Data.Array.Repa.Algorithms.ColorRamp:rampColorHotToCold from repa-algorithms-3.4.0.1, A

Percentage Accurate: 99.8% → 99.8%

Time: 7.9s

Alternatives: 10

Speedup: 1.0×

• 21.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ 1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.75)) z)) y)))

C

double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 1.0d0 + ((4.0d0 * ((x + (y * 0.75d0)) - z)) / y)
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}

Python

def code(x, y, z):
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y)

Julia

function code(x, y, z)
	return Float64(1.0 + Float64(Float64(4.0 * Float64(Float64(x + Float64(y * 0.75)) - z)) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
end

Wolfram

code[x_, y_, z_] := N[(1.0 + N[(N[(4.0 * N[(N[(x + N[(y * 0.75), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.75)) z)) y)))

C

double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 1.0d0 + ((4.0d0 * ((x + (y * 0.75d0)) - z)) / y)
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}

Python

def code(x, y, z):
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y)

Julia

function code(x, y, z)
	return Float64(1.0 + Float64(Float64(4.0 * Float64(Float64(x + Float64(y * 0.75)) - z)) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
end

Wolfram

code[x_, y_, z_] := N[(1.0 + N[(N[(4.0 * N[(N[(x + N[(y * 0.75), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} + 1 \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ (/ (* (- (+ (* 0.75 y) x) z) 4.0) y) 1.0))

C

double code(double x, double y, double z) {
	return (((((0.75 * y) + x) - z) * 4.0) / y) + 1.0;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (((((0.75d0 * y) + x) - z) * 4.0d0) / y) + 1.0d0
end function

Java

public static double code(double x, double y, double z) {
	return (((((0.75 * y) + x) - z) * 4.0) / y) + 1.0;
}

Python

def code(x, y, z):
	return (((((0.75 * y) + x) - z) * 4.0) / y) + 1.0

Julia

function code(x, y, z)
	return Float64(Float64(Float64(Float64(Float64(Float64(0.75 * y) + x) - z) * 4.0) / y) + 1.0)
end

MATLAB

function tmp = code(x, y, z)
	tmp = (((((0.75 * y) + x) - z) * 4.0) / y) + 1.0;
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(N[(N[(N[(0.75 * y), $MachinePrecision] + x), $MachinePrecision] - z), $MachinePrecision] * 4.0), $MachinePrecision] / y), $MachinePrecision] + 1.0), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
2. Add Preprocessing

3. Final simplification 100.0%

   \[\leadsto \frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} + 1 \]
4. Add Preprocessing

Alternative 2: 66.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y}\\ \mathbf{if}\;t\_0 \leq -4 \cdot 10^{+62}:\\ \;\;\;\;\frac{x \cdot 4}{y}\\ \mathbf{elif}\;t\_0 \leq -100:\\ \;\;\;\;\frac{-4}{y} \cdot z\\ \mathbf{elif}\;t\_0 \leq 50000000:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{y} \cdot -4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* (- (+ (* 0.75 y) x) z) 4.0) y)))
   (if (<= t_0 -4e+62)
     (/ (* x 4.0) y)
     (if (<= t_0 -100.0)
       (* (/ -4.0 y) z)
       (if (<= t_0 50000000.0) 4.0 (* (/ z y) -4.0))))))

C

double code(double x, double y, double z) {
	double t_0 = ((((0.75 * y) + x) - z) * 4.0) / y;
	double tmp;
	if (t_0 <= -4e+62) {
		tmp = (x * 4.0) / y;
	} else if (t_0 <= -100.0) {
		tmp = (-4.0 / y) * z;
	} else if (t_0 <= 50000000.0) {
		tmp = 4.0;
	} else {
		tmp = (z / y) * -4.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((((0.75d0 * y) + x) - z) * 4.0d0) / y
    if (t_0 <= (-4d+62)) then
        tmp = (x * 4.0d0) / y
    else if (t_0 <= (-100.0d0)) then
        tmp = ((-4.0d0) / y) * z
    else if (t_0 <= 50000000.0d0) then
        tmp = 4.0d0
    else
        tmp = (z / y) * (-4.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = ((((0.75 * y) + x) - z) * 4.0) / y;
	double tmp;
	if (t_0 <= -4e+62) {
		tmp = (x * 4.0) / y;
	} else if (t_0 <= -100.0) {
		tmp = (-4.0 / y) * z;
	} else if (t_0 <= 50000000.0) {
		tmp = 4.0;
	} else {
		tmp = (z / y) * -4.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = ((((0.75 * y) + x) - z) * 4.0) / y
	tmp = 0
	if t_0 <= -4e+62:
		tmp = (x * 4.0) / y
	elif t_0 <= -100.0:
		tmp = (-4.0 / y) * z
	elif t_0 <= 50000000.0:
		tmp = 4.0
	else:
		tmp = (z / y) * -4.0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(Float64(0.75 * y) + x) - z) * 4.0) / y)
	tmp = 0.0
	if (t_0 <= -4e+62)
		tmp = Float64(Float64(x * 4.0) / y);
	elseif (t_0 <= -100.0)
		tmp = Float64(Float64(-4.0 / y) * z);
	elseif (t_0 <= 50000000.0)
		tmp = 4.0;
	else
		tmp = Float64(Float64(z / y) * -4.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = ((((0.75 * y) + x) - z) * 4.0) / y;
	tmp = 0.0;
	if (t_0 <= -4e+62)
		tmp = (x * 4.0) / y;
	elseif (t_0 <= -100.0)
		tmp = (-4.0 / y) * z;
	elseif (t_0 <= 50000000.0)
		tmp = 4.0;
	else
		tmp = (z / y) * -4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(N[(0.75 * y), $MachinePrecision] + x), $MachinePrecision] - z), $MachinePrecision] * 4.0), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[t$95$0, -4e+62], N[(N[(x * 4.0), $MachinePrecision] / y), $MachinePrecision], If[LessEqual[t$95$0, -100.0], N[(N[(-4.0 / y), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[t$95$0, 50000000.0], 4.0, N[(N[(z / y), $MachinePrecision] * -4.0), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < -4.00000000000000014e62

