Data.Colour.Matrix:inverse from colour-2.3.3, B

Percentage Accurate: 98.2% → 98.5%

Time: 22.8s

Alternatives: 7

Speedup: 1.0×

• 25.1% of points produce a very large (infinite) output. You may want to add a precondition.

• could not determine a ground truth

  1. x = 4.4615969743612705e-208
  2. y = 9.639480171657537e+39
  3. z = 1.3895418240950962e-170
  4. t = 5.693610098935886e-286
  5. a = 1.5675405107948216e+153

Specification

Math

\[\begin{array}{l} \\ \frac{x \cdot y - z \cdot t}{a} \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (/ (- (* x y) (* z t)) a))

C

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

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = ((x * y) - (z * t)) / a
end function

Java

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

Python

def code(x, y, z, t, a):
	return ((x * y) - (z * t)) / a

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(x * y) - Float64(z * t)) / a)
end

MATLAB

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

Wolfram

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

Initial Program: 98.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x \cdot y - z \cdot t}{a} \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (/ (- (* x y) (* z t)) a))

C

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

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = ((x * y) - (z * t)) / a
end function

Java

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

Python

def code(x, y, z, t, a):
	return ((x * y) - (z * t)) / a

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(x * y) - Float64(z * t)) / a)
end

MATLAB

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

Wolfram

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

Alternative 1: 98.5% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \frac{\mathsf{fma}\left(y, x, t \cdot \left(-z\right)\right)}{a} \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (/ (fma y x (* t (- z))) a))

C

double code(double x, double y, double z, double t, double a) {
	return fma(y, x, (t * -z)) / a;
}

Julia

function code(x, y, z, t, a)
	return Float64(fma(y, x, Float64(t * Float64(-z))) / a)
end

Wolfram

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

Derivation

1. Initial program 97.9%

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

2. Taylor expanded in x around 0 94.0%

   \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot z}{a} + \frac{y \cdot x}{a}} \]
3. Step-by-step derivation

   1. +-commutative 94.0%

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

   2. mul-1-neg 94.0%

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

   3. sub-neg 94.0%

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

   4. div-sub 97.9%

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

   5. fma-neg 98.3%

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

   6. distribute-rgt-neg-out 98.3%

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

4. Simplified 98.3%

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

5. Final simplification 98.3%

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

Alternative 2: 66.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(-z\right) \cdot \frac{t}{a}\\ \mathbf{if}\;y \leq -1.35 \cdot 10^{-101}:\\ \;\;\;\;y \cdot \frac{x}{a}\\ \mathbf{elif}\;y \leq 1.45 \cdot 10^{+44}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.05 \cdot 10^{+87}:\\ \;\;\;\;\frac{y \cdot x}{a}\\ \mathbf{elif}\;y \leq 9.6 \cdot 10^{+101}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{a}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* (- z) (/ t a))))
   (if (<= y -1.35e-101)
     (* y (/ x a))
     (if (<= y 1.45e+44)
       t_1
       (if (<= y 2.05e+87)
         (/ (* y x) a)
         (if (<= y 9.6e+101) t_1 (/ y (/ a x))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = -z * (t / a);
	double tmp;
	if (y <= -1.35e-101) {
		tmp = y * (x / a);
	} else if (y <= 1.45e+44) {
		tmp = t_1;
	} else if (y <= 2.05e+87) {
		tmp = (y * x) / a;
	} else if (y <= 9.6e+101) {
		tmp = t_1;
	} else {
		tmp = y / (a / x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = -z * (t / a)
    if (y <= (-1.35d-101)) then
        tmp = y * (x / a)
    else if (y <= 1.45d+44) then
        tmp = t_1
    else if (y <= 2.05d+87) then
        tmp = (y * x) / a
    else if (y <= 9.6d+101) then
        tmp = t_1
    else
        tmp = y / (a / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = -z * (t / a);
	double tmp;
	if (y <= -1.35e-101) {
		tmp = y * (x / a);
	} else if (y <= 1.45e+44) {
		tmp = t_1;
	} else if (y <= 2.05e+87) {
		tmp = (y * x) / a;
	} else if (y <= 9.6e+101) {
		tmp = t_1;
	} else {
		tmp = y / (a / x);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = -z * (t / a)
	tmp = 0
	if y <= -1.35e-101:
		tmp = y * (x / a)
	elif y <= 1.45e+44:
		tmp = t_1
	elif y <= 2.05e+87:
		tmp = (y * x) / a
	elif y <= 9.6e+101:
		tmp = t_1
	else:
		tmp = y / (a / x)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(-z) * Float64(t / a))
	tmp = 0.0
	if (y <= -1.35e-101)
		tmp = Float64(y * Float64(x / a));
	elseif (y <= 1.45e+44)
		tmp = t_1;
	elseif (y <= 2.05e+87)
		tmp = Float64(Float64(y * x) / a);
	elseif (y <= 9.6e+101)
		tmp = t_1;
	else
		tmp = Float64(y / Float64(a / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = -z * (t / a);
	tmp = 0.0;
	if (y <= -1.35e-101)
		tmp = y * (x / a);
	elseif (y <= 1.45e+44)
		tmp = t_1;
	elseif (y <= 2.05e+87)
		tmp = (y * x) / a;
	elseif (y <= 9.6e+101)
		tmp = t_1;
	else
		tmp = y / (a / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[((-z) * N[(t / a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.35e-101], N[(y * N[(x / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.45e+44], t$95$1, If[LessEqual[y, 2.05e+87], N[(N[(y * x), $MachinePrecision] / a), $MachinePrecision], If[LessEqual[y, 9.6e+101], t$95$1, N[(y / N[(a / x), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -1.3500000000000001e-101

