{\color{blue}{a}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 58.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b}\\ \mathbf{if}\;y \leq -1.8 \cdot 10^{-100}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 1.85 \cdot 10^{+28}:\\ \;\;\;\;\frac{x}{a - -1}\\ \mathbf{elif}\;y \leq 2.15 \cdot 10^{+47}:\\ \;\;\;\;\frac{z}{a} \cdot \frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (fma t (/ x y) z) b)))
   (if (<= y -1.8e-100)
     t_1
     (if (<= y 1.85e+28)
       (/ x (- a -1.0))
       (if (<= y 2.15e+47) (* (/ z a) (/ y t)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = fma(t, (x / y), z) / b;
	double tmp;
	if (y <= -1.8e-100) {
		tmp = t_1;
	} else if (y <= 1.85e+28) {
		tmp = x / (a - -1.0);
	} else if (y <= 2.15e+47) {
		tmp = (z / a) * (y / t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(fma(t, Float64(x / y), z) / b)
	tmp = 0.0
	if (y <= -1.8e-100)
		tmp = t_1;
	elseif (y <= 1.85e+28)
		tmp = Float64(x / Float64(a - -1.0));
	elseif (y <= 2.15e+47)
		tmp = Float64(Float64(z / a) * Float64(y / t));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(t * N[(x / y), $MachinePrecision] + z), $MachinePrecision] / b), $MachinePrecision]}, If[LessEqual[y, -1.8e-100], t$95$1, If[LessEqual[y, 1.85e+28], N[(x / N[(a - -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.15e+47], N[(N[(z / a), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.7999999999999999e-100 or 2.14999999999999997e47 < y

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b} \cdot y} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y} \]

      6. lower-*.f64 25.7

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

   4. Applied rewrites 25.7%

      \[\leadsto \color{blue}{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot y}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{\color{blue}{b \cdot y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{b \cdot \color{blue}{y}} \]

      3. *-commutative N/A

         \[\leadsto \frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y \cdot \color{blue}{b}} \]

      4. associate-/r* N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\frac{t \cdot \left(x + \frac{y \cdot z}{t}\right)}{y}}{\color{blue}{b}} \]

   6. Applied rewrites 34.1%

      \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{\color{blue}{b}} \]
   7. Step-by-step derivation

      1. associate-*l/ 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      2. *-commutative 34.1

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, \color{blue}{t}, y \cdot z\right)}{y}}{b} \]

      3. +-commutative 34.1

         \[\leadsto \frac{\frac{\color{blue}{\mathsf{fma}\left(x, t, y \cdot z\right)}}{y}}{b} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(x, t, y \cdot z\right)}{y}}{b} \]

      5. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t + y \cdot z}{y}}{b} \]

      6. +-commutative N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{\frac{y \cdot z + x \cdot t}{y}}{b} \]

      8. *-commutative N/A

         \[\leadsto \frac{\frac{z \cdot y + x \cdot t}{y}}{b} \]

      9. add-to-fraction-rev N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      10. lower-+.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      11. lower-/.f64 N/A

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      12. lower-*.f64 40.0

          \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

   8. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\frac{z + \frac{x \cdot t}{y}}{b}} \]
   9. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{z + \frac{x \cdot t}{y}}{b} \]

      2. +-commutative N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{x \cdot t}{y} + z}{b} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{t \cdot x}{y} + z}{b} \]

      6. associate-/l* N/A

         \[\leadsto \frac{t \cdot \frac{x}{y} + z}{b} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

      8. lower-/.f64 39.8

         \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   10. Applied rewrites 39.8%

       \[\leadsto \frac{\mathsf{fma}\left(t, \frac{x}{y}, z\right)}{b} \]

   if -1.7999999999999999e-100 < y < 1.85e28

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in y around 0

      \[\leadsto \color{blue}{\frac{x}{1 + a}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{1 + a}} \]

      2. lower-+.f64 42.8

         \[\leadsto \frac{x}{1 + \color{blue}{a}} \]

   6. Applied rewrites 42.8%

      \[\leadsto \color{blue}{\frac{x}{1 + a}} \]
   7. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{x}{1 + \color{blue}{a}} \]

      2. +-commutative N/A

         \[\leadsto \frac{x}{a + \color{blue}{1}} \]

      3. add-flip N/A

         \[\leadsto \frac{x}{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{x}{a - -1} \]

      5. lower--.f64 42.8

         \[\leadsto \frac{x}{a - \color{blue}{-1}} \]

   8. Applied rewrites 42.8%

      \[\leadsto \color{blue}{\frac{x}{a - -1}} \]

   if 1.85e28 < y < 2.14999999999999997e47

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in a around inf

      \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{a}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{a}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

      4. lower-*.f64 34.2

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

   6. Applied rewrites 34.2%

      \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{a}} \]