   1. Initial program 100.0%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{4 \cdot \frac{x}{y}} \]
   4. Step-by-step derivation

      1. *-lft-identity N/A

         \[\leadsto 4 \cdot \frac{\color{blue}{1 \cdot x}}{y} \]

      2. associate-*l/ N/A

         \[\leadsto 4 \cdot \color{blue}{\left(\frac{1}{y} \cdot x\right)} \]

      3. associate-*l* N/A

         \[\leadsto \color{blue}{\left(4 \cdot \frac{1}{y}\right) \cdot x} \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(4 \cdot \frac{1}{y}\right) \cdot x} \]

      5. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{4 \cdot 1}{y}} \cdot x \]

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{4}}{y} \cdot x \]

      7. lower-/.f64 65.2

         \[\leadsto \color{blue}{\frac{4}{y}} \cdot x \]

   5. Applied rewrites 65.2%

      \[\leadsto \color{blue}{\frac{4}{y} \cdot x} \]
   6. Step-by-step derivation

   7. Applied rewrites 65.3%

      \[\leadsto \frac{x \cdot 4}{\color{blue}{y}} \]

   if -4.00000000000000014e62 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < -100

   1. Initial program 99.9%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

      \[\leadsto \color{blue}{-4 \cdot \frac{z}{y}} \]
   4. Step-by-step derivation

      1. *-lft-identity N/A

         \[\leadsto -4 \cdot \frac{\color{blue}{1 \cdot z}}{y} \]

      2. associate-*l/ N/A

         \[\leadsto -4 \cdot \color{blue}{\left(\frac{1}{y} \cdot z\right)} \]

      3. associate-*l* N/A

         \[\leadsto \color{blue}{\left(-4 \cdot \frac{1}{y}\right) \cdot z} \]

      4. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\left(\mathsf{neg}\left(4\right)\right)} \cdot \frac{1}{y}\right) \cdot z \]

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

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)} \cdot z \]

      6. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right) \cdot z} \]

      7. associate-*r/ N/A

         \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\frac{4 \cdot 1}{y}}\right)\right) \cdot z \]

      8. metadata-eval N/A

         \[\leadsto \left(\mathsf{neg}\left(\frac{\color{blue}{4}}{y}\right)\right) \cdot z \]

      9. distribute-neg-frac N/A

         \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(4\right)}{y}} \cdot z \]

      10. metadata-eval N/A

          \[\leadsto \frac{\color{blue}{-4}}{y} \cdot z \]

      11. lower-/.f64 64.5

          \[\leadsto \color{blue}{\frac{-4}{y}} \cdot z \]

   5. Applied rewrites 64.5%

      \[\leadsto \color{blue}{\frac{-4}{y} \cdot z} \]

   if -100 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < 5e7

   1. Initial program 99.9%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{4} \]
   4. Step-by-step derivation

   5. Applied rewrites 96.0%

      \[\leadsto \color{blue}{4} \]

   if 5e7 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y)

   1. Initial program 100.0%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)\right)} \]

      2. lower-neg.f64 N/A

         \[\leadsto \color{blue}{-x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)} \]

      3. sub-neg N/A

         \[\leadsto -x \cdot \color{blue}{\left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} + \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

      4. distribute-lft-in N/A

         \[\leadsto -\color{blue}{\left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x}\right) + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

      5. *-commutative N/A

         \[\leadsto -\left(\color{blue}{\left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x}\right) \cdot x} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      6. associate-*r/ N/A

         \[\leadsto -\left(\color{blue}{\frac{-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)}{x}} \cdot x + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      7. associate-*l/ N/A

         \[\leadsto -\left(\color{blue}{\frac{\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot x}{x}} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      8. associate-/l* N/A

         \[\leadsto -\left(\color{blue}{\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \frac{x}{x}} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      9. *-rgt-identity N/A

         \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \frac{\color{blue}{x \cdot 1}}{x} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      10. associate-*r/ N/A

          \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \color{blue}{\left(x \cdot \frac{1}{x}\right)} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      11. rgt-mult-inverse N/A

          \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \color{blue}{1} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      12. lower-fma.f64 N/A

          \[\leadsto -\color{blue}{\mathsf{fma}\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right), 1, x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

   5. Applied rewrites 94.0%

      \[\leadsto \color{blue}{-\mathsf{fma}\left(\mathsf{fma}\left(\frac{4}{y}, z, -4\right), 1, \frac{-4}{y} \cdot x\right)} \]

   6. Taylor expanded in z around inf

      \[\leadsto \color{blue}{-4 \cdot \frac{z}{y}} \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\frac{z}{y} \cdot -4} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{z}{y} \cdot -4} \]

      3. lower-/.f64 57.2

         \[\leadsto \color{blue}{\frac{z}{y}} \cdot -4 \]

   8. Applied rewrites 57.2%

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

4. Final simplification 72.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq -4 \cdot 10^{+62}:\\ \;\;\;\;\frac{x \cdot 4}{y}\\ \mathbf{elif}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq -100:\\ \;\;\;\;\frac{-4}{y} \cdot z\\ \mathbf{elif}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq 50000000:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{y} \cdot -4\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 66.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y}\\ \mathbf{if}\;t\_0 \leq -4 \cdot 10^{+62}:\\ \;\;\;\;\frac{4}{y} \cdot x\\ \mathbf{elif}\;t\_0 \leq -100:\\ \;\;\;\;\frac{-4}{y} \cdot z\\ \mathbf{elif}\;t\_0 \leq 50000000:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{y} \cdot -4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* (- (+ (* 0.75 y) x) z) 4.0) y)))
   (if (<= t_0 -4e+62)
     (* (/ 4.0 y) x)
     (if (<= t_0 -100.0)
       (* (/ -4.0 y) z)
       (if (<= t_0 50000000.0) 4.0 (* (/ z y) -4.0))))))