   1. Initial program 97.6%

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

   2. Taylor expanded in x around inf 73.0%

      \[\leadsto \color{blue}{\frac{y \cdot x}{a}} \]
   3. Step-by-step derivation

      1. associate-*r/ 72.0%

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

   4. Simplified 72.0%

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

   if -1.3500000000000001e-101 < y < 1.4500000000000001e44 or 2.05e87 < y < 9.59999999999999953e101

   1. Initial program 97.1%

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

   2. Taylor expanded in x around 0 94.4%

      \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot z}{a} + \frac{y \cdot x}{a}} \]
   3. Step-by-step derivation

      1. +-commutative 94.4%

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

      2. mul-1-neg 94.4%

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

      3. sub-neg 94.4%

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

      4. div-sub 97.1%

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

      5. fma-neg 97.1%

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

      6. distribute-rgt-neg-out 97.1%

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

   4. Simplified 97.1%

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

   5. Taylor expanded in y around 0 72.4%

      \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot z}{a}} \]
   6. Step-by-step derivation

      1. mul-1-neg 72.4%

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

      2. associate-*l/ 70.8%

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

      3. distribute-rgt-neg-in 70.8%

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

   7. Simplified 70.8%

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

   if 1.4500000000000001e44 < y < 2.05e87

   1. Initial program 100.0%

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

   2. Taylor expanded in x around inf 66.9%

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

   if 9.59999999999999953e101 < y

   1. Initial program 99.8%

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

   2. Taylor expanded in x around inf 85.0%

      \[\leadsto \color{blue}{\frac{y \cdot x}{a}} \]
   3. Step-by-step derivation

      1. associate-/l* 85.1%

         \[\leadsto \color{blue}{\frac{y}{\frac{a}{x}}} \]

   4. Simplified 85.1%

      \[\leadsto \color{blue}{\frac{y}{\frac{a}{x}}} \]
3. Recombined 4 regimes into one program.