   7. Taylor expanded in x around 0

      \[\leadsto \frac{y \cdot z}{\color{blue}{a \cdot t}} \]
   8. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{a \cdot \color{blue}{t}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{a \cdot t} \]

      3. lower-*.f64 16.6

         \[\leadsto \frac{y \cdot z}{a \cdot t} \]

   9. Applied rewrites 16.6%

      \[\leadsto \frac{y \cdot z}{\color{blue}{a \cdot t}} \]
   10. Step-by-step derivation

       1. lift-/.f64 N/A

          \[\leadsto \frac{y \cdot z}{a \cdot \color{blue}{t}} \]

       2. lift-*.f64 N/A

          \[\leadsto \frac{y \cdot z}{a \cdot t} \]

       3. *-commutative N/A

          \[\leadsto \frac{z \cdot y}{a \cdot t} \]

       4. lift-*.f64 N/A

          \[\leadsto \frac{z \cdot y}{a \cdot t} \]

       5. times-frac N/A

          \[\leadsto \frac{z}{a} \cdot \frac{y}{\color{blue}{t}} \]

       6. lift-/.f64 N/A

          \[\leadsto \frac{z}{a} \cdot \frac{y}{t} \]

       7. lower-*.f64 N/A

          \[\leadsto \frac{z}{a} \cdot \frac{y}{\color{blue}{t}} \]

       8. lower-/.f64 19.1

          \[\leadsto \frac{z}{a} \cdot \frac{y}{t} \]

   11. Applied rewrites 19.1%

       \[\leadsto \frac{z}{a} \cdot \frac{y}{\color{blue}{t}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 54.0% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.05 \cdot 10^{-100}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;y \leq 1.85 \cdot 10^{+28}:\\ \;\;\;\;\frac{x}{a - -1}\\ \mathbf{elif}\;y \leq 2.6 \cdot 10^{+47}:\\ \;\;\;\;\frac{z}{a} \cdot \frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y -1.05e-100)
   (/ z b)
   (if (<= y 1.85e+28)
     (/ x (- a -1.0))
     (if (<= y 2.6e+47) (* (/ z a) (/ y t)) (/ z b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -1.05e-100) {
		tmp = z / b;
	} else if (y <= 1.85e+28) {
		tmp = x / (a - -1.0);
	} else if (y <= 2.6e+47) {
		tmp = (z / a) * (y / t);
	} else {
		tmp = z / b;
	}
	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)
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) :: tmp
    if (y <= (-1.05d-100)) then
        tmp = z / b
    else if (y <= 1.85d+28) then
        tmp = x / (a - (-1.0d0))
    else if (y <= 2.6d+47) then
        tmp = (z / a) * (y / t)
    else
        tmp = z / b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -1.05e-100) {
		tmp = z / b;
	} else if (y <= 1.85e+28) {
		tmp = x / (a - -1.0);
	} else if (y <= 2.6e+47) {
		tmp = (z / a) * (y / t);
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if y <= -1.05e-100:
		tmp = z / b
	elif y <= 1.85e+28:
		tmp = x / (a - -1.0)
	elif y <= 2.6e+47:
		tmp = (z / a) * (y / t)
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= -1.05e-100)
		tmp = Float64(z / b);
	elseif (y <= 1.85e+28)
		tmp = Float64(x / Float64(a - -1.0));
	elseif (y <= 2.6e+47)
		tmp = Float64(Float64(z / a) * Float64(y / t));
	else
		tmp = Float64(z / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (y <= -1.05e-100)
		tmp = z / b;
	elseif (y <= 1.85e+28)
		tmp = x / (a - -1.0);
	elseif (y <= 2.6e+47)
		tmp = (z / a) * (y / t);
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, -1.05e-100], N[(z / b), $MachinePrecision], If[LessEqual[y, 1.85e+28], N[(x / N[(a - -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.6e+47], N[(N[(z / a), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision], N[(z / b), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.05000000000000005e-100 or 2.60000000000000003e47 < y

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\frac{z}{b}} \]
   3. Step-by-step derivation

      1. lower-/.f64 33.8

         \[\leadsto \frac{z}{\color{blue}{b}} \]

   4. Applied rewrites 33.8%

      \[\leadsto \color{blue}{\frac{z}{b}} \]

   if -1.05000000000000005e-100 < y < 1.85e28

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in y around 0

      \[\leadsto \color{blue}{\frac{x}{1 + a}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{1 + a}} \]

      2. lower-+.f64 42.8

         \[\leadsto \frac{x}{1 + \color{blue}{a}} \]

   6. Applied rewrites 42.8%

      \[\leadsto \color{blue}{\frac{x}{1 + a}} \]
   7. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{x}{1 + \color{blue}{a}} \]

      2. +-commutative N/A

         \[\leadsto \frac{x}{a + \color{blue}{1}} \]