C

double code(double x, double y, double z) {
	double t_0 = ((((0.75 * y) + x) - z) * 4.0) / y;
	double tmp;
	if (t_0 <= -4e+62) {
		tmp = (4.0 / y) * x;
	} else if (t_0 <= -100.0) {
		tmp = (-4.0 / y) * z;
	} else if (t_0 <= 50000000.0) {
		tmp = 4.0;
	} else {
		tmp = (z / y) * -4.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((((0.75d0 * y) + x) - z) * 4.0d0) / y
    if (t_0 <= (-4d+62)) then
        tmp = (4.0d0 / y) * x
    else if (t_0 <= (-100.0d0)) then
        tmp = ((-4.0d0) / y) * z
    else if (t_0 <= 50000000.0d0) then
        tmp = 4.0d0
    else
        tmp = (z / y) * (-4.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = ((((0.75 * y) + x) - z) * 4.0) / y;
	double tmp;
	if (t_0 <= -4e+62) {
		tmp = (4.0 / y) * x;
	} else if (t_0 <= -100.0) {
		tmp = (-4.0 / y) * z;
	} else if (t_0 <= 50000000.0) {
		tmp = 4.0;
	} else {
		tmp = (z / y) * -4.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = ((((0.75 * y) + x) - z) * 4.0) / y
	tmp = 0
	if t_0 <= -4e+62:
		tmp = (4.0 / y) * x
	elif t_0 <= -100.0:
		tmp = (-4.0 / y) * z
	elif t_0 <= 50000000.0:
		tmp = 4.0
	else:
		tmp = (z / y) * -4.0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(Float64(0.75 * y) + x) - z) * 4.0) / y)
	tmp = 0.0
	if (t_0 <= -4e+62)
		tmp = Float64(Float64(4.0 / y) * x);
	elseif (t_0 <= -100.0)
		tmp = Float64(Float64(-4.0 / y) * z);
	elseif (t_0 <= 50000000.0)
		tmp = 4.0;
	else
		tmp = Float64(Float64(z / y) * -4.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = ((((0.75 * y) + x) - z) * 4.0) / y;
	tmp = 0.0;
	if (t_0 <= -4e+62)
		tmp = (4.0 / y) * x;
	elseif (t_0 <= -100.0)
		tmp = (-4.0 / y) * z;
	elseif (t_0 <= 50000000.0)
		tmp = 4.0;
	else
		tmp = (z / y) * -4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(N[(0.75 * y), $MachinePrecision] + x), $MachinePrecision] - z), $MachinePrecision] * 4.0), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[t$95$0, -4e+62], N[(N[(4.0 / y), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[t$95$0, -100.0], N[(N[(-4.0 / y), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[t$95$0, 50000000.0], 4.0, N[(N[(z / y), $MachinePrecision] * -4.0), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < -4.00000000000000014e62

   1. Initial program 100.0%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{4 \cdot \frac{x}{y}} \]
   4. Step-by-step derivation

      1. *-lft-identity N/A

         \[\leadsto 4 \cdot \frac{\color{blue}{1 \cdot x}}{y} \]

      2. associate-*l/ N/A

         \[\leadsto 4 \cdot \color{blue}{\left(\frac{1}{y} \cdot x\right)} \]

      3. associate-*l* N/A

         \[\leadsto \color{blue}{\left(4 \cdot \frac{1}{y}\right) \cdot x} \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(4 \cdot \frac{1}{y}\right) \cdot x} \]

      5. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{4 \cdot 1}{y}} \cdot x \]

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{4}}{y} \cdot x \]

      7. lower-/.f64 65.2

         \[\leadsto \color{blue}{\frac{4}{y}} \cdot x \]

   5. Applied rewrites 65.2%

      \[\leadsto \color{blue}{\frac{4}{y} \cdot x} \]

   if -4.00000000000000014e62 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < -100

   1. Initial program 99.9%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

      \[\leadsto \color{blue}{-4 \cdot \frac{z}{y}} \]
   4. Step-by-step derivation

      1. *-lft-identity N/A

         \[\leadsto -4 \cdot \frac{\color{blue}{1 \cdot z}}{y} \]

      2. associate-*l/ N/A

         \[\leadsto -4 \cdot \color{blue}{\left(\frac{1}{y} \cdot z\right)} \]

      3. associate-*l* N/A

         \[\leadsto \color{blue}{\left(-4 \cdot \frac{1}{y}\right) \cdot z} \]

      4. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\left(\mathsf{neg}\left(4\right)\right)} \cdot \frac{1}{y}\right) \cdot z \]

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

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)} \cdot z \]

      6. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right) \cdot z} \]

      7. associate-*r/ N/A

         \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\frac{4 \cdot 1}{y}}\right)\right) \cdot z \]

      8. metadata-eval N/A

         \[\leadsto \left(\mathsf{neg}\left(\frac{\color{blue}{4}}{y}\right)\right) \cdot z \]

      9. distribute-neg-frac N/A

         \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(4\right)}{y}} \cdot z \]

      10. metadata-eval N/A

          \[\leadsto \frac{\color{blue}{-4}}{y} \cdot z \]

      11. lower-/.f64 64.5

          \[\leadsto \color{blue}{\frac{-4}{y}} \cdot z \]

   5. Applied rewrites 64.5%

      \[\leadsto \color{blue}{\frac{-4}{y} \cdot z} \]

   if -100 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < 5e7

   1. Initial program 99.9%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{4} \]
   4. Step-by-step derivation

   5. Applied rewrites 96.0%

      \[\leadsto \color{blue}{4} \]

   if 5e7 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y)

   1. Initial program 100.0%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)\right)} \]

      2. lower-neg.f64 N/A

         \[\leadsto \color{blue}{-x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)} \]

      3. sub-neg N/A

         \[\leadsto -x \cdot \color{blue}{\left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} + \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

      4. distribute-lft-in N/A

         \[\leadsto -\color{blue}{\left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x}\right) + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

