Data.Colour.Matrix:determinant from colour-2.3.3, A

Percentage Accurate: 73.7% → 81.4%

Time: 7.9s

Alternatives: 20

Speedup: 0.5×

• 57.4% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (+
  (- (* x (- (* y z) (* t a))) (* b (- (* c z) (* t i))))
  (* j (- (* c a) (* y i)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	return ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    code = ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	return ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)));
}

Python

def code(x, y, z, t, a, b, c, i, j):
	return ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)))

Julia

function code(x, y, z, t, a, b, c, i, j)
	return Float64(Float64(Float64(x * Float64(Float64(y * z) - Float64(t * a))) - Float64(b * Float64(Float64(c * z) - Float64(t * i)))) + Float64(j * Float64(Float64(c * a) - Float64(y * i))))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j)
	tmp = ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := N[(N[(N[(x * N[(N[(y * z), $MachinePrecision] - N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(b * N[(N[(c * z), $MachinePrecision] - N[(t * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(j * N[(N[(c * a), $MachinePrecision] - N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 73.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (+
  (- (* x (- (* y z) (* t a))) (* b (- (* c z) (* t i))))
  (* j (- (* c a) (* y i)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	return ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    code = ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	return ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)));
}

Python

def code(x, y, z, t, a, b, c, i, j):
	return ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)))

Julia

function code(x, y, z, t, a, b, c, i, j)
	return Float64(Float64(Float64(x * Float64(Float64(y * z) - Float64(t * a))) - Float64(b * Float64(Float64(c * z) - Float64(t * i)))) + Float64(j * Float64(Float64(c * a) - Float64(y * i))))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j)
	tmp = ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := N[(N[(N[(x * N[(N[(y * z), $MachinePrecision] - N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(b * N[(N[(c * z), $MachinePrecision] - N[(t * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(j * N[(N[(c * a), $MachinePrecision] - N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 81.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right)\\ \mathbf{if}\;t\_1 \leq \infty:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;b \cdot \left(i \cdot t - c \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1
         (+
          (- (* x (- (* y z) (* t a))) (* b (- (* c z) (* t i))))
          (* j (- (* c a) (* y i))))))
   (if (<= t_1 INFINITY) t_1 (* b (- (* i t) (* c z))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)));
	double tmp;
	if (t_1 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = b * ((i * t) - (c * z));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)));
	double tmp;
	if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else {
		tmp = b * ((i * t) - (c * z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j):
	t_1 = ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)))
	tmp = 0
	if t_1 <= math.inf:
		tmp = t_1
	else:
		tmp = b * ((i * t) - (c * z))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(Float64(Float64(x * Float64(Float64(y * z) - Float64(t * a))) - Float64(b * Float64(Float64(c * z) - Float64(t * i)))) + Float64(j * Float64(Float64(c * a) - Float64(y * i))))
	tmp = 0.0
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = Float64(b * Float64(Float64(i * t) - Float64(c * z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j)
	t_1 = ((x * ((y * z) - (t * a))) - (b * ((c * z) - (t * i)))) + (j * ((c * a) - (y * i)));
	tmp = 0.0;
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = b * ((i * t) - (c * z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(N[(N[(x * N[(N[(y * z), $MachinePrecision] - N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(b * N[(N[(c * z), $MachinePrecision] - N[(t * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(j * N[(N[(c * a), $MachinePrecision] - N[(y * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, Infinity], t$95$1, N[(b * N[(N[(i * t), $MachinePrecision] - N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (*.f64 x (-.f64 (*.f64 y z) (*.f64 t a))) (*.f64 b (-.f64 (*.f64 c z) (*.f64 t i)))) (*.f64 j (-.f64 (*.f64 c a) (*.f64 y i)))) < +inf.0

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   if +inf.0 < (+.f64 (-.f64 (*.f64 x (-.f64 (*.f64 y z) (*.f64 t a))) (*.f64 b (-.f64 (*.f64 c z) (*.f64 t i)))) (*.f64 j (-.f64 (*.f64 c a) (*.f64 y i))))

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   4. Applied rewrites 38.4%

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 69.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(y \cdot z - a \cdot t\right)\\ t_2 := a \cdot c - i \cdot y\\ \mathbf{if}\;j \leq -4.4 \cdot 10^{+73}:\\ \;\;\;\;\mathsf{fma}\left(j, t\_2, \mathsf{fma}\left(t, b \cdot i, x \cdot \left(y \cdot z\right)\right)\right) - b \cdot \left(c \cdot z\right)\\ \mathbf{elif}\;j \leq 3.5 \cdot 10^{-170}:\\ \;\;\;\;t\_1 - b \cdot \left(c \cdot z - i \cdot t\right)\\ \mathbf{elif}\;j \leq 5.5 \cdot 10^{+178}:\\ \;\;\;\;\mathsf{fma}\left(c, a \cdot j - b \cdot z, t\_1\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(j, t\_2, t\_1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* x (- (* y z) (* a t)))) (t_2 (- (* a c) (* i y))))
   (if (<= j -4.4e+73)
     (- (fma j t_2 (fma t (* b i) (* x (* y z)))) (* b (* c z)))
     (if (<= j 3.5e-170)
       (- t_1 (* b (- (* c z) (* i t))))
       (if (<= j 5.5e+178) (fma c (- (* a j) (* b z)) t_1) (fma j t_2 t_1))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * ((y * z) - (a * t));
	double t_2 = (a * c) - (i * y);
	double tmp;
	if (j <= -4.4e+73) {
		tmp = fma(j, t_2, fma(t, (b * i), (x * (y * z)))) - (b * (c * z));
	} else if (j <= 3.5e-170) {
		tmp = t_1 - (b * ((c * z) - (i * t)));
	} else if (j <= 5.5e+178) {
		tmp = fma(c, ((a * j) - (b * z)), t_1);
	} else {
		tmp = fma(j, t_2, t_1);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(x * Float64(Float64(y * z) - Float64(a * t)))
	t_2 = Float64(Float64(a * c) - Float64(i * y))
	tmp = 0.0
	if (j <= -4.4e+73)
		tmp = Float64(fma(j, t_2, fma(t, Float64(b * i), Float64(x * Float64(y * z)))) - Float64(b * Float64(c * z)));
	elseif (j <= 3.5e-170)
		tmp = Float64(t_1 - Float64(b * Float64(Float64(c * z) - Float64(i * t))));
	elseif (j <= 5.5e+178)
		tmp = fma(c, Float64(Float64(a * j) - Float64(b * z)), t_1);
	else
		tmp = fma(j, t_2, t_1);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a * c), $MachinePrecision] - N[(i * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[j, -4.4e+73], N[(N[(j * t$95$2 + N[(t * N[(b * i), $MachinePrecision] + N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(b * N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 3.5e-170], N[(t$95$1 - N[(b * N[(N[(c * z), $MachinePrecision] - N[(i * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 5.5e+178], N[(c * N[(N[(a * j), $MachinePrecision] - N[(b * z), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision], N[(j * t$95$2 + t$95$1), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if j < -4.4e73

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 65.8%

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

   if -4.4e73 < j < 3.49999999999999985e-170

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 68.9%

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

   5. Taylor expanded in j around 0

      \[\leadsto \color{blue}{x \cdot \left(y \cdot z - a \cdot t\right) - b \cdot \left(c \cdot z - i \cdot t\right)} \]