      3. add-flip N/A

         \[\leadsto \frac{x}{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{x}{a - -1} \]

      5. lower--.f64 42.8

         \[\leadsto \frac{x}{a - \color{blue}{-1}} \]

   8. Applied rewrites 42.8%

      \[\leadsto \color{blue}{\frac{x}{a - -1}} \]

   if 1.85e28 < y < 2.60000000000000003e47

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in a around inf

      \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{a}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{a}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

      4. lower-*.f64 34.2

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

   6. Applied rewrites 34.2%

      \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{a}} \]

   7. Taylor expanded in x around 0

      \[\leadsto \frac{y \cdot z}{\color{blue}{a \cdot t}} \]
   8. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{y \cdot z}{a \cdot \color{blue}{t}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{y \cdot z}{a \cdot t} \]

      3. lower-*.f64 16.6

         \[\leadsto \frac{y \cdot z}{a \cdot t} \]

   9. Applied rewrites 16.6%

      \[\leadsto \frac{y \cdot z}{\color{blue}{a \cdot t}} \]
   10. Step-by-step derivation

       1. lift-/.f64 N/A

          \[\leadsto \frac{y \cdot z}{a \cdot \color{blue}{t}} \]

       2. lift-*.f64 N/A

          \[\leadsto \frac{y \cdot z}{a \cdot t} \]

       3. *-commutative N/A

          \[\leadsto \frac{z \cdot y}{a \cdot t} \]

       4. lift-*.f64 N/A

          \[\leadsto \frac{z \cdot y}{a \cdot t} \]

       5. times-frac N/A

          \[\leadsto \frac{z}{a} \cdot \frac{y}{\color{blue}{t}} \]

       6. lift-/.f64 N/A

          \[\leadsto \frac{z}{a} \cdot \frac{y}{t} \]

       7. lower-*.f64 N/A

          \[\leadsto \frac{z}{a} \cdot \frac{y}{\color{blue}{t}} \]

       8. lower-/.f64 19.1

          \[\leadsto \frac{z}{a} \cdot \frac{y}{t} \]

   11. Applied rewrites 19.1%

       \[\leadsto \frac{z}{a} \cdot \frac{y}{\color{blue}{t}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 11: 53.7% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.05 \cdot 10^{-100}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{+59}:\\ \;\;\;\;\frac{x}{a - -1}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y -1.05e-100) (/ z b) (if (<= y 4.4e+59) (/ x (- a -1.0)) (/ z b))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -1.05e-100) {
		tmp = z / b;
	} else if (y <= 4.4e+59) {
		tmp = x / (a - -1.0);
	} else {
		tmp = z / b;
	}
	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)
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) :: tmp
    if (y <= (-1.05d-100)) then
        tmp = z / b
    else if (y <= 4.4d+59) then
        tmp = x / (a - (-1.0d0))
    else
        tmp = z / b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -1.05e-100) {
		tmp = z / b;
	} else if (y <= 4.4e+59) {
		tmp = x / (a - -1.0);
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if y <= -1.05e-100:
		tmp = z / b
	elif y <= 4.4e+59:
		tmp = x / (a - -1.0)
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= -1.05e-100)
		tmp = Float64(z / b);
	elseif (y <= 4.4e+59)
		tmp = Float64(x / Float64(a - -1.0));
	else
		tmp = Float64(z / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (y <= -1.05e-100)
		tmp = z / b;
	elseif (y <= 4.4e+59)
		tmp = x / (a - -1.0);
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, -1.05e-100], N[(z / b), $MachinePrecision], If[LessEqual[y, 4.4e+59], N[(x / N[(a - -1.0), $MachinePrecision]), $MachinePrecision], N[(z / b), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.05000000000000005e-100 or 4.3999999999999999e59 < y

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\frac{z}{b}} \]
   3. Step-by-step derivation

      1. lower-/.f64 33.8

         \[\leadsto \frac{z}{\color{blue}{b}} \]

   4. Applied rewrites 33.8%

      \[\leadsto \color{blue}{\frac{z}{b}} \]

   if -1.05000000000000005e-100 < y < 4.3999999999999999e59

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in y around 0

      \[\leadsto \color{blue}{\frac{x}{1 + a}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{1 + a}} \]

      2. lower-+.f64 42.8

         \[\leadsto \frac{x}{1 + \color{blue}{a}} \]

   6. Applied rewrites 42.8%

      \[\leadsto \color{blue}{\frac{x}{1 + a}} \]
   7. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{x}{1 + \color{blue}{a}} \]

      2. +-commutative N/A

         \[\leadsto \frac{x}{a + \color{blue}{1}} \]

      3. add-flip N/A

         \[\leadsto \frac{x}{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{x}{a - -1} \]