      5. *-commutative N/A

         \[\leadsto -\left(\color{blue}{\left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x}\right) \cdot x} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      6. associate-*r/ N/A

         \[\leadsto -\left(\color{blue}{\frac{-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)}{x}} \cdot x + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      7. associate-*l/ N/A

         \[\leadsto -\left(\color{blue}{\frac{\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot x}{x}} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      8. associate-/l* N/A

         \[\leadsto -\left(\color{blue}{\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \frac{x}{x}} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      9. *-rgt-identity N/A

         \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \frac{\color{blue}{x \cdot 1}}{x} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      10. associate-*r/ N/A

          \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \color{blue}{\left(x \cdot \frac{1}{x}\right)} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      11. rgt-mult-inverse N/A

          \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \color{blue}{1} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      12. lower-fma.f64 N/A

          \[\leadsto -\color{blue}{\mathsf{fma}\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right), 1, x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

   5. Applied rewrites 94.0%

      \[\leadsto \color{blue}{-\mathsf{fma}\left(\mathsf{fma}\left(\frac{4}{y}, z, -4\right), 1, \frac{-4}{y} \cdot x\right)} \]

   6. Taylor expanded in z around inf

      \[\leadsto \color{blue}{-4 \cdot \frac{z}{y}} \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\frac{z}{y} \cdot -4} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{z}{y} \cdot -4} \]

      3. lower-/.f64 57.2

         \[\leadsto \color{blue}{\frac{z}{y}} \cdot -4 \]

   8. Applied rewrites 57.2%

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

4. Final simplification 72.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq -4 \cdot 10^{+62}:\\ \;\;\;\;\frac{4}{y} \cdot x\\ \mathbf{elif}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq -100:\\ \;\;\;\;\frac{-4}{y} \cdot z\\ \mathbf{elif}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq 50000000:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{y} \cdot -4\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 66.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y}\\ t_1 := \frac{-4}{y} \cdot z\\ \mathbf{if}\;t\_0 \leq -4 \cdot 10^{+62}:\\ \;\;\;\;\frac{4}{y} \cdot x\\ \mathbf{elif}\;t\_0 \leq -100:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 50000000:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* (- (+ (* 0.75 y) x) z) 4.0) y)) (t_1 (* (/ -4.0 y) z)))
   (if (<= t_0 -4e+62)
     (* (/ 4.0 y) x)
     (if (<= t_0 -100.0) t_1 (if (<= t_0 50000000.0) 4.0 t_1)))))

C

double code(double x, double y, double z) {
	double t_0 = ((((0.75 * y) + x) - z) * 4.0) / y;
	double t_1 = (-4.0 / y) * z;
	double tmp;
	if (t_0 <= -4e+62) {
		tmp = (4.0 / y) * x;
	} else if (t_0 <= -100.0) {
		tmp = t_1;
	} else if (t_0 <= 50000000.0) {
		tmp = 4.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = ((((0.75d0 * y) + x) - z) * 4.0d0) / y
    t_1 = ((-4.0d0) / y) * z
    if (t_0 <= (-4d+62)) then
        tmp = (4.0d0 / y) * x
    else if (t_0 <= (-100.0d0)) then
        tmp = t_1
    else if (t_0 <= 50000000.0d0) then
        tmp = 4.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = ((((0.75 * y) + x) - z) * 4.0) / y;
	double t_1 = (-4.0 / y) * z;
	double tmp;
	if (t_0 <= -4e+62) {
		tmp = (4.0 / y) * x;
	} else if (t_0 <= -100.0) {
		tmp = t_1;
	} else if (t_0 <= 50000000.0) {
		tmp = 4.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = ((((0.75 * y) + x) - z) * 4.0) / y
	t_1 = (-4.0 / y) * z
	tmp = 0
	if t_0 <= -4e+62:
		tmp = (4.0 / y) * x
	elif t_0 <= -100.0:
		tmp = t_1
	elif t_0 <= 50000000.0:
		tmp = 4.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(Float64(0.75 * y) + x) - z) * 4.0) / y)
	t_1 = Float64(Float64(-4.0 / y) * z)
	tmp = 0.0
	if (t_0 <= -4e+62)
		tmp = Float64(Float64(4.0 / y) * x);
	elseif (t_0 <= -100.0)
		tmp = t_1;
	elseif (t_0 <= 50000000.0)
		tmp = 4.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = ((((0.75 * y) + x) - z) * 4.0) / y;
	t_1 = (-4.0 / y) * z;
	tmp = 0.0;
	if (t_0 <= -4e+62)
		tmp = (4.0 / y) * x;
	elseif (t_0 <= -100.0)
		tmp = t_1;
	elseif (t_0 <= 50000000.0)
		tmp = 4.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(N[(0.75 * y), $MachinePrecision] + x), $MachinePrecision] - z), $MachinePrecision] * 4.0), $MachinePrecision] / y), $MachinePrecision]}, Block[{t$95$1 = N[(N[(-4.0 / y), $MachinePrecision] * z), $MachinePrecision]}, If[LessEqual[t$95$0, -4e+62], N[(N[(4.0 / y), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[t$95$0, -100.0], t$95$1, If[LessEqual[t$95$0, 50000000.0], 4.0, t$95$1]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < -4.00000000000000014e62

   1. Initial program 100.0%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{4 \cdot \frac{x}{y}} \]
   4. Step-by-step derivation

      1. *-lft-identity N/A

         \[\leadsto 4 \cdot \frac{\color{blue}{1 \cdot x}}{y} \]

      2. associate-*l/ N/A

         \[\leadsto 4 \cdot \color{blue}{\left(\frac{1}{y} \cdot x\right)} \]

      3. associate-*l* N/A

         \[\leadsto \color{blue}{\left(4 \cdot \frac{1}{y}\right) \cdot x} \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(4 \cdot \frac{1}{y}\right) \cdot x} \]

      5. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{4 \cdot 1}{y}} \cdot x \]

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{4}}{y} \cdot x \]

      7. lower-/.f64 65.2

         \[\leadsto \color{blue}{\frac{4}{y}} \cdot x \]