   7. Applied rewrites 59.1%

      \[\leadsto \color{blue}{x \cdot \left(y \cdot z - a \cdot t\right) - b \cdot \left(c \cdot z - i \cdot t\right)} \]

   if 3.49999999999999985e-170 < j < 5.5000000000000001e178

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in c around 0

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

   7. Applied rewrites 61.0%

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

   if 5.5000000000000001e178 < j

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

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

Alternative 3: 69.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(y \cdot z - a \cdot t\right)\\ t_2 := a \cdot c - i \cdot y\\ \mathbf{if}\;j \leq -5.4 \cdot 10^{+48}:\\ \;\;\;\;\mathsf{fma}\left(j, t\_2, t \cdot \left(-1 \cdot \left(a \cdot x\right) - -1 \cdot \left(b \cdot i\right)\right)\right)\\ \mathbf{elif}\;j \leq 3.5 \cdot 10^{-170}:\\ \;\;\;\;t\_1 - b \cdot \left(c \cdot z - i \cdot t\right)\\ \mathbf{elif}\;j \leq 5.5 \cdot 10^{+178}:\\ \;\;\;\;\mathsf{fma}\left(c, a \cdot j - b \cdot z, t\_1\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(j, t\_2, t\_1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* x (- (* y z) (* a t)))) (t_2 (- (* a c) (* i y))))
   (if (<= j -5.4e+48)
     (fma j t_2 (* t (- (* -1.0 (* a x)) (* -1.0 (* b i)))))
     (if (<= j 3.5e-170)
       (- t_1 (* b (- (* c z) (* i t))))
       (if (<= j 5.5e+178) (fma c (- (* a j) (* b z)) t_1) (fma j t_2 t_1))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * ((y * z) - (a * t));
	double t_2 = (a * c) - (i * y);
	double tmp;
	if (j <= -5.4e+48) {
		tmp = fma(j, t_2, (t * ((-1.0 * (a * x)) - (-1.0 * (b * i)))));
	} else if (j <= 3.5e-170) {
		tmp = t_1 - (b * ((c * z) - (i * t)));
	} else if (j <= 5.5e+178) {
		tmp = fma(c, ((a * j) - (b * z)), t_1);
	} else {
		tmp = fma(j, t_2, t_1);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(x * Float64(Float64(y * z) - Float64(a * t)))
	t_2 = Float64(Float64(a * c) - Float64(i * y))
	tmp = 0.0
	if (j <= -5.4e+48)
		tmp = fma(j, t_2, Float64(t * Float64(Float64(-1.0 * Float64(a * x)) - Float64(-1.0 * Float64(b * i)))));
	elseif (j <= 3.5e-170)
		tmp = Float64(t_1 - Float64(b * Float64(Float64(c * z) - Float64(i * t))));
	elseif (j <= 5.5e+178)
		tmp = fma(c, Float64(Float64(a * j) - Float64(b * z)), t_1);
	else
		tmp = fma(j, t_2, t_1);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a * c), $MachinePrecision] - N[(i * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[j, -5.4e+48], N[(j * t$95$2 + N[(t * N[(N[(-1.0 * N[(a * x), $MachinePrecision]), $MachinePrecision] - N[(-1.0 * N[(b * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 3.5e-170], N[(t$95$1 - N[(b * N[(N[(c * z), $MachinePrecision] - N[(i * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 5.5e+178], N[(c * N[(N[(a * j), $MachinePrecision] - N[(b * z), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision], N[(j * t$95$2 + t$95$1), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if j < -5.40000000000000007e48

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in z around 0

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

   7. Applied rewrites 60.4%

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

   if -5.40000000000000007e48 < j < 3.49999999999999985e-170

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 68.9%

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

   5. Taylor expanded in j around 0

      \[\leadsto \color{blue}{x \cdot \left(y \cdot z - a \cdot t\right) - b \cdot \left(c \cdot z - i \cdot t\right)} \]

   7. Applied rewrites 59.1%

      \[\leadsto \color{blue}{x \cdot \left(y \cdot z - a \cdot t\right) - b \cdot \left(c \cdot z - i \cdot t\right)} \]

   if 3.49999999999999985e-170 < j < 5.5000000000000001e178

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in c around 0

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

   7. Applied rewrites 61.0%

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

   if 5.5000000000000001e178 < j

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

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

Alternative 4: 68.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(y \cdot z - a \cdot t\right)\\ t_2 := a \cdot c - i \cdot y\\ \mathbf{if}\;j \leq -4.4 \cdot 10^{+64}:\\ \;\;\;\;\mathsf{fma}\left(j, t\_2, x \cdot \left(a \cdot \left(\frac{y \cdot z}{a} - t\right)\right)\right)\\ \mathbf{elif}\;j \leq 3.5 \cdot 10^{-170}:\\ \;\;\;\;t\_1 - b \cdot \left(c \cdot z - i \cdot t\right)\\ \mathbf{elif}\;j \leq 5.5 \cdot 10^{+178}:\\ \;\;\;\;\mathsf{fma}\left(c, a \cdot j - b \cdot z, t\_1\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(j, t\_2, t\_1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* x (- (* y z) (* a t)))) (t_2 (- (* a c) (* i y))))
   (if (<= j -4.4e+64)
     (fma j t_2 (* x (* a (- (/ (* y z) a) t))))
     (if (<= j 3.5e-170)
       (- t_1 (* b (- (* c z) (* i t))))
       (if (<= j 5.5e+178) (fma c (- (* a j) (* b z)) t_1) (fma j t_2 t_1))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * ((y * z) - (a * t));
	double t_2 = (a * c) - (i * y);
	double tmp;
	if (j <= -4.4e+64) {
		tmp = fma(j, t_2, (x * (a * (((y * z) / a) - t))));
	} else if (j <= 3.5e-170) {
		tmp = t_1 - (b * ((c * z) - (i * t)));
	} else if (j <= 5.5e+178) {
		tmp = fma(c, ((a * j) - (b * z)), t_1);
	} else {
		tmp = fma(j, t_2, t_1);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(x * Float64(Float64(y * z) - Float64(a * t)))
	t_2 = Float64(Float64(a * c) - Float64(i * y))
	tmp = 0.0
	if (j <= -4.4e+64)
		tmp = fma(j, t_2, Float64(x * Float64(a * Float64(Float64(Float64(y * z) / a) - t))));
	elseif (j <= 3.5e-170)
		tmp = Float64(t_1 - Float64(b * Float64(Float64(c * z) - Float64(i * t))));
	elseif (j <= 5.5e+178)
		tmp = fma(c, Float64(Float64(a * j) - Float64(b * z)), t_1);
	else
		tmp = fma(j, t_2, t_1);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a * c), $MachinePrecision] - N[(i * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[j, -4.4e+64], N[(j * t$95$2 + N[(x * N[(a * N[(N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision] - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 3.5e-170], N[(t$95$1 - N[(b * N[(N[(c * z), $MachinePrecision] - N[(i * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 5.5e+178], N[(c * N[(N[(a * j), $MachinePrecision] - N[(b * z), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision], N[(j * t$95$2 + t$95$1), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if j < -4.40000000000000004e64

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(j, a \cdot c - i \cdot y, x \cdot \left(y \cdot z - a \cdot t\right)\right)} \]

   5. Taylor expanded in a around inf

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

   7. Applied rewrites 59.8%

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

   if -4.40000000000000004e64 < j < 3.49999999999999985e-170

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 68.9%

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

   5. Taylor expanded in j around 0

      \[\leadsto \color{blue}{x \cdot \left(y \cdot z - a \cdot t\right) - b \cdot \left(c \cdot z - i \cdot t\right)} \]

   7. Applied rewrites 59.1%

      \[\leadsto \color{blue}{x \cdot \left(y \cdot z - a \cdot t\right) - b \cdot \left(c \cdot z - i \cdot t\right)} \]