      5. lower--.f64 42.8

         \[\leadsto \frac{x}{a - \color{blue}{-1}} \]

   8. Applied rewrites 42.8%

      \[\leadsto \color{blue}{\frac{x}{a - -1}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 42.1% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.05 \cdot 10^{-100}:\\ \;\;\;\;\frac{z}{b}\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{+59}:\\ \;\;\;\;\frac{x}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y -1.05e-100) (/ z b) (if (<= y 4.4e+59) (/ x a) (/ z b))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -1.05e-100) {
		tmp = z / b;
	} else if (y <= 4.4e+59) {
		tmp = x / a;
	} else {
		tmp = z / b;
	}
	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)
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) :: tmp
    if (y <= (-1.05d-100)) then
        tmp = z / b
    else if (y <= 4.4d+59) then
        tmp = x / a
    else
        tmp = z / b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= -1.05e-100) {
		tmp = z / b;
	} else if (y <= 4.4e+59) {
		tmp = x / a;
	} else {
		tmp = z / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if y <= -1.05e-100:
		tmp = z / b
	elif y <= 4.4e+59:
		tmp = x / a
	else:
		tmp = z / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= -1.05e-100)
		tmp = Float64(z / b);
	elseif (y <= 4.4e+59)
		tmp = Float64(x / a);
	else
		tmp = Float64(z / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (y <= -1.05e-100)
		tmp = z / b;
	elseif (y <= 4.4e+59)
		tmp = x / a;
	else
		tmp = z / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, -1.05e-100], N[(z / b), $MachinePrecision], If[LessEqual[y, 4.4e+59], N[(x / a), $MachinePrecision], N[(z / b), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.05000000000000005e-100 or 4.3999999999999999e59 < y

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\frac{z}{b}} \]
   3. Step-by-step derivation

      1. lower-/.f64 33.8

         \[\leadsto \frac{z}{\color{blue}{b}} \]

   4. Applied rewrites 33.8%

      \[\leadsto \color{blue}{\frac{z}{b}} \]

   if -1.05000000000000005e-100 < y < 4.3999999999999999e59

   1. Initial program 75.0%

      \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\color{blue}{x + \frac{y \cdot z}{t}}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t} + x}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\color{blue}{\frac{y \cdot z}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\color{blue}{y \cdot z}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      5. *-commutative N/A

         \[\leadsto \frac{\frac{\color{blue}{z \cdot y}}{t} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      6. associate-/l* N/A

         \[\leadsto \frac{\color{blue}{z \cdot \frac{y}{t}} + x}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

      8. lower-/.f64 75.1

         \[\leadsto \frac{\mathsf{fma}\left(z, \color{blue}{\frac{y}{t}}, x\right)}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   3. Applied rewrites 75.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(z, \frac{y}{t}, x\right)}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

   4. Taylor expanded in a around inf

      \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{a}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{\color{blue}{a}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

      4. lower-*.f64 34.2

         \[\leadsto \frac{x + \frac{y \cdot z}{t}}{a} \]

   6. Applied rewrites 34.2%

      \[\leadsto \color{blue}{\frac{x + \frac{y \cdot z}{t}}{a}} \]

   7. Taylor expanded in x around inf

      \[\leadsto \frac{x}{\color{blue}{a}} \]
   8. Step-by-step derivation

      1. lower-/.f64 26.6

         \[\leadsto \frac{x}{a} \]

   9. Applied rewrites 26.6%

      \[\leadsto \frac{x}{\color{blue}{a}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 13: 33.8% accurate, 5.8× speedup

Math

\[\begin{array}{l} \\ \frac{z}{b} \end{array} \]

FPCore

(FPCore (x y z t a b) :precision binary64 (/ z b))

C

double code(double x, double y, double z, double t, double a, double b) {
	return z / b;
}

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)
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
    code = z / b
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return z / b;
}

Python

def code(x, y, z, t, a, b):
	return z / b

Julia

function code(x, y, z, t, a, b)
	return Float64(z / b)
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = z / b;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(z / b), $MachinePrecision]

Derivation

1. Initial program 75.0%

   \[\frac{x + \frac{y \cdot z}{t}}{\left(a + 1\right) + \frac{y \cdot b}{t}} \]

2. Taylor expanded in y around inf

   \[\leadsto \color{blue}{\frac{z}{b}} \]
3. Step-by-step derivation

   1. lower-/.f64 33.8

      \[\leadsto \frac{z}{\color{blue}{b}} \]

4. Applied rewrites 33.8%

   \[\leadsto \color{blue}{\frac{z}{b}} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025156 
(FPCore (x y z t a b)
  :name "Diagrams.Solve.Tridiagonal:solveCyclicTriDiagonal from diagrams-solve-0.1, B"
  :precision binary64
  (/ (+ x (/ (* y z) t)) (+ (+ a 1.0) (/ (* y b) t))))