   5. Applied rewrites 65.2%

      \[\leadsto \color{blue}{\frac{4}{y} \cdot x} \]

   if -4.00000000000000014e62 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < -100 or 5e7 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y)

   1. Initial program 100.0%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

      \[\leadsto \color{blue}{-4 \cdot \frac{z}{y}} \]
   4. Step-by-step derivation

      1. *-lft-identity N/A

         \[\leadsto -4 \cdot \frac{\color{blue}{1 \cdot z}}{y} \]

      2. associate-*l/ N/A

         \[\leadsto -4 \cdot \color{blue}{\left(\frac{1}{y} \cdot z\right)} \]

      3. associate-*l* N/A

         \[\leadsto \color{blue}{\left(-4 \cdot \frac{1}{y}\right) \cdot z} \]

      4. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\left(\mathsf{neg}\left(4\right)\right)} \cdot \frac{1}{y}\right) \cdot z \]

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

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)} \cdot z \]

      6. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right) \cdot z} \]

      7. associate-*r/ N/A

         \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\frac{4 \cdot 1}{y}}\right)\right) \cdot z \]

      8. metadata-eval N/A

         \[\leadsto \left(\mathsf{neg}\left(\frac{\color{blue}{4}}{y}\right)\right) \cdot z \]

      9. distribute-neg-frac N/A

         \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(4\right)}{y}} \cdot z \]

      10. metadata-eval N/A

          \[\leadsto \frac{\color{blue}{-4}}{y} \cdot z \]

      11. lower-/.f64 58.1

          \[\leadsto \color{blue}{\frac{-4}{y}} \cdot z \]

   5. Applied rewrites 58.1%

      \[\leadsto \color{blue}{\frac{-4}{y} \cdot z} \]

   if -100 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < 5e7

   1. Initial program 99.9%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{4} \]
   4. Step-by-step derivation

   5. Applied rewrites 96.0%

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

4. Final simplification 72.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq -4 \cdot 10^{+62}:\\ \;\;\;\;\frac{4}{y} \cdot x\\ \mathbf{elif}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq -100:\\ \;\;\;\;\frac{-4}{y} \cdot z\\ \mathbf{elif}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq 50000000:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;\frac{-4}{y} \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 98.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x - z}{y} \cdot 4\\ t_1 := \frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y}\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+14}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq 50000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{y}, 4, 4\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (/ (- x z) y) 4.0)) (t_1 (/ (* (- (+ (* 0.75 y) x) z) 4.0) y)))
   (if (<= t_1 -2e+14)
     t_0
     (if (<= t_1 50000000.0) (fma (/ x y) 4.0 4.0) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = ((x - z) / y) * 4.0;
	double t_1 = ((((0.75 * y) + x) - z) * 4.0) / y;
	double tmp;
	if (t_1 <= -2e+14) {
		tmp = t_0;
	} else if (t_1 <= 50000000.0) {
		tmp = fma((x / y), 4.0, 4.0);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(x - z) / y) * 4.0)
	t_1 = Float64(Float64(Float64(Float64(Float64(0.75 * y) + x) - z) * 4.0) / y)
	tmp = 0.0
	if (t_1 <= -2e+14)
		tmp = t_0;
	elseif (t_1 <= 50000000.0)
		tmp = fma(Float64(x / y), 4.0, 4.0);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(x - z), $MachinePrecision] / y), $MachinePrecision] * 4.0), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(N[(N[(0.75 * y), $MachinePrecision] + x), $MachinePrecision] - z), $MachinePrecision] * 4.0), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+14], t$95$0, If[LessEqual[t$95$1, 50000000.0], N[(N[(x / y), $MachinePrecision] * 4.0 + 4.0), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < -2e14 or 5e7 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y)

   1. Initial program 100.0%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)\right)} \]

      2. lower-neg.f64 N/A

         \[\leadsto \color{blue}{-x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)} \]

      3. sub-neg N/A

         \[\leadsto -x \cdot \color{blue}{\left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} + \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

      4. distribute-lft-in N/A

         \[\leadsto -\color{blue}{\left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x}\right) + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

      5. *-commutative N/A

         \[\leadsto -\left(\color{blue}{\left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x}\right) \cdot x} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      6. associate-*r/ N/A

         \[\leadsto -\left(\color{blue}{\frac{-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)}{x}} \cdot x + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      7. associate-*l/ N/A

         \[\leadsto -\left(\color{blue}{\frac{\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot x}{x}} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      8. associate-/l* N/A

         \[\leadsto -\left(\color{blue}{\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \frac{x}{x}} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      9. *-rgt-identity N/A

         \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \frac{\color{blue}{x \cdot 1}}{x} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      10. associate-*r/ N/A

          \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \color{blue}{\left(x \cdot \frac{1}{x}\right)} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      11. rgt-mult-inverse N/A

          \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \color{blue}{1} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      12. lower-fma.f64 N/A

          \[\leadsto -\color{blue}{\mathsf{fma}\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right), 1, x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

   5. Applied rewrites 94.5%

      \[\leadsto \color{blue}{-\mathsf{fma}\left(\mathsf{fma}\left(\frac{4}{y}, z, -4\right), 1, \frac{-4}{y} \cdot x\right)} \]

   6. Taylor expanded in y around 0

      \[\leadsto \color{blue}{4 \cdot \frac{x - z}{y}} \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\frac{x - z}{y} \cdot 4} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{x - z}{y} \cdot 4} \]

      3. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x - z}{y}} \cdot 4 \]

      4. lower--.f64 99.6

         \[\leadsto \frac{\color{blue}{x - z}}{y} \cdot 4 \]

   8. Applied rewrites 99.6%

      \[\leadsto \color{blue}{\frac{x - z}{y} \cdot 4} \]

   if -2e14 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < 5e7

   1. Initial program 99.9%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0

      \[\leadsto \color{blue}{1 + 4 \cdot \frac{x + \frac{3}{4} \cdot y}{y}} \]