   if 3.49999999999999985e-170 < j < 5.5000000000000001e178

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in c around 0

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

   7. Applied rewrites 61.0%

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

   if 5.5000000000000001e178 < j

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

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

Alternative 5: 68.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(y \cdot z - a \cdot t\right)\\ t_2 := \mathsf{fma}\left(j, a \cdot c - i \cdot y, t\_1\right)\\ \mathbf{if}\;j \leq -4.3 \cdot 10^{+64}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;j \leq 3.5 \cdot 10^{-170}:\\ \;\;\;\;t\_1 - b \cdot \left(c \cdot z - i \cdot t\right)\\ \mathbf{elif}\;j \leq 5.5 \cdot 10^{+178}:\\ \;\;\;\;\mathsf{fma}\left(c, a \cdot j - b \cdot z, t\_1\right)\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* x (- (* y z) (* a t)))) (t_2 (fma j (- (* a c) (* i y)) t_1)))
   (if (<= j -4.3e+64)
     t_2
     (if (<= j 3.5e-170)
       (- t_1 (* b (- (* c z) (* i t))))
       (if (<= j 5.5e+178) (fma c (- (* a j) (* b z)) t_1) t_2)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * ((y * z) - (a * t));
	double t_2 = fma(j, ((a * c) - (i * y)), t_1);
	double tmp;
	if (j <= -4.3e+64) {
		tmp = t_2;
	} else if (j <= 3.5e-170) {
		tmp = t_1 - (b * ((c * z) - (i * t)));
	} else if (j <= 5.5e+178) {
		tmp = fma(c, ((a * j) - (b * z)), t_1);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(x * Float64(Float64(y * z) - Float64(a * t)))
	t_2 = fma(j, Float64(Float64(a * c) - Float64(i * y)), t_1)
	tmp = 0.0
	if (j <= -4.3e+64)
		tmp = t_2;
	elseif (j <= 3.5e-170)
		tmp = Float64(t_1 - Float64(b * Float64(Float64(c * z) - Float64(i * t))));
	elseif (j <= 5.5e+178)
		tmp = fma(c, Float64(Float64(a * j) - Float64(b * z)), t_1);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(j * N[(N[(a * c), $MachinePrecision] - N[(i * y), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]}, If[LessEqual[j, -4.3e+64], t$95$2, If[LessEqual[j, 3.5e-170], N[(t$95$1 - N[(b * N[(N[(c * z), $MachinePrecision] - N[(i * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 5.5e+178], N[(c * N[(N[(a * j), $MachinePrecision] - N[(b * z), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if j < -4.2999999999999998e64 or 5.5000000000000001e178 < j

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(j, a \cdot c - i \cdot y, x \cdot \left(y \cdot z - a \cdot t\right)\right)} \]

   if -4.2999999999999998e64 < j < 3.49999999999999985e-170

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 68.9%

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

   5. Taylor expanded in j around 0

      \[\leadsto \color{blue}{x \cdot \left(y \cdot z - a \cdot t\right) - b \cdot \left(c \cdot z - i \cdot t\right)} \]

   7. Applied rewrites 59.1%

      \[\leadsto \color{blue}{x \cdot \left(y \cdot z - a \cdot t\right) - b \cdot \left(c \cdot z - i \cdot t\right)} \]

   if 3.49999999999999985e-170 < j < 5.5000000000000001e178

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in c around 0

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

   7. Applied rewrites 61.0%

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

Alternative 6: 67.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := i \cdot \mathsf{fma}\left(-1, j \cdot y, b \cdot t\right)\\ \mathbf{if}\;i \leq -8.2 \cdot 10^{+132}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;i \leq 5.6 \cdot 10^{+134}:\\ \;\;\;\;\mathsf{fma}\left(c, a \cdot j - b \cdot z, x \cdot \left(y \cdot z - a \cdot t\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* i (fma -1.0 (* j y) (* b t)))))
   (if (<= i -8.2e+132)
     t_1
     (if (<= i 5.6e+134)
       (fma c (- (* a j) (* b z)) (* x (- (* y z) (* a t))))
       t_1))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = i * fma(-1.0, (j * y), (b * t));
	double tmp;
	if (i <= -8.2e+132) {
		tmp = t_1;
	} else if (i <= 5.6e+134) {
		tmp = fma(c, ((a * j) - (b * z)), (x * ((y * z) - (a * t))));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(i * fma(-1.0, Float64(j * y), Float64(b * t)))
	tmp = 0.0
	if (i <= -8.2e+132)
		tmp = t_1;
	elseif (i <= 5.6e+134)
		tmp = fma(c, Float64(Float64(a * j) - Float64(b * z)), Float64(x * Float64(Float64(y * z) - Float64(a * t))));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(i * N[(-1.0 * N[(j * y), $MachinePrecision] + N[(b * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[i, -8.2e+132], t$95$1, If[LessEqual[i, 5.6e+134], N[(c * N[(N[(a * j), $MachinePrecision] - N[(b * z), $MachinePrecision]), $MachinePrecision] + N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if i < -8.19999999999999983e132 or 5.5999999999999997e134 < i

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in i around inf

      \[\leadsto i \cdot \color{blue}{\left(-1 \cdot \left(j \cdot y\right) + b \cdot t\right)} \]

   7. Applied rewrites 38.4%

      \[\leadsto i \cdot \color{blue}{\mathsf{fma}\left(-1, j \cdot y, b \cdot t\right)} \]

   if -8.19999999999999983e132 < i < 5.5999999999999997e134

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in c around 0

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

   7. Applied rewrites 61.0%

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

Alternative 7: 59.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := i \cdot \mathsf{fma}\left(-1, j \cdot y, b \cdot t\right)\\ \mathbf{if}\;i \leq -9.5 \cdot 10^{+146}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;i \leq 4.4 \cdot 10^{+101}:\\ \;\;\;\;x \cdot \left(y \cdot z - a \cdot t\right) - b \cdot \left(c \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* i (fma -1.0 (* j y) (* b t)))))
   (if (<= i -9.5e+146)
     t_1
     (if (<= i 4.4e+101) (- (* x (- (* y z) (* a t))) (* b (* c z))) t_1))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = i * fma(-1.0, (j * y), (b * t));
	double tmp;
	if (i <= -9.5e+146) {
		tmp = t_1;
	} else if (i <= 4.4e+101) {
		tmp = (x * ((y * z) - (a * t))) - (b * (c * z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(i * fma(-1.0, Float64(j * y), Float64(b * t)))
	tmp = 0.0
	if (i <= -9.5e+146)
		tmp = t_1;
	elseif (i <= 4.4e+101)
		tmp = Float64(Float64(x * Float64(Float64(y * z) - Float64(a * t))) - Float64(b * Float64(c * z)));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(i * N[(-1.0 * N[(j * y), $MachinePrecision] + N[(b * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[i, -9.5e+146], t$95$1, If[LessEqual[i, 4.4e+101], N[(N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(b * N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if i < -9.49999999999999926e146 or 4.4000000000000001e101 < i

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in i around inf

      \[\leadsto i \cdot \color{blue}{\left(-1 \cdot \left(j \cdot y\right) + b \cdot t\right)} \]

   7. Applied rewrites 38.4%

      \[\leadsto i \cdot \color{blue}{\mathsf{fma}\left(-1, j \cdot y, b \cdot t\right)} \]

   if -9.49999999999999926e146 < i < 4.4000000000000001e101

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in j around 0

      \[\leadsto x \cdot \left(y \cdot z - a \cdot t\right) - \color{blue}{b \cdot \left(c \cdot z\right)} \]

   7. Applied rewrites 49.8%

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

Alternative 8: 55.7% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(i \cdot t - c \cdot z\right)\\ \mathbf{if}\;b \leq -3 \cdot 10^{-153}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;b \leq 1.2 \cdot 10^{+77}:\\ \;\;\;\;\mathsf{fma}\left(j, a \cdot c, x \cdot \left(y \cdot z - a \cdot t\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* b (- (* i t) (* c z)))))
   (if (<= b -3e-153)
     t_1
     (if (<= b 1.2e+77) (fma j (* a c) (* x (- (* y z) (* a t)))) t_1))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = b * ((i * t) - (c * z));
	double tmp;
	if (b <= -3e-153) {
		tmp = t_1;
	} else if (b <= 1.2e+77) {
		tmp = fma(j, (a * c), (x * ((y * z) - (a * t))));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(b * Float64(Float64(i * t) - Float64(c * z)))
	tmp = 0.0
	if (b <= -3e-153)
		tmp = t_1;
	elseif (b <= 1.2e+77)
		tmp = fma(j, Float64(a * c), Float64(x * Float64(Float64(y * z) - Float64(a * t))));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(b * N[(N[(i * t), $MachinePrecision] - N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -3e-153], t$95$1, If[LessEqual[b, 1.2e+77], N[(j * N[(a * c), $MachinePrecision] + N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if b < -3e-153 or 1.1999999999999999e77 < b

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   4. Applied rewrites 38.4%

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   if -3e-153 < b < 1.1999999999999999e77

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(j, a \cdot c - i \cdot y, x \cdot \left(y \cdot z - a \cdot t\right)\right)} \]