4. Final simplification 73.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.35 \cdot 10^{-101}:\\ \;\;\;\;y \cdot \frac{x}{a}\\ \mathbf{elif}\;y \leq 1.45 \cdot 10^{+44}:\\ \;\;\;\;\left(-z\right) \cdot \frac{t}{a}\\ \mathbf{elif}\;y \leq 2.05 \cdot 10^{+87}:\\ \;\;\;\;\frac{y \cdot x}{a}\\ \mathbf{elif}\;y \leq 9.6 \cdot 10^{+101}:\\ \;\;\;\;\left(-z\right) \cdot \frac{t}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{a}{x}}\\ \end{array} \]

Alternative 3: 66.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.4 \cdot 10^{-101}:\\ \;\;\;\;y \cdot \frac{x}{a}\\ \mathbf{elif}\;y \leq 1.95 \cdot 10^{+44}:\\ \;\;\;\;\left(-z\right) \cdot \frac{t}{a}\\ \mathbf{elif}\;y \leq 1.95 \cdot 10^{+87}:\\ \;\;\;\;\frac{y \cdot x}{a}\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+101}:\\ \;\;\;\;t \cdot \frac{-z}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{a}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= y -1.4e-101)
   (* y (/ x a))
   (if (<= y 1.95e+44)
     (* (- z) (/ t a))
     (if (<= y 1.95e+87)
       (/ (* y x) a)
       (if (<= y 1.9e+101) (* t (/ (- z) a)) (/ y (/ a x)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -1.4e-101) {
		tmp = y * (x / a);
	} else if (y <= 1.95e+44) {
		tmp = -z * (t / a);
	} else if (y <= 1.95e+87) {
		tmp = (y * x) / a;
	} else if (y <= 1.9e+101) {
		tmp = t * (-z / a);
	} else {
		tmp = y / (a / x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if (y <= (-1.4d-101)) then
        tmp = y * (x / a)
    else if (y <= 1.95d+44) then
        tmp = -z * (t / a)
    else if (y <= 1.95d+87) then
        tmp = (y * x) / a
    else if (y <= 1.9d+101) then
        tmp = t * (-z / a)
    else
        tmp = y / (a / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -1.4e-101) {
		tmp = y * (x / a);
	} else if (y <= 1.95e+44) {
		tmp = -z * (t / a);
	} else if (y <= 1.95e+87) {
		tmp = (y * x) / a;
	} else if (y <= 1.9e+101) {
		tmp = t * (-z / a);
	} else {
		tmp = y / (a / x);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if y <= -1.4e-101:
		tmp = y * (x / a)
	elif y <= 1.95e+44:
		tmp = -z * (t / a)
	elif y <= 1.95e+87:
		tmp = (y * x) / a
	elif y <= 1.9e+101:
		tmp = t * (-z / a)
	else:
		tmp = y / (a / x)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (y <= -1.4e-101)
		tmp = Float64(y * Float64(x / a));
	elseif (y <= 1.95e+44)
		tmp = Float64(Float64(-z) * Float64(t / a));
	elseif (y <= 1.95e+87)
		tmp = Float64(Float64(y * x) / a);
	elseif (y <= 1.9e+101)
		tmp = Float64(t * Float64(Float64(-z) / a));
	else
		tmp = Float64(y / Float64(a / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (y <= -1.4e-101)
		tmp = y * (x / a);
	elseif (y <= 1.95e+44)
		tmp = -z * (t / a);
	elseif (y <= 1.95e+87)
		tmp = (y * x) / a;
	elseif (y <= 1.9e+101)
		tmp = t * (-z / a);
	else
		tmp = y / (a / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[y, -1.4e-101], N[(y * N[(x / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.95e+44], N[((-z) * N[(t / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.95e+87], N[(N[(y * x), $MachinePrecision] / a), $MachinePrecision], If[LessEqual[y, 1.9e+101], N[(t * N[((-z) / a), $MachinePrecision]), $MachinePrecision], N[(y / N[(a / x), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if y < -1.39999999999999995e-101

   1. Initial program 97.6%

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

   2. Taylor expanded in x around inf 73.0%

      \[\leadsto \color{blue}{\frac{y \cdot x}{a}} \]
   3. Step-by-step derivation

      1. associate-*r/ 72.0%

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

   4. Simplified 72.0%

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

   if -1.39999999999999995e-101 < y < 1.9500000000000001e44

   1. Initial program 97.0%

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

   2. Taylor expanded in x around 0 95.1%

      \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot z}{a} + \frac{y \cdot x}{a}} \]
   3. Step-by-step derivation