   5. Applied rewrites 97.5%

      \[\leadsto \color{blue}{-\mathsf{fma}\left(\frac{-4}{y}, x, -4\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 97.5%

      \[\leadsto \mathsf{fma}\left(\frac{x}{y}, \color{blue}{4}, 4\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq -2 \cdot 10^{+14}:\\ \;\;\;\;\frac{x - z}{y} \cdot 4\\ \mathbf{elif}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq 50000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{y}, 4, 4\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{x - z}{y} \cdot 4\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 98.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-4}{y} \cdot \left(z - x\right)\\ t_1 := \frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y}\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+14}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq 50000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{y}, 4, 4\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (/ -4.0 y) (- z x)))
        (t_1 (/ (* (- (+ (* 0.75 y) x) z) 4.0) y)))
   (if (<= t_1 -2e+14)
     t_0
     (if (<= t_1 50000000.0) (fma (/ x y) 4.0 4.0) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (-4.0 / y) * (z - x);
	double t_1 = ((((0.75 * y) + x) - z) * 4.0) / y;
	double tmp;
	if (t_1 <= -2e+14) {
		tmp = t_0;
	} else if (t_1 <= 50000000.0) {
		tmp = fma((x / y), 4.0, 4.0);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(-4.0 / y) * Float64(z - x))
	t_1 = Float64(Float64(Float64(Float64(Float64(0.75 * y) + x) - z) * 4.0) / y)
	tmp = 0.0
	if (t_1 <= -2e+14)
		tmp = t_0;
	elseif (t_1 <= 50000000.0)
		tmp = fma(Float64(x / y), 4.0, 4.0);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(-4.0 / y), $MachinePrecision] * N[(z - x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(N[(N[(0.75 * y), $MachinePrecision] + x), $MachinePrecision] - z), $MachinePrecision] * 4.0), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+14], t$95$0, If[LessEqual[t$95$1, 50000000.0], N[(N[(x / y), $MachinePrecision] * 4.0 + 4.0), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < -2e14 or 5e7 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y)

   1. Initial program 100.0%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{4 \cdot \frac{x - z}{y}} \]

   5. Applied rewrites 99.3%

      \[\leadsto \color{blue}{\left(z - x\right) \cdot \frac{-4}{y}} \]

   if -2e14 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < 5e7

   1. Initial program 99.9%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0

      \[\leadsto \color{blue}{1 + 4 \cdot \frac{x + \frac{3}{4} \cdot y}{y}} \]

   5. Applied rewrites 97.5%

      \[\leadsto \color{blue}{-\mathsf{fma}\left(\frac{-4}{y}, x, -4\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 97.5%

      \[\leadsto \mathsf{fma}\left(\frac{x}{y}, \color{blue}{4}, 4\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq -2 \cdot 10^{+14}:\\ \;\;\;\;\frac{-4}{y} \cdot \left(z - x\right)\\ \mathbf{elif}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq 50000000:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{y}, 4, 4\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{-4}{y} \cdot \left(z - x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 67.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-4}{y} \cdot z\\ t_1 := \frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y}\\ \mathbf{if}\;t\_1 \leq -100:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq 50000000:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (/ -4.0 y) z)) (t_1 (/ (* (- (+ (* 0.75 y) x) z) 4.0) y)))
   (if (<= t_1 -100.0) t_0 (if (<= t_1 50000000.0) 4.0 t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (-4.0 / y) * z;
	double t_1 = ((((0.75 * y) + x) - z) * 4.0) / y;
	double tmp;
	if (t_1 <= -100.0) {
		tmp = t_0;
	} else if (t_1 <= 50000000.0) {
		tmp = 4.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = ((-4.0d0) / y) * z
    t_1 = ((((0.75d0 * y) + x) - z) * 4.0d0) / y
    if (t_1 <= (-100.0d0)) then
        tmp = t_0
    else if (t_1 <= 50000000.0d0) then
        tmp = 4.0d0
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (-4.0 / y) * z;
	double t_1 = ((((0.75 * y) + x) - z) * 4.0) / y;
	double tmp;
	if (t_1 <= -100.0) {
		tmp = t_0;
	} else if (t_1 <= 50000000.0) {
		tmp = 4.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (-4.0 / y) * z
	t_1 = ((((0.75 * y) + x) - z) * 4.0) / y
	tmp = 0
	if t_1 <= -100.0:
		tmp = t_0
	elif t_1 <= 50000000.0:
		tmp = 4.0
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(-4.0 / y) * z)
	t_1 = Float64(Float64(Float64(Float64(Float64(0.75 * y) + x) - z) * 4.0) / y)
	tmp = 0.0
	if (t_1 <= -100.0)
		tmp = t_0;
	elseif (t_1 <= 50000000.0)
		tmp = 4.0;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (-4.0 / y) * z;
	t_1 = ((((0.75 * y) + x) - z) * 4.0) / y;
	tmp = 0.0;
	if (t_1 <= -100.0)
		tmp = t_0;
	elseif (t_1 <= 50000000.0)
		tmp = 4.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(-4.0 / y), $MachinePrecision] * z), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(N[(N[(0.75 * y), $MachinePrecision] + x), $MachinePrecision] - z), $MachinePrecision] * 4.0), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[t$95$1, -100.0], t$95$0, If[LessEqual[t$95$1, 50000000.0], 4.0, t$95$0]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < -100 or 5e7 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y)

   1. Initial program 100.0%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

      \[\leadsto \color{blue}{-4 \cdot \frac{z}{y}} \]
   4. Step-by-step derivation

      1. *-lft-identity N/A

         \[\leadsto -4 \cdot \frac{\color{blue}{1 \cdot z}}{y} \]

      2. associate-*l/ N/A

         \[\leadsto -4 \cdot \color{blue}{\left(\frac{1}{y} \cdot z\right)} \]

      3. associate-*l* N/A

         \[\leadsto \color{blue}{\left(-4 \cdot \frac{1}{y}\right) \cdot z} \]

      4. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\left(\mathsf{neg}\left(4\right)\right)} \cdot \frac{1}{y}\right) \cdot z \]