   5. Taylor expanded in y around 0

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

   7. Applied rewrites 51.0%

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

Alternative 9: 52.3% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2 \cdot 10^{+52}:\\ \;\;\;\;x \cdot \left(a \cdot \mathsf{fma}\left(-1, t, \frac{y \cdot z}{a}\right)\right)\\ \mathbf{elif}\;x \leq 3.5 \cdot 10^{-241}:\\ \;\;\;\;c \cdot \left(a \cdot j - b \cdot z\right)\\ \mathbf{elif}\;x \leq 1.5 \cdot 10^{-93}:\\ \;\;\;\;b \cdot \left(i \cdot t - c \cdot z\right)\\ \mathbf{elif}\;x \leq 3.5 \cdot 10^{+102}:\\ \;\;\;\;i \cdot \mathsf{fma}\left(-1, j \cdot y, b \cdot t\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y \cdot z - a \cdot t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (if (<= x -2e+52)
   (* x (* a (fma -1.0 t (/ (* y z) a))))
   (if (<= x 3.5e-241)
     (* c (- (* a j) (* b z)))
     (if (<= x 1.5e-93)
       (* b (- (* i t) (* c z)))
       (if (<= x 3.5e+102)
         (* i (fma -1.0 (* j y) (* b t)))
         (* x (- (* y z) (* a t))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double tmp;
	if (x <= -2e+52) {
		tmp = x * (a * fma(-1.0, t, ((y * z) / a)));
	} else if (x <= 3.5e-241) {
		tmp = c * ((a * j) - (b * z));
	} else if (x <= 1.5e-93) {
		tmp = b * ((i * t) - (c * z));
	} else if (x <= 3.5e+102) {
		tmp = i * fma(-1.0, (j * y), (b * t));
	} else {
		tmp = x * ((y * z) - (a * t));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j)
	tmp = 0.0
	if (x <= -2e+52)
		tmp = Float64(x * Float64(a * fma(-1.0, t, Float64(Float64(y * z) / a))));
	elseif (x <= 3.5e-241)
		tmp = Float64(c * Float64(Float64(a * j) - Float64(b * z)));
	elseif (x <= 1.5e-93)
		tmp = Float64(b * Float64(Float64(i * t) - Float64(c * z)));
	elseif (x <= 3.5e+102)
		tmp = Float64(i * fma(-1.0, Float64(j * y), Float64(b * t)));
	else
		tmp = Float64(x * Float64(Float64(y * z) - Float64(a * t)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := If[LessEqual[x, -2e+52], N[(x * N[(a * N[(-1.0 * t + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.5e-241], N[(c * N[(N[(a * j), $MachinePrecision] - N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.5e-93], N[(b * N[(N[(i * t), $MachinePrecision] - N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.5e+102], N[(i * N[(-1.0 * N[(j * y), $MachinePrecision] + N[(b * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if x < -2e52

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 39.4%

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

   8. Taylor expanded in a around inf

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

   10. Applied rewrites 39.7%

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

   if -2e52 < x < 3.4999999999999999e-241

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(a \cdot j - b \cdot z\right)} \]

   4. Applied rewrites 39.9%

      \[\leadsto \color{blue}{c \cdot \left(a \cdot j - b \cdot z\right)} \]

   if 3.4999999999999999e-241 < x < 1.5000000000000001e-93

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   4. Applied rewrites 38.4%

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   if 1.5000000000000001e-93 < x < 3.50000000000000011e102

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in i around inf

      \[\leadsto i \cdot \color{blue}{\left(-1 \cdot \left(j \cdot y\right) + b \cdot t\right)} \]

   7. Applied rewrites 38.4%

      \[\leadsto i \cdot \color{blue}{\mathsf{fma}\left(-1, j \cdot y, b \cdot t\right)} \]

   if 3.50000000000000011e102 < x

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 39.4%

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

   9. Applied rewrites 39.4%

      \[\leadsto x \cdot \color{blue}{\left(y \cdot z - a \cdot t\right)} \]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 10: 51.7% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(y \cdot z - a \cdot t\right)\\ \mathbf{if}\;x \leq -2 \cdot 10^{+52}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 3.5 \cdot 10^{-241}:\\ \;\;\;\;c \cdot \left(a \cdot j - b \cdot z\right)\\ \mathbf{elif}\;x \leq 1.5 \cdot 10^{-93}:\\ \;\;\;\;b \cdot \left(i \cdot t - c \cdot z\right)\\ \mathbf{elif}\;x \leq 3.5 \cdot 10^{+102}:\\ \;\;\;\;i \cdot \mathsf{fma}\left(-1, j \cdot y, b \cdot t\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* x (- (* y z) (* a t)))))
   (if (<= x -2e+52)
     t_1
     (if (<= x 3.5e-241)
       (* c (- (* a j) (* b z)))
       (if (<= x 1.5e-93)
         (* b (- (* i t) (* c z)))
         (if (<= x 3.5e+102) (* i (fma -1.0 (* j y) (* b t))) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * ((y * z) - (a * t));
	double tmp;
	if (x <= -2e+52) {
		tmp = t_1;
	} else if (x <= 3.5e-241) {
		tmp = c * ((a * j) - (b * z));
	} else if (x <= 1.5e-93) {
		tmp = b * ((i * t) - (c * z));
	} else if (x <= 3.5e+102) {
		tmp = i * fma(-1.0, (j * y), (b * t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(x * Float64(Float64(y * z) - Float64(a * t)))
	tmp = 0.0
	if (x <= -2e+52)
		tmp = t_1;
	elseif (x <= 3.5e-241)
		tmp = Float64(c * Float64(Float64(a * j) - Float64(b * z)));
	elseif (x <= 1.5e-93)
		tmp = Float64(b * Float64(Float64(i * t) - Float64(c * z)));
	elseif (x <= 3.5e+102)
		tmp = Float64(i * fma(-1.0, Float64(j * y), Float64(b * t)));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2e+52], t$95$1, If[LessEqual[x, 3.5e-241], N[(c * N[(N[(a * j), $MachinePrecision] - N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.5e-93], N[(b * N[(N[(i * t), $MachinePrecision] - N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.5e+102], N[(i * N[(-1.0 * N[(j * y), $MachinePrecision] + N[(b * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -2e52 or 3.50000000000000011e102 < x

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 39.4%

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

   9. Applied rewrites 39.4%

      \[\leadsto x \cdot \color{blue}{\left(y \cdot z - a \cdot t\right)} \]

   if -2e52 < x < 3.4999999999999999e-241

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(a \cdot j - b \cdot z\right)} \]

   4. Applied rewrites 39.9%

      \[\leadsto \color{blue}{c \cdot \left(a \cdot j - b \cdot z\right)} \]

   if 3.4999999999999999e-241 < x < 1.5000000000000001e-93

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   4. Applied rewrites 38.4%

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   if 1.5000000000000001e-93 < x < 3.50000000000000011e102

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in i around inf

      \[\leadsto i \cdot \color{blue}{\left(-1 \cdot \left(j \cdot y\right) + b \cdot t\right)} \]

   7. Applied rewrites 38.4%

      \[\leadsto i \cdot \color{blue}{\mathsf{fma}\left(-1, j \cdot y, b \cdot t\right)} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 11: 51.6% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(y \cdot z - a \cdot t\right)\\ \mathbf{if}\;x \leq -2 \cdot 10^{+52}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 3.5 \cdot 10^{-241}:\\ \;\;\;\;c \cdot \left(a \cdot j - b \cdot z\right)\\ \mathbf{elif}\;x \leq 3 \cdot 10^{+102}:\\ \;\;\;\;b \cdot \left(i \cdot t - c \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* x (- (* y z) (* a t)))))
   (if (<= x -2e+52)
     t_1
     (if (<= x 3.5e-241)
       (* c (- (* a j) (* b z)))
       (if (<= x 3e+102) (* b (- (* i t) (* c z))) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * ((y * z) - (a * t));
	double tmp;
	if (x <= -2e+52) {
		tmp = t_1;
	} else if (x <= 3.5e-241) {
		tmp = c * ((a * j) - (b * z));
	} else if (x <= 3e+102) {
		tmp = b * ((i * t) - (c * z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * ((y * z) - (a * t))
    if (x <= (-2d+52)) then
        tmp = t_1
    else if (x <= 3.5d-241) then
        tmp = c * ((a * j) - (b * z))
    else if (x <= 3d+102) then
        tmp = b * ((i * t) - (c * z))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * ((y * z) - (a * t));
	double tmp;
	if (x <= -2e+52) {
		tmp = t_1;
	} else if (x <= 3.5e-241) {
		tmp = c * ((a * j) - (b * z));
	} else if (x <= 3e+102) {
		tmp = b * ((i * t) - (c * z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j):
	t_1 = x * ((y * z) - (a * t))
	tmp = 0
	if x <= -2e+52:
		tmp = t_1
	elif x <= 3.5e-241:
		tmp = c * ((a * j) - (b * z))
	elif x <= 3e+102:
		tmp = b * ((i * t) - (c * z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(x * Float64(Float64(y * z) - Float64(a * t)))
	tmp = 0.0
	if (x <= -2e+52)
		tmp = t_1;
	elseif (x <= 3.5e-241)
		tmp = Float64(c * Float64(Float64(a * j) - Float64(b * z)));
	elseif (x <= 3e+102)
		tmp = Float64(b * Float64(Float64(i * t) - Float64(c * z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j)
	t_1 = x * ((y * z) - (a * t));
	tmp = 0.0;
	if (x <= -2e+52)
		tmp = t_1;
	elseif (x <= 3.5e-241)
		tmp = c * ((a * j) - (b * z));
	elseif (x <= 3e+102)
		tmp = b * ((i * t) - (c * z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2e+52], t$95$1, If[LessEqual[x, 3.5e-241], N[(c * N[(N[(a * j), $MachinePrecision] - N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3e+102], N[(b * N[(N[(i * t), $MachinePrecision] - N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if x < -2e52 or 2.9999999999999998e102 < x