      1. +-commutative 95.1%

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

      2. mul-1-neg 95.1%

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

      3. sub-neg 95.1%

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

      4. div-sub 97.0%

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

      5. fma-neg 97.0%

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

      6. distribute-rgt-neg-out 97.0%

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

   4. Simplified 97.0%

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

   5. Taylor expanded in y around 0 72.3%

      \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot z}{a}} \]
   6. Step-by-step derivation

      1. mul-1-neg 72.3%

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

      2. associate-*l/ 70.6%

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

      3. distribute-rgt-neg-in 70.6%

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

   7. Simplified 70.6%

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

   if 1.9500000000000001e44 < y < 1.9500000000000001e87

   1. Initial program 100.0%

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

   2. Taylor expanded in x around inf 66.9%

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

   if 1.9500000000000001e87 < y < 1.8999999999999999e101

   1. Initial program 99.6%

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

   2. Taylor expanded in x around 0 75.5%

      \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot z}{a}} \]
   3. Step-by-step derivation

      1. associate-*r/ 75.5%

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

      2. mul-1-neg 75.5%

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

      3. distribute-rgt-neg-out 75.5%

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

      4. *-commutative 75.5%

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

      5. associate-/l* 75.4%

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

      6. associate-/r/ 75.4%

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

   4. Simplified 75.4%

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

   if 1.8999999999999999e101 < y

   1. Initial program 99.8%

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

   2. Taylor expanded in x around inf 85.0%

      \[\leadsto \color{blue}{\frac{y \cdot x}{a}} \]
   3. Step-by-step derivation

      1. associate-/l* 85.1%

         \[\leadsto \color{blue}{\frac{y}{\frac{a}{x}}} \]

   4. Simplified 85.1%

      \[\leadsto \color{blue}{\frac{y}{\frac{a}{x}}} \]
3. Recombined 5 regimes into one program.

4. Final simplification 73.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.4 \cdot 10^{-101}:\\ \;\;\;\;y \cdot \frac{x}{a}\\ \mathbf{elif}\;y \leq 1.95 \cdot 10^{+44}:\\ \;\;\;\;\left(-z\right) \cdot \frac{t}{a}\\ \mathbf{elif}\;y \leq 1.95 \cdot 10^{+87}:\\ \;\;\;\;\frac{y \cdot x}{a}\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+101}:\\ \;\;\;\;t \cdot \frac{-z}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{a}{x}}\\ \end{array} \]