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

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)} \cdot z \]

      6. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right) \cdot z} \]

      7. associate-*r/ N/A

         \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\frac{4 \cdot 1}{y}}\right)\right) \cdot z \]

      8. metadata-eval N/A

         \[\leadsto \left(\mathsf{neg}\left(\frac{\color{blue}{4}}{y}\right)\right) \cdot z \]

      9. distribute-neg-frac N/A

         \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(4\right)}{y}} \cdot z \]

      10. metadata-eval N/A

          \[\leadsto \frac{\color{blue}{-4}}{y} \cdot z \]

      11. lower-/.f64 51.6

          \[\leadsto \color{blue}{\frac{-4}{y}} \cdot z \]

   5. Applied rewrites 51.6%

      \[\leadsto \color{blue}{\frac{-4}{y} \cdot z} \]

   if -100 < (/.f64 (*.f64 #s(literal 4 binary64) (-.f64 (+.f64 x (*.f64 y #s(literal 3/4 binary64))) z)) y) < 5e7

   1. Initial program 99.9%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{4} \]
   4. Step-by-step derivation

   5. Applied rewrites 96.0%

      \[\leadsto \color{blue}{4} \]
3. Recombined 2 regimes into one program.

4. Final simplification 65.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq -100:\\ \;\;\;\;\frac{-4}{y} \cdot z\\ \mathbf{elif}\;\frac{\left(\left(0.75 \cdot y + x\right) - z\right) \cdot 4}{y} \leq 50000000:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;\frac{-4}{y} \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 86.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(\frac{x}{y}, 4, 4\right)\\ \mathbf{if}\;x \leq -1.46 \cdot 10^{+63}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 4 \cdot 10^{+32}:\\ \;\;\;\;\mathsf{fma}\left(\frac{z}{y}, -4, 4\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fma (/ x y) 4.0 4.0)))
   (if (<= x -1.46e+63) t_0 (if (<= x 4e+32) (fma (/ z y) -4.0 4.0) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fma((x / y), 4.0, 4.0);
	double tmp;
	if (x <= -1.46e+63) {
		tmp = t_0;
	} else if (x <= 4e+32) {
		tmp = fma((z / y), -4.0, 4.0);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = fma(Float64(x / y), 4.0, 4.0)
	tmp = 0.0
	if (x <= -1.46e+63)
		tmp = t_0;
	elseif (x <= 4e+32)
		tmp = fma(Float64(z / y), -4.0, 4.0);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x / y), $MachinePrecision] * 4.0 + 4.0), $MachinePrecision]}, If[LessEqual[x, -1.46e+63], t$95$0, If[LessEqual[x, 4e+32], N[(N[(z / y), $MachinePrecision] * -4.0 + 4.0), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.4599999999999999e63 or 4.00000000000000021e32 < x

   1. Initial program 100.0%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0

      \[\leadsto \color{blue}{1 + 4 \cdot \frac{x + \frac{3}{4} \cdot y}{y}} \]

   5. Applied rewrites 85.6%

      \[\leadsto \color{blue}{-\mathsf{fma}\left(\frac{-4}{y}, x, -4\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 85.7%

      \[\leadsto \mathsf{fma}\left(\frac{x}{y}, \color{blue}{4}, 4\right) \]

   if -1.4599999999999999e63 < x < 4.00000000000000021e32

   1. Initial program 99.9%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1 + 4 \cdot \frac{\frac{3}{4} \cdot y - z}{y}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{4 \cdot \frac{\frac{3}{4} \cdot y - z}{y} + 1} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{\frac{3}{4} \cdot y - z}{y} \cdot 4} + 1 \]

      3. div-sub N/A

         \[\leadsto \color{blue}{\left(\frac{\frac{3}{4} \cdot y}{y} - \frac{z}{y}\right)} \cdot 4 + 1 \]

      4. associate-/l* N/A

         \[\leadsto \left(\color{blue}{\frac{3}{4} \cdot \frac{y}{y}} - \frac{z}{y}\right) \cdot 4 + 1 \]

      5. *-inverses N/A

         \[\leadsto \left(\frac{3}{4} \cdot \color{blue}{1} - \frac{z}{y}\right) \cdot 4 + 1 \]

      6. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\frac{3}{4}} - \frac{z}{y}\right) \cdot 4 + 1 \]

      7. *-commutative N/A

         \[\leadsto \color{blue}{4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)} + 1 \]

      8. sub-neg N/A

         \[\leadsto 4 \cdot \color{blue}{\left(\frac{3}{4} + \left(\mathsf{neg}\left(\frac{z}{y}\right)\right)\right)} + 1 \]

      9. +-commutative N/A

         \[\leadsto 4 \cdot \color{blue}{\left(\left(\mathsf{neg}\left(\frac{z}{y}\right)\right) + \frac{3}{4}\right)} + 1 \]

      10. distribute-rgt-in N/A

          \[\leadsto \color{blue}{\left(\left(\mathsf{neg}\left(\frac{z}{y}\right)\right) \cdot 4 + \frac{3}{4} \cdot 4\right)} + 1 \]

      11. *-commutative N/A

          \[\leadsto \left(\color{blue}{4 \cdot \left(\mathsf{neg}\left(\frac{z}{y}\right)\right)} + \frac{3}{4} \cdot 4\right) + 1 \]

      12. *-lft-identity N/A

          \[\leadsto \left(4 \cdot \left(\mathsf{neg}\left(\frac{\color{blue}{1 \cdot z}}{y}\right)\right) + \frac{3}{4} \cdot 4\right) + 1 \]

      13. associate-*l/ N/A

          \[\leadsto \left(4 \cdot \left(\mathsf{neg}\left(\color{blue}{\frac{1}{y} \cdot z}\right)\right) + \frac{3}{4} \cdot 4\right) + 1 \]

      14. distribute-rgt-neg-in N/A

          \[\leadsto \left(\color{blue}{\left(\mathsf{neg}\left(4 \cdot \left(\frac{1}{y} \cdot z\right)\right)\right)} + \frac{3}{4} \cdot 4\right) + 1 \]