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 39.4%

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

   9. Applied rewrites 39.4%

      \[\leadsto x \cdot \color{blue}{\left(y \cdot z - a \cdot t\right)} \]

   if -2e52 < x < 3.4999999999999999e-241

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(a \cdot j - b \cdot z\right)} \]

   4. Applied rewrites 39.9%

      \[\leadsto \color{blue}{c \cdot \left(a \cdot j - b \cdot z\right)} \]

   if 3.4999999999999999e-241 < x < 2.9999999999999998e102

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   4. Applied rewrites 38.4%

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 12: 51.5% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(y \cdot z - a \cdot t\right)\\ \mathbf{if}\;x \leq -1.6 \cdot 10^{-5}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 3 \cdot 10^{+102}:\\ \;\;\;\;b \cdot \left(i \cdot t - c \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* x (- (* y z) (* a t)))))
   (if (<= x -1.6e-5) t_1 (if (<= x 3e+102) (* b (- (* i t) (* c z))) t_1))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * ((y * z) - (a * t));
	double tmp;
	if (x <= -1.6e-5) {
		tmp = t_1;
	} else if (x <= 3e+102) {
		tmp = b * ((i * t) - (c * z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * ((y * z) - (a * t))
    if (x <= (-1.6d-5)) then
        tmp = t_1
    else if (x <= 3d+102) then
        tmp = b * ((i * t) - (c * z))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * ((y * z) - (a * t));
	double tmp;
	if (x <= -1.6e-5) {
		tmp = t_1;
	} else if (x <= 3e+102) {
		tmp = b * ((i * t) - (c * z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j):
	t_1 = x * ((y * z) - (a * t))
	tmp = 0
	if x <= -1.6e-5:
		tmp = t_1
	elif x <= 3e+102:
		tmp = b * ((i * t) - (c * z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(x * Float64(Float64(y * z) - Float64(a * t)))
	tmp = 0.0
	if (x <= -1.6e-5)
		tmp = t_1;
	elseif (x <= 3e+102)
		tmp = Float64(b * Float64(Float64(i * t) - Float64(c * z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j)
	t_1 = x * ((y * z) - (a * t));
	tmp = 0.0;
	if (x <= -1.6e-5)
		tmp = t_1;
	elseif (x <= 3e+102)
		tmp = b * ((i * t) - (c * z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.6e-5], t$95$1, If[LessEqual[x, 3e+102], N[(b * N[(N[(i * t), $MachinePrecision] - N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.59999999999999993e-5 or 2.9999999999999998e102 < x

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 39.4%

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

   9. Applied rewrites 39.4%

      \[\leadsto x \cdot \color{blue}{\left(y \cdot z - a \cdot t\right)} \]

   if -1.59999999999999993e-5 < x < 2.9999999999999998e102

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   4. Applied rewrites 38.4%

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 13: 50.7% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(y \cdot z - a \cdot t\right)\\ \mathbf{if}\;x \leq -3.2 \cdot 10^{+22}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 7 \cdot 10^{+23}:\\ \;\;\;\;j \cdot \left(a \cdot c - i \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* x (- (* y z) (* a t)))))
   (if (<= x -3.2e+22) t_1 (if (<= x 7e+23) (* j (- (* a c) (* i y))) t_1))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * ((y * z) - (a * t));
	double tmp;
	if (x <= -3.2e+22) {
		tmp = t_1;
	} else if (x <= 7e+23) {
		tmp = j * ((a * c) - (i * y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * ((y * z) - (a * t))
    if (x <= (-3.2d+22)) then
        tmp = t_1
    else if (x <= 7d+23) then
        tmp = j * ((a * c) - (i * y))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * ((y * z) - (a * t));
	double tmp;
	if (x <= -3.2e+22) {
		tmp = t_1;
	} else if (x <= 7e+23) {
		tmp = j * ((a * c) - (i * y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j):
	t_1 = x * ((y * z) - (a * t))
	tmp = 0
	if x <= -3.2e+22:
		tmp = t_1
	elif x <= 7e+23:
		tmp = j * ((a * c) - (i * y))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(x * Float64(Float64(y * z) - Float64(a * t)))
	tmp = 0.0
	if (x <= -3.2e+22)
		tmp = t_1;
	elseif (x <= 7e+23)
		tmp = Float64(j * Float64(Float64(a * c) - Float64(i * y)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j)
	t_1 = x * ((y * z) - (a * t));
	tmp = 0.0;
	if (x <= -3.2e+22)
		tmp = t_1;
	elseif (x <= 7e+23)
		tmp = j * ((a * c) - (i * y));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(x * N[(N[(y * z), $MachinePrecision] - N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -3.2e+22], t$95$1, If[LessEqual[x, 7e+23], N[(j * N[(N[(a * c), $MachinePrecision] - N[(i * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -3.2e22 or 7.0000000000000004e23 < x

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 74.2%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 39.4%

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

   9. Applied rewrites 39.4%

      \[\leadsto x \cdot \color{blue}{\left(y \cdot z - a \cdot t\right)} \]

   if -3.2e22 < x < 7.0000000000000004e23

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(j, a \cdot c - i \cdot y, x \cdot \left(y \cdot z - a \cdot t\right)\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

   7. Applied rewrites 39.6%

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 41.1% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(-1 \cdot \left(t \cdot x\right)\right)\\ \mathbf{if}\;x \leq -6.3 \cdot 10^{+34}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 4.3 \cdot 10^{+111}:\\ \;\;\;\;j \cdot \left(a \cdot c - i \cdot y\right)\\ \mathbf{elif}\;x \leq 1.5 \cdot 10^{+234}:\\ \;\;\;\;x \cdot \left(y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* a (* -1.0 (* t x)))))
   (if (<= x -6.3e+34)
     t_1
     (if (<= x 4.3e+111)
       (* j (- (* a c) (* i y)))
       (if (<= x 1.5e+234) (* x (* y z)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = a * (-1.0 * (t * x));
	double tmp;
	if (x <= -6.3e+34) {
		tmp = t_1;
	} else if (x <= 4.3e+111) {
		tmp = j * ((a * c) - (i * y));
	} else if (x <= 1.5e+234) {
		tmp = x * (y * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a * ((-1.0d0) * (t * x))
    if (x <= (-6.3d+34)) then
        tmp = t_1
    else if (x <= 4.3d+111) then
        tmp = j * ((a * c) - (i * y))
    else if (x <= 1.5d+234) then
        tmp = x * (y * z)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = a * (-1.0 * (t * x));
	double tmp;
	if (x <= -6.3e+34) {
		tmp = t_1;
	} else if (x <= 4.3e+111) {
		tmp = j * ((a * c) - (i * y));
	} else if (x <= 1.5e+234) {
		tmp = x * (y * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j):
	t_1 = a * (-1.0 * (t * x))
	tmp = 0
	if x <= -6.3e+34:
		tmp = t_1
	elif x <= 4.3e+111:
		tmp = j * ((a * c) - (i * y))
	elif x <= 1.5e+234:
		tmp = x * (y * z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(a * Float64(-1.0 * Float64(t * x)))
	tmp = 0.0
	if (x <= -6.3e+34)
		tmp = t_1;
	elseif (x <= 4.3e+111)
		tmp = Float64(j * Float64(Float64(a * c) - Float64(i * y)));
	elseif (x <= 1.5e+234)
		tmp = Float64(x * Float64(y * z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j)
	t_1 = a * (-1.0 * (t * x));
	tmp = 0.0;
	if (x <= -6.3e+34)
		tmp = t_1;
	elseif (x <= 4.3e+111)
		tmp = j * ((a * c) - (i * y));
	elseif (x <= 1.5e+234)
		tmp = x * (y * z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(a * N[(-1.0 * N[(t * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -6.3e+34], t$95$1, If[LessEqual[x, 4.3e+111], N[(j * N[(N[(a * c), $MachinePrecision] - N[(i * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.5e+234], N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if x < -6.3000000000000001e34 or 1.4999999999999999e234 < x