Alternative 4: 66.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.8 \cdot 10^{-109}:\\ \;\;\;\;y \cdot \frac{x}{a}\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+44}:\\ \;\;\;\;\frac{z}{-\frac{a}{t}}\\ \mathbf{elif}\;y \leq 2.05 \cdot 10^{+87}:\\ \;\;\;\;\frac{y \cdot x}{a}\\ \mathbf{elif}\;y \leq 3.1 \cdot 10^{+101}:\\ \;\;\;\;t \cdot \frac{-z}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{a}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= y -4.8e-109)
   (* y (/ x a))
   (if (<= y 1.9e+44)
     (/ z (- (/ a t)))
     (if (<= y 2.05e+87)
       (/ (* y x) a)
       (if (<= y 3.1e+101) (* t (/ (- z) a)) (/ y (/ a x)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -4.8e-109) {
		tmp = y * (x / a);
	} else if (y <= 1.9e+44) {
		tmp = z / -(a / t);
	} else if (y <= 2.05e+87) {
		tmp = (y * x) / a;
	} else if (y <= 3.1e+101) {
		tmp = t * (-z / a);
	} else {
		tmp = y / (a / x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if (y <= (-4.8d-109)) then
        tmp = y * (x / a)
    else if (y <= 1.9d+44) then
        tmp = z / -(a / t)
    else if (y <= 2.05d+87) then
        tmp = (y * x) / a
    else if (y <= 3.1d+101) then
        tmp = t * (-z / a)
    else
        tmp = y / (a / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -4.8e-109) {
		tmp = y * (x / a);
	} else if (y <= 1.9e+44) {
		tmp = z / -(a / t);
	} else if (y <= 2.05e+87) {
		tmp = (y * x) / a;
	} else if (y <= 3.1e+101) {
		tmp = t * (-z / a);
	} else {
		tmp = y / (a / x);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if y <= -4.8e-109:
		tmp = y * (x / a)
	elif y <= 1.9e+44:
		tmp = z / -(a / t)
	elif y <= 2.05e+87:
		tmp = (y * x) / a
	elif y <= 3.1e+101:
		tmp = t * (-z / a)
	else:
		tmp = y / (a / x)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (y <= -4.8e-109)
		tmp = Float64(y * Float64(x / a));
	elseif (y <= 1.9e+44)
		tmp = Float64(z / Float64(-Float64(a / t)));
	elseif (y <= 2.05e+87)
		tmp = Float64(Float64(y * x) / a);
	elseif (y <= 3.1e+101)
		tmp = Float64(t * Float64(Float64(-z) / a));
	else
		tmp = Float64(y / Float64(a / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (y <= -4.8e-109)
		tmp = y * (x / a);
	elseif (y <= 1.9e+44)
		tmp = z / -(a / t);
	elseif (y <= 2.05e+87)
		tmp = (y * x) / a;
	elseif (y <= 3.1e+101)
		tmp = t * (-z / a);
	else
		tmp = y / (a / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[y, -4.8e-109], N[(y * N[(x / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.9e+44], N[(z / (-N[(a / t), $MachinePrecision])), $MachinePrecision], If[LessEqual[y, 2.05e+87], N[(N[(y * x), $MachinePrecision] / a), $MachinePrecision], If[LessEqual[y, 3.1e+101], N[(t * N[((-z) / a), $MachinePrecision]), $MachinePrecision], N[(y / N[(a / x), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if y < -4.79999999999999977e-109

   1. Initial program 97.7%

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

   2. Taylor expanded in x around inf 73.3%

      \[\leadsto \color{blue}{\frac{y \cdot x}{a}} \]
   3. Step-by-step derivation

      1. associate-*r/ 71.3%

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

   4. Simplified 71.3%

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

   if -4.79999999999999977e-109 < y < 1.9000000000000001e44

   1. Initial program 97.0%

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

   2. Taylor expanded in x around 0 72.9%

      \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot z}{a}} \]
   3. Step-by-step derivation

      1. associate-*r/ 72.9%

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

      2. mul-1-neg 72.9%

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

      3. distribute-rgt-neg-out 72.9%

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

      4. *-commutative 72.9%

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

      5. associate-/l* 71.4%

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

      6. associate-/r/ 66.9%

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

   4. Simplified 66.9%

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

      1. add-sqr-sqrt 32.2%

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

      2. sqrt-unprod 26.8%

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

      3. sqr-neg 26.8%

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

      4. sqrt-unprod 1.7%

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

      5. add-sqr-sqrt 3.2%

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

      6. associate-/r/ 4.1%

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

      7. frac-2neg 4.1%

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

      8. add-sqr-sqrt 2.4%

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

      9. sqrt-unprod 30.1%

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

      10. sqr-neg 30.1%

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

      11. sqrt-unprod 35.5%

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

      12. add-sqr-sqrt 71.4%

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

   6. Applied egg-rr 71.4%

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

   if 1.9000000000000001e44 < y < 2.05e87

   1. Initial program 100.0%

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

   2. Taylor expanded in x around inf 66.9%

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

   if 2.05e87 < y < 3.09999999999999999e101

   1. Initial program 99.6%

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

   2. Taylor expanded in x around 0 75.5%

      \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot z}{a}} \]
   3. Step-by-step derivation

      1. associate-*r/ 75.5%

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

      2. mul-1-neg 75.5%

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

      3. distribute-rgt-neg-out 75.5%

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

      4. *-commutative 75.5%

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

      5. associate-/l* 75.4%

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

      6. associate-/r/ 75.4%

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

   4. Simplified 75.4%

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

   if 3.09999999999999999e101 < y

   1. Initial program 99.8%

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

   2. Taylor expanded in x around inf 85.0%

      \[\leadsto \color{blue}{\frac{y \cdot x}{a}} \]
   3. Step-by-step derivation

      1. associate-/l* 85.1%

         \[\leadsto \color{blue}{\frac{y}{\frac{a}{x}}} \]

   4. Simplified 85.1%

      \[\leadsto \color{blue}{\frac{y}{\frac{a}{x}}} \]
3. Recombined 5 regimes into one program.