      15. associate-*l* N/A

          \[\leadsto \left(\left(\mathsf{neg}\left(\color{blue}{\left(4 \cdot \frac{1}{y}\right) \cdot z}\right)\right) + \frac{3}{4} \cdot 4\right) + 1 \]

      16. metadata-eval N/A

          \[\leadsto \left(\left(\mathsf{neg}\left(\left(4 \cdot \frac{1}{y}\right) \cdot z\right)\right) + \color{blue}{3}\right) + 1 \]

      17. associate-+l+ N/A

          \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\left(4 \cdot \frac{1}{y}\right) \cdot z\right)\right) + \left(3 + 1\right)} \]

      18. metadata-eval N/A

          \[\leadsto \left(\mathsf{neg}\left(\left(4 \cdot \frac{1}{y}\right) \cdot z\right)\right) + \color{blue}{4} \]

   5. Applied rewrites 91.8%

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

Alternative 9: 80.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{z}{y} \cdot -4\\ \mathbf{if}\;z \leq -2.4 \cdot 10^{+104}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 4.7 \cdot 10^{+117}:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{y}, 4, 4\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (/ z y) -4.0)))
   (if (<= z -2.4e+104) t_0 (if (<= z 4.7e+117) (fma (/ x y) 4.0 4.0) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (z / y) * -4.0;
	double tmp;
	if (z <= -2.4e+104) {
		tmp = t_0;
	} else if (z <= 4.7e+117) {
		tmp = fma((x / y), 4.0, 4.0);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(z / y) * -4.0)
	tmp = 0.0
	if (z <= -2.4e+104)
		tmp = t_0;
	elseif (z <= 4.7e+117)
		tmp = fma(Float64(x / y), 4.0, 4.0);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(z / y), $MachinePrecision] * -4.0), $MachinePrecision]}, If[LessEqual[z, -2.4e+104], t$95$0, If[LessEqual[z, 4.7e+117], N[(N[(x / y), $MachinePrecision] * 4.0 + 4.0), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -2.4e104 or 4.70000000000000006e117 < z

   1. Initial program 100.0%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)\right)} \]

      2. lower-neg.f64 N/A

         \[\leadsto \color{blue}{-x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} - 4 \cdot \frac{1}{y}\right)} \]

      3. sub-neg N/A

         \[\leadsto -x \cdot \color{blue}{\left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x} + \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

      4. distribute-lft-in N/A

         \[\leadsto -\color{blue}{\left(x \cdot \left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x}\right) + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

      5. *-commutative N/A

         \[\leadsto -\left(\color{blue}{\left(-1 \cdot \frac{1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)}{x}\right) \cdot x} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      6. associate-*r/ N/A

         \[\leadsto -\left(\color{blue}{\frac{-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)}{x}} \cdot x + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      7. associate-*l/ N/A

         \[\leadsto -\left(\color{blue}{\frac{\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot x}{x}} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      8. associate-/l* N/A

         \[\leadsto -\left(\color{blue}{\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \frac{x}{x}} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      9. *-rgt-identity N/A

         \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \frac{\color{blue}{x \cdot 1}}{x} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      10. associate-*r/ N/A

          \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \color{blue}{\left(x \cdot \frac{1}{x}\right)} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      11. rgt-mult-inverse N/A

          \[\leadsto -\left(\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right)\right) \cdot \color{blue}{1} + x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right) \]

      12. lower-fma.f64 N/A

          \[\leadsto -\color{blue}{\mathsf{fma}\left(-1 \cdot \left(1 + 4 \cdot \left(\frac{3}{4} - \frac{z}{y}\right)\right), 1, x \cdot \left(\mathsf{neg}\left(4 \cdot \frac{1}{y}\right)\right)\right)} \]

   5. Applied rewrites 94.2%

      \[\leadsto \color{blue}{-\mathsf{fma}\left(\mathsf{fma}\left(\frac{4}{y}, z, -4\right), 1, \frac{-4}{y} \cdot x\right)} \]

   6. Taylor expanded in z around inf

      \[\leadsto \color{blue}{-4 \cdot \frac{z}{y}} \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\frac{z}{y} \cdot -4} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{z}{y} \cdot -4} \]

      3. lower-/.f64 72.5

         \[\leadsto \color{blue}{\frac{z}{y}} \cdot -4 \]

   8. Applied rewrites 72.5%

      \[\leadsto \color{blue}{\frac{z}{y} \cdot -4} \]

   if -2.4e104 < z < 4.70000000000000006e117

   1. Initial program 99.9%

      \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0

      \[\leadsto \color{blue}{1 + 4 \cdot \frac{x + \frac{3}{4} \cdot y}{y}} \]

   5. Applied rewrites 85.4%

      \[\leadsto \color{blue}{-\mathsf{fma}\left(\frac{-4}{y}, x, -4\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 85.5%

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

Alternative 10: 34.1% accurate, 31.0× speedup

Math

\[\begin{array}{l} \\ 4 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 4.0)

C

double code(double x, double y, double z) {
	return 4.0;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 4.0d0
end function

Java

public static double code(double x, double y, double z) {
	return 4.0;
}

Python

def code(x, y, z):
	return 4.0

Julia

function code(x, y, z)
	return 4.0
end

MATLAB

function tmp = code(x, y, z)
	tmp = 4.0;
end

Wolfram

code[x_, y_, z_] := 4.0

Derivation

1. Initial program 100.0%

   \[1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \]
2. Add Preprocessing

3. Taylor expanded in y around inf

   \[\leadsto \color{blue}{4} \]
4. Step-by-step derivation

5. Applied rewrites 31.9%

   \[\leadsto \color{blue}{4} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024276 
(FPCore (x y z)
  :name "Data.Array.Repa.Algorithms.ColorRamp:rampColorHotToCold from repa-algorithms-3.4.0.1, A"
  :precision binary64
  (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.75)) z)) y)))