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \left(-1 \cdot \left(t \cdot x\right) + c \cdot j\right)} \]

   4. Applied rewrites 38.6%

      \[\leadsto \color{blue}{a \cdot \mathsf{fma}\left(-1, t \cdot x, c \cdot j\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto a \cdot \left(-1 \cdot \color{blue}{\left(t \cdot x\right)}\right) \]

   7. Applied rewrites 21.6%

      \[\leadsto a \cdot \left(-1 \cdot \color{blue}{\left(t \cdot x\right)}\right) \]

   if -6.3000000000000001e34 < x < 4.29999999999999993e111

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(j, a \cdot c - i \cdot y, x \cdot \left(y \cdot z - a \cdot t\right)\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

   7. Applied rewrites 39.6%

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

   if 4.29999999999999993e111 < x < 1.4999999999999999e234

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in y around inf

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

   7. Applied rewrites 22.8%

      \[\leadsto x \cdot \color{blue}{\left(y \cdot z\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 15: 30.2% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(-1 \cdot \left(t \cdot x\right)\right)\\ \mathbf{if}\;x \leq -1.7 \cdot 10^{+33}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 7.2 \cdot 10^{-245}:\\ \;\;\;\;b \cdot \left(-1 \cdot \left(c \cdot z\right)\right)\\ \mathbf{elif}\;x \leq 9.5 \cdot 10^{+43}:\\ \;\;\;\;b \cdot \left(i \cdot t\right)\\ \mathbf{elif}\;x \leq 1.5 \cdot 10^{+234}:\\ \;\;\;\;x \cdot \left(y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* a (* -1.0 (* t x)))))
   (if (<= x -1.7e+33)
     t_1
     (if (<= x 7.2e-245)
       (* b (* -1.0 (* c z)))
       (if (<= x 9.5e+43)
         (* b (* i t))
         (if (<= x 1.5e+234) (* x (* y z)) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = a * (-1.0 * (t * x));
	double tmp;
	if (x <= -1.7e+33) {
		tmp = t_1;
	} else if (x <= 7.2e-245) {
		tmp = b * (-1.0 * (c * z));
	} else if (x <= 9.5e+43) {
		tmp = b * (i * t);
	} else if (x <= 1.5e+234) {
		tmp = x * (y * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a * ((-1.0d0) * (t * x))
    if (x <= (-1.7d+33)) then
        tmp = t_1
    else if (x <= 7.2d-245) then
        tmp = b * ((-1.0d0) * (c * z))
    else if (x <= 9.5d+43) then
        tmp = b * (i * t)
    else if (x <= 1.5d+234) then
        tmp = x * (y * z)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = a * (-1.0 * (t * x));
	double tmp;
	if (x <= -1.7e+33) {
		tmp = t_1;
	} else if (x <= 7.2e-245) {
		tmp = b * (-1.0 * (c * z));
	} else if (x <= 9.5e+43) {
		tmp = b * (i * t);
	} else if (x <= 1.5e+234) {
		tmp = x * (y * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j):
	t_1 = a * (-1.0 * (t * x))
	tmp = 0
	if x <= -1.7e+33:
		tmp = t_1
	elif x <= 7.2e-245:
		tmp = b * (-1.0 * (c * z))
	elif x <= 9.5e+43:
		tmp = b * (i * t)
	elif x <= 1.5e+234:
		tmp = x * (y * z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(a * Float64(-1.0 * Float64(t * x)))
	tmp = 0.0
	if (x <= -1.7e+33)
		tmp = t_1;
	elseif (x <= 7.2e-245)
		tmp = Float64(b * Float64(-1.0 * Float64(c * z)));
	elseif (x <= 9.5e+43)
		tmp = Float64(b * Float64(i * t));
	elseif (x <= 1.5e+234)
		tmp = Float64(x * Float64(y * z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j)
	t_1 = a * (-1.0 * (t * x));
	tmp = 0.0;
	if (x <= -1.7e+33)
		tmp = t_1;
	elseif (x <= 7.2e-245)
		tmp = b * (-1.0 * (c * z));
	elseif (x <= 9.5e+43)
		tmp = b * (i * t);
	elseif (x <= 1.5e+234)
		tmp = x * (y * z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(a * N[(-1.0 * N[(t * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.7e+33], t$95$1, If[LessEqual[x, 7.2e-245], N[(b * N[(-1.0 * N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 9.5e+43], N[(b * N[(i * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.5e+234], N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -1.7e33 or 1.4999999999999999e234 < x

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \left(-1 \cdot \left(t \cdot x\right) + c \cdot j\right)} \]

   4. Applied rewrites 38.6%

      \[\leadsto \color{blue}{a \cdot \mathsf{fma}\left(-1, t \cdot x, c \cdot j\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto a \cdot \left(-1 \cdot \color{blue}{\left(t \cdot x\right)}\right) \]

   7. Applied rewrites 21.6%

      \[\leadsto a \cdot \left(-1 \cdot \color{blue}{\left(t \cdot x\right)}\right) \]

   if -1.7e33 < x < 7.19999999999999999e-245

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   4. Applied rewrites 38.4%

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   5. Taylor expanded in z around inf

      \[\leadsto b \cdot \left(-1 \cdot \color{blue}{\left(c \cdot z\right)}\right) \]

   7. Applied rewrites 22.7%

      \[\leadsto b \cdot \left(-1 \cdot \color{blue}{\left(c \cdot z\right)}\right) \]

   if 7.19999999999999999e-245 < x < 9.5000000000000004e43

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   4. Applied rewrites 38.4%

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   5. Taylor expanded in z around 0

      \[\leadsto b \cdot \left(i \cdot \color{blue}{t}\right) \]

   7. Applied rewrites 21.2%

      \[\leadsto b \cdot \left(i \cdot \color{blue}{t}\right) \]