4. Final simplification 73.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.8 \cdot 10^{-109}:\\ \;\;\;\;y \cdot \frac{x}{a}\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+44}:\\ \;\;\;\;\frac{z}{-\frac{a}{t}}\\ \mathbf{elif}\;y \leq 2.05 \cdot 10^{+87}:\\ \;\;\;\;\frac{y \cdot x}{a}\\ \mathbf{elif}\;y \leq 3.1 \cdot 10^{+101}:\\ \;\;\;\;t \cdot \frac{-z}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{a}{x}}\\ \end{array} \]

Alternative 5: 67.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{-102}:\\ \;\;\;\;y \cdot \frac{x}{a}\\ \mathbf{elif}\;y \leq 1.02 \cdot 10^{+101}:\\ \;\;\;\;\frac{t \cdot \left(-z\right)}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{a}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= y -5e-102)
   (* y (/ x a))
   (if (<= y 1.02e+101) (/ (* t (- z)) a) (/ y (/ a x)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -5e-102) {
		tmp = y * (x / a);
	} else if (y <= 1.02e+101) {
		tmp = (t * -z) / a;
	} else {
		tmp = y / (a / x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if (y <= (-5d-102)) then
        tmp = y * (x / a)
    else if (y <= 1.02d+101) then
        tmp = (t * -z) / a
    else
        tmp = y / (a / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -5e-102) {
		tmp = y * (x / a);
	} else if (y <= 1.02e+101) {
		tmp = (t * -z) / a;
	} else {
		tmp = y / (a / x);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if y <= -5e-102:
		tmp = y * (x / a)
	elif y <= 1.02e+101:
		tmp = (t * -z) / a
	else:
		tmp = y / (a / x)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (y <= -5e-102)
		tmp = Float64(y * Float64(x / a));
	elseif (y <= 1.02e+101)
		tmp = Float64(Float64(t * Float64(-z)) / a);
	else
		tmp = Float64(y / Float64(a / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (y <= -5e-102)
		tmp = y * (x / a);
	elseif (y <= 1.02e+101)
		tmp = (t * -z) / a;
	else
		tmp = y / (a / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[y, -5e-102], N[(y * N[(x / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.02e+101], N[(N[(t * (-z)), $MachinePrecision] / a), $MachinePrecision], N[(y / N[(a / x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -5.00000000000000026e-102

   1. Initial program 97.6%

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

   2. Taylor expanded in x around inf 73.0%

      \[\leadsto \color{blue}{\frac{y \cdot x}{a}} \]
   3. Step-by-step derivation

      1. associate-*r/ 72.0%

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

   4. Simplified 72.0%

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

   if -5.00000000000000026e-102 < y < 1.02000000000000002e101

   1. Initial program 97.3%

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

   2. Taylor expanded in x around 0 70.3%

      \[\leadsto \color{blue}{-1 \cdot \frac{t \cdot z}{a}} \]
   3. Step-by-step derivation

      1. associate-*r/ 70.3%

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

      2. associate-*r* 70.3%

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

      3. neg-mul-1 70.3%

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

   4. Simplified 70.3%

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

   if 1.02000000000000002e101 < y

   1. Initial program 99.8%

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

   2. Taylor expanded in x around inf 85.0%

      \[\leadsto \color{blue}{\frac{y \cdot x}{a}} \]
   3. Step-by-step derivation

      1. associate-/l* 85.1%

         \[\leadsto \color{blue}{\frac{y}{\frac{a}{x}}} \]