   if 9.5000000000000004e43 < x < 1.4999999999999999e234

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in y around inf

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

   7. Applied rewrites 22.8%

      \[\leadsto x \cdot \color{blue}{\left(y \cdot z\right)} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 16: 29.3% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(-1 \cdot \left(t \cdot x\right)\right)\\ \mathbf{if}\;x \leq -9.8 \cdot 10^{+31}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 7.5 \cdot 10^{-241}:\\ \;\;\;\;c \cdot \left(a \cdot j\right)\\ \mathbf{elif}\;x \leq 9.5 \cdot 10^{+43}:\\ \;\;\;\;b \cdot \left(i \cdot t\right)\\ \mathbf{elif}\;x \leq 1.5 \cdot 10^{+234}:\\ \;\;\;\;x \cdot \left(y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* a (* -1.0 (* t x)))))
   (if (<= x -9.8e+31)
     t_1
     (if (<= x 7.5e-241)
       (* c (* a j))
       (if (<= x 9.5e+43)
         (* b (* i t))
         (if (<= x 1.5e+234) (* x (* y z)) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = a * (-1.0 * (t * x));
	double tmp;
	if (x <= -9.8e+31) {
		tmp = t_1;
	} else if (x <= 7.5e-241) {
		tmp = c * (a * j);
	} else if (x <= 9.5e+43) {
		tmp = b * (i * t);
	} else if (x <= 1.5e+234) {
		tmp = x * (y * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a * ((-1.0d0) * (t * x))
    if (x <= (-9.8d+31)) then
        tmp = t_1
    else if (x <= 7.5d-241) then
        tmp = c * (a * j)
    else if (x <= 9.5d+43) then
        tmp = b * (i * t)
    else if (x <= 1.5d+234) then
        tmp = x * (y * z)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = a * (-1.0 * (t * x));
	double tmp;
	if (x <= -9.8e+31) {
		tmp = t_1;
	} else if (x <= 7.5e-241) {
		tmp = c * (a * j);
	} else if (x <= 9.5e+43) {
		tmp = b * (i * t);
	} else if (x <= 1.5e+234) {
		tmp = x * (y * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j):
	t_1 = a * (-1.0 * (t * x))
	tmp = 0
	if x <= -9.8e+31:
		tmp = t_1
	elif x <= 7.5e-241:
		tmp = c * (a * j)
	elif x <= 9.5e+43:
		tmp = b * (i * t)
	elif x <= 1.5e+234:
		tmp = x * (y * z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(a * Float64(-1.0 * Float64(t * x)))
	tmp = 0.0
	if (x <= -9.8e+31)
		tmp = t_1;
	elseif (x <= 7.5e-241)
		tmp = Float64(c * Float64(a * j));
	elseif (x <= 9.5e+43)
		tmp = Float64(b * Float64(i * t));
	elseif (x <= 1.5e+234)
		tmp = Float64(x * Float64(y * z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j)
	t_1 = a * (-1.0 * (t * x));
	tmp = 0.0;
	if (x <= -9.8e+31)
		tmp = t_1;
	elseif (x <= 7.5e-241)
		tmp = c * (a * j);
	elseif (x <= 9.5e+43)
		tmp = b * (i * t);
	elseif (x <= 1.5e+234)
		tmp = x * (y * z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(a * N[(-1.0 * N[(t * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -9.8e+31], t$95$1, If[LessEqual[x, 7.5e-241], N[(c * N[(a * j), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 9.5e+43], N[(b * N[(i * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.5e+234], N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -9.79999999999999991e31 or 1.4999999999999999e234 < x

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in a around inf

      \[\leadsto \color{blue}{a \cdot \left(-1 \cdot \left(t \cdot x\right) + c \cdot j\right)} \]

   4. Applied rewrites 38.6%

      \[\leadsto \color{blue}{a \cdot \mathsf{fma}\left(-1, t \cdot x, c \cdot j\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto a \cdot \left(-1 \cdot \color{blue}{\left(t \cdot x\right)}\right) \]

   7. Applied rewrites 21.6%

      \[\leadsto a \cdot \left(-1 \cdot \color{blue}{\left(t \cdot x\right)}\right) \]

   if -9.79999999999999991e31 < x < 7.49999999999999977e-241

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(a \cdot j - b \cdot z\right)} \]

   4. Applied rewrites 39.9%

      \[\leadsto \color{blue}{c \cdot \left(a \cdot j - b \cdot z\right)} \]

   5. Taylor expanded in z around 0

      \[\leadsto c \cdot \left(a \cdot \color{blue}{j}\right) \]

   7. Applied rewrites 22.7%

      \[\leadsto c \cdot \left(a \cdot \color{blue}{j}\right) \]

   if 7.49999999999999977e-241 < x < 9.5000000000000004e43

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   4. Applied rewrites 38.4%

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   5. Taylor expanded in z around 0

      \[\leadsto b \cdot \left(i \cdot \color{blue}{t}\right) \]

   7. Applied rewrites 21.2%

      \[\leadsto b \cdot \left(i \cdot \color{blue}{t}\right) \]

   if 9.5000000000000004e43 < x < 1.4999999999999999e234

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in y around inf

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

   7. Applied rewrites 22.8%

      \[\leadsto x \cdot \color{blue}{\left(y \cdot z\right)} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 17: 29.1% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(y \cdot z\right)\\ \mathbf{if}\;y \leq -1.4 \cdot 10^{+93}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{-121}:\\ \;\;\;\;j \cdot \left(a \cdot c\right)\\ \mathbf{elif}\;y \leq 6.2 \cdot 10^{-202}:\\ \;\;\;\;b \cdot \left(i \cdot t\right)\\ \mathbf{elif}\;y \leq 7.5 \cdot 10^{+128}:\\ \;\;\;\;j \cdot \left(-1 \cdot \left(i \cdot y\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* x (* y z))))
   (if (<= y -1.4e+93)
     t_1
     (if (<= y -1.15e-121)
       (* j (* a c))
       (if (<= y 6.2e-202)
         (* b (* i t))
         (if (<= y 7.5e+128) (* j (* -1.0 (* i y))) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * (y * z);
	double tmp;
	if (y <= -1.4e+93) {
		tmp = t_1;
	} else if (y <= -1.15e-121) {
		tmp = j * (a * c);
	} else if (y <= 6.2e-202) {
		tmp = b * (i * t);
	} else if (y <= 7.5e+128) {
		tmp = j * (-1.0 * (i * y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * (y * z)
    if (y <= (-1.4d+93)) then
        tmp = t_1
    else if (y <= (-1.15d-121)) then
        tmp = j * (a * c)
    else if (y <= 6.2d-202) then
        tmp = b * (i * t)
    else if (y <= 7.5d+128) then
        tmp = j * ((-1.0d0) * (i * y))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * (y * z);
	double tmp;
	if (y <= -1.4e+93) {
		tmp = t_1;
	} else if (y <= -1.15e-121) {
		tmp = j * (a * c);
	} else if (y <= 6.2e-202) {
		tmp = b * (i * t);
	} else if (y <= 7.5e+128) {
		tmp = j * (-1.0 * (i * y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j):
	t_1 = x * (y * z)
	tmp = 0
	if y <= -1.4e+93:
		tmp = t_1
	elif y <= -1.15e-121:
		tmp = j * (a * c)
	elif y <= 6.2e-202:
		tmp = b * (i * t)
	elif y <= 7.5e+128:
		tmp = j * (-1.0 * (i * y))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(x * Float64(y * z))
	tmp = 0.0
	if (y <= -1.4e+93)
		tmp = t_1;
	elseif (y <= -1.15e-121)
		tmp = Float64(j * Float64(a * c));
	elseif (y <= 6.2e-202)
		tmp = Float64(b * Float64(i * t));
	elseif (y <= 7.5e+128)
		tmp = Float64(j * Float64(-1.0 * Float64(i * y)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j)
	t_1 = x * (y * z);
	tmp = 0.0;
	if (y <= -1.4e+93)
		tmp = t_1;
	elseif (y <= -1.15e-121)
		tmp = j * (a * c);
	elseif (y <= 6.2e-202)
		tmp = b * (i * t);
	elseif (y <= 7.5e+128)
		tmp = j * (-1.0 * (i * y));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.4e+93], t$95$1, If[LessEqual[y, -1.15e-121], N[(j * N[(a * c), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6.2e-202], N[(b * N[(i * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7.5e+128], N[(j * N[(-1.0 * N[(i * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -1.39999999999999994e93 or 7.50000000000000076e128 < y

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in y around inf

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

   7. Applied rewrites 22.8%

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

   if -1.39999999999999994e93 < y < -1.15000000000000006e-121

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(j, a \cdot c - i \cdot y, x \cdot \left(y \cdot z - a \cdot t\right)\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

   7. Applied rewrites 39.6%

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

   8. Taylor expanded in y around 0

      \[\leadsto j \cdot \left(a \cdot c\right) \]

   10. Applied rewrites 22.6%

       \[\leadsto j \cdot \left(a \cdot c\right) \]

   if -1.15000000000000006e-121 < y < 6.2e-202

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   4. Applied rewrites 38.4%

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   5. Taylor expanded in z around 0

      \[\leadsto b \cdot \left(i \cdot \color{blue}{t}\right) \]

   7. Applied rewrites 21.2%

      \[\leadsto b \cdot \left(i \cdot \color{blue}{t}\right) \]

   if 6.2e-202 < y < 7.50000000000000076e128

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(j, a \cdot c - i \cdot y, x \cdot \left(y \cdot z - a \cdot t\right)\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