   4. Simplified 85.1%

      \[\leadsto \color{blue}{\frac{y}{\frac{a}{x}}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 73.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{-102}:\\ \;\;\;\;y \cdot \frac{x}{a}\\ \mathbf{elif}\;y \leq 1.02 \cdot 10^{+101}:\\ \;\;\;\;\frac{t \cdot \left(-z\right)}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{a}{x}}\\ \end{array} \]

Alternative 6: 98.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{y \cdot x - t \cdot z}{a} \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (/ (- (* y x) (* t z)) a))

C

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

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = ((y * x) - (t * z)) / a
end function

Java

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

Python

def code(x, y, z, t, a):
	return ((y * x) - (t * z)) / a

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(y * x) - Float64(t * z)) / a)
end

MATLAB

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

Wolfram

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

Derivation

1. Initial program 97.9%

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

2. Final simplification 97.9%

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

Alternative 7: 50.0% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ y \cdot \frac{x}{a} \end{array} \]

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = y * (x / a)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := N[(y * N[(x / a), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 97.9%

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

2. Taylor expanded in x around inf 56.3%

   \[\leadsto \color{blue}{\frac{y \cdot x}{a}} \]
3. Step-by-step derivation

   1. associate-*r/ 55.2%

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

4. Simplified 55.2%

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

5. Final simplification 55.2%

   \[\leadsto y \cdot \frac{x}{a} \]

Developer target: 92.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y}{a} \cdot x - \frac{t}{a} \cdot z\\ \mathbf{if}\;z < -2.468684968699548 \cdot 10^{+170}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z < 6.309831121978371 \cdot 10^{-71}:\\ \;\;\;\;\frac{x \cdot y - z \cdot t}{a}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- (* (/ y a) x) (* (/ t a) z))))
   (if (< z -2.468684968699548e+170)
     t_1
     (if (< z 6.309831121978371e-71) (/ (- (* x y) (* z t)) a) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = ((y / a) * x) - ((t / a) * z);
	double tmp;
	if (z < -2.468684968699548e+170) {
		tmp = t_1;
	} else if (z < 6.309831121978371e-71) {
		tmp = ((x * y) - (z * t)) / a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = ((y / a) * x) - ((t / a) * z)
    if (z < (-2.468684968699548d+170)) then
        tmp = t_1
    else if (z < 6.309831121978371d-71) then
        tmp = ((x * y) - (z * t)) / a
    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 t_1 = ((y / a) * x) - ((t / a) * z);
	double tmp;
	if (z < -2.468684968699548e+170) {
		tmp = t_1;
	} else if (z < 6.309831121978371e-71) {
		tmp = ((x * y) - (z * t)) / a;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = ((y / a) * x) - ((t / a) * z)
	tmp = 0
	if z < -2.468684968699548e+170:
		tmp = t_1
	elif z < 6.309831121978371e-71:
		tmp = ((x * y) - (z * t)) / a
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(Float64(y / a) * x) - Float64(Float64(t / a) * z))
	tmp = 0.0
	if (z < -2.468684968699548e+170)
		tmp = t_1;
	elseif (z < 6.309831121978371e-71)
		tmp = Float64(Float64(Float64(x * y) - Float64(z * t)) / a);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = ((y / a) * x) - ((t / a) * z);
	tmp = 0.0;
	if (z < -2.468684968699548e+170)
		tmp = t_1;
	elseif (z < 6.309831121978371e-71)
		tmp = ((x * y) - (z * t)) / a;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(N[(y / a), $MachinePrecision] * x), $MachinePrecision] - N[(N[(t / a), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]}, If[Less[z, -2.468684968699548e+170], t$95$1, If[Less[z, 6.309831121978371e-71], N[(N[(N[(x * y), $MachinePrecision] - N[(z * t), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2023278 
(FPCore (x y z t a)
  :name "Data.Colour.Matrix:inverse from colour-2.3.3, B"
  :precision binary64

  :herbie-target
  (if (< z -2.468684968699548e+170) (- (* (/ y a) x) (* (/ t a) z)) (if (< z 6.309831121978371e-71) (/ (- (* x y) (* z t)) a) (- (* (/ y a) x) (* (/ t a) z))))

  (/ (- (* x y) (* z t)) a))