   7. Applied rewrites 39.6%

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

   8. Taylor expanded in y around inf

      \[\leadsto j \cdot \left(-1 \cdot \left(i \cdot \color{blue}{y}\right)\right) \]

   10. Applied rewrites 22.7%

       \[\leadsto j \cdot \left(-1 \cdot \left(i \cdot \color{blue}{y}\right)\right) \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 18: 29.1% accurate, 2.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(y \cdot z\right)\\ \mathbf{if}\;y \leq -1.4 \cdot 10^{+93}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{-121}:\\ \;\;\;\;j \cdot \left(a \cdot c\right)\\ \mathbf{elif}\;y \leq 3.5 \cdot 10^{+54}:\\ \;\;\;\;b \cdot \left(i \cdot t\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* x (* y z))))
   (if (<= y -1.4e+93)
     t_1
     (if (<= y -1.15e-121)
       (* j (* a c))
       (if (<= y 3.5e+54) (* b (* i t)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * (y * z);
	double tmp;
	if (y <= -1.4e+93) {
		tmp = t_1;
	} else if (y <= -1.15e-121) {
		tmp = j * (a * c);
	} else if (y <= 3.5e+54) {
		tmp = b * (i * t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x * (y * z)
    if (y <= (-1.4d+93)) then
        tmp = t_1
    else if (y <= (-1.15d-121)) then
        tmp = j * (a * c)
    else if (y <= 3.5d+54) then
        tmp = b * (i * t)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = x * (y * z);
	double tmp;
	if (y <= -1.4e+93) {
		tmp = t_1;
	} else if (y <= -1.15e-121) {
		tmp = j * (a * c);
	} else if (y <= 3.5e+54) {
		tmp = b * (i * t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j):
	t_1 = x * (y * z)
	tmp = 0
	if y <= -1.4e+93:
		tmp = t_1
	elif y <= -1.15e-121:
		tmp = j * (a * c)
	elif y <= 3.5e+54:
		tmp = b * (i * t)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(x * Float64(y * z))
	tmp = 0.0
	if (y <= -1.4e+93)
		tmp = t_1;
	elseif (y <= -1.15e-121)
		tmp = Float64(j * Float64(a * c));
	elseif (y <= 3.5e+54)
		tmp = Float64(b * Float64(i * t));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j)
	t_1 = x * (y * z);
	tmp = 0.0;
	if (y <= -1.4e+93)
		tmp = t_1;
	elseif (y <= -1.15e-121)
		tmp = j * (a * c);
	elseif (y <= 3.5e+54)
		tmp = b * (i * t);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.4e+93], t$95$1, If[LessEqual[y, -1.15e-121], N[(j * N[(a * c), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3.5e+54], N[(b * N[(i * t), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.39999999999999994e93 or 3.5000000000000001e54 < y

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in y around inf

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

   7. Applied rewrites 22.8%

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

   if -1.39999999999999994e93 < y < -1.15000000000000006e-121

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(j, a \cdot c - i \cdot y, x \cdot \left(y \cdot z - a \cdot t\right)\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

   7. Applied rewrites 39.6%

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

   8. Taylor expanded in y around 0

      \[\leadsto j \cdot \left(a \cdot c\right) \]

   10. Applied rewrites 22.6%

       \[\leadsto j \cdot \left(a \cdot c\right) \]

   if -1.15000000000000006e-121 < y < 3.5000000000000001e54

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   4. Applied rewrites 38.4%

      \[\leadsto \color{blue}{b \cdot \left(i \cdot t - c \cdot z\right)} \]

   5. Taylor expanded in z around 0

      \[\leadsto b \cdot \left(i \cdot \color{blue}{t}\right) \]

   7. Applied rewrites 21.2%

      \[\leadsto b \cdot \left(i \cdot \color{blue}{t}\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 19: 27.4% accurate, 2.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := j \cdot \left(a \cdot c\right)\\ \mathbf{if}\;c \leq -1.25 \cdot 10^{+26}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;c \leq 1750:\\ \;\;\;\;x \cdot \left(y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j)
 :precision binary64
 (let* ((t_1 (* j (* a c))))
   (if (<= c -1.25e+26) t_1 (if (<= c 1750.0) (* x (* y z)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = j * (a * c);
	double tmp;
	if (c <= -1.25e+26) {
		tmp = t_1;
	} else if (c <= 1750.0) {
		tmp = x * (y * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8) :: t_1
    real(8) :: tmp
    t_1 = j * (a * c)
    if (c <= (-1.25d+26)) then
        tmp = t_1
    else if (c <= 1750.0d0) then
        tmp = x * (y * z)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	double t_1 = j * (a * c);
	double tmp;
	if (c <= -1.25e+26) {
		tmp = t_1;
	} else if (c <= 1750.0) {
		tmp = x * (y * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j):
	t_1 = j * (a * c)
	tmp = 0
	if c <= -1.25e+26:
		tmp = t_1
	elif c <= 1750.0:
		tmp = x * (y * z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j)
	t_1 = Float64(j * Float64(a * c))
	tmp = 0.0
	if (c <= -1.25e+26)
		tmp = t_1;
	elseif (c <= 1750.0)
		tmp = Float64(x * Float64(y * z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j)
	t_1 = j * (a * c);
	tmp = 0.0;
	if (c <= -1.25e+26)
		tmp = t_1;
	elseif (c <= 1750.0)
		tmp = x * (y * z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := Block[{t$95$1 = N[(j * N[(a * c), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -1.25e+26], t$95$1, If[LessEqual[c, 1750.0], N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if c < -1.25e26 or 1750 < c

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

   4. Applied rewrites 61.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(j, a \cdot c - i \cdot y, x \cdot \left(y \cdot z - a \cdot t\right)\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

   7. Applied rewrites 39.6%

      \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

   8. Taylor expanded in y around 0

      \[\leadsto j \cdot \left(a \cdot c\right) \]

   10. Applied rewrites 22.6%

       \[\leadsto j \cdot \left(a \cdot c\right) \]

   if -1.25e26 < c < 1750

   1. Initial program 73.7%

      \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

   2. Taylor expanded in i around 0

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

   4. Applied rewrites 58.7%

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

   5. Taylor expanded in y around inf

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

   7. Applied rewrites 22.8%

      \[\leadsto x \cdot \color{blue}{\left(y \cdot z\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 20: 22.6% accurate, 5.9× speedup

Math

\[\begin{array}{l} \\ j \cdot \left(a \cdot c\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j) :precision binary64 (* j (* a c)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	return j * (a * c);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c, i, j)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    code = j * (a * c)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j) {
	return j * (a * c);
}

Python

def code(x, y, z, t, a, b, c, i, j):
	return j * (a * c)

Julia

function code(x, y, z, t, a, b, c, i, j)
	return Float64(j * Float64(a * c))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j)
	tmp = j * (a * c);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_] := N[(j * N[(a * c), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.7%

   \[\left(x \cdot \left(y \cdot z - t \cdot a\right) - b \cdot \left(c \cdot z - t \cdot i\right)\right) + j \cdot \left(c \cdot a - y \cdot i\right) \]

2. Taylor expanded in b around 0

   \[\leadsto \color{blue}{j \cdot \left(a \cdot c - i \cdot y\right) + x \cdot \left(y \cdot z - a \cdot t\right)} \]

4. Applied rewrites 61.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(j, a \cdot c - i \cdot y, x \cdot \left(y \cdot z - a \cdot t\right)\right)} \]

5. Taylor expanded in x around 0

   \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

7. Applied rewrites 39.6%

   \[\leadsto j \cdot \color{blue}{\left(a \cdot c - i \cdot y\right)} \]

8. Taylor expanded in y around 0

   \[\leadsto j \cdot \left(a \cdot c\right) \]

10. Applied rewrites 22.6%

    \[\leadsto j \cdot \left(a \cdot c\right) \]
11. Add Preprocessing

Reproduce

herbie shell --seed 2025161 
(FPCore (x y z t a b c i j)
  :name "Data.Colour.Matrix:determinant from colour-2.3.3, A"
  :precision binary64
  (+ (- (* x (- (* y z) (* t a))) (* b (- (* c z) (* t i)))) (* j (- (* c a) (* y i)))))