Language.Haskell.HsColour.ColourHighlight:unbase from hscolour-1.23

Percentage Accurate: 99.9% → 99.9%

Time: 8.4s

Alternatives: 7

Speedup: 1.0×

• 29.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (+ (* (+ (* x y) z) y) t))

C

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

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (((x * y) + z) * y) + t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (+ (* (+ (* x y) z) y) t))

C

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

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (((x * y) + z) * y) + t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (+ (* y (+ (* x y) z)) t))

C

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

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (y * ((x * y) + z)) + t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

   \[\left(x \cdot y + z\right) \cdot y + t \]
2. Add Preprocessing

3. Final simplification 99.9%

   \[\leadsto y \cdot \left(x \cdot y + z\right) + t \]
4. Add Preprocessing

Alternative 2: 90.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(x \cdot y + z\right)\\ t_2 := y \cdot \mathsf{fma}\left(y, x, z\right)\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+80}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 4 \cdot 10^{+161}:\\ \;\;\;\;\mathsf{fma}\left(y, z, t\right)\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (+ (* x y) z))) (t_2 (* y (fma y x z))))
   (if (<= t_1 -2e+80) t_2 (if (<= t_1 4e+161) (fma y z t) t_2))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * ((x * y) + z);
	double t_2 = y * fma(y, x, z);
	double tmp;
	if (t_1 <= -2e+80) {
		tmp = t_2;
	} else if (t_1 <= 4e+161) {
		tmp = fma(y, z, t);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(Float64(x * y) + z))
	t_2 = Float64(y * fma(y, x, z))
	tmp = 0.0
	if (t_1 <= -2e+80)
		tmp = t_2;
	elseif (t_1 <= 4e+161)
		tmp = fma(y, z, t);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(y * x + z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+80], t$95$2, If[LessEqual[t$95$1, 4e+161], N[(y * z + t), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (+.f64 (*.f64 x y) z) y) < -2e80 or 4.0000000000000002e161 < (*.f64 (+.f64 (*.f64 x y) z) y)

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{{y}^{2} \cdot \left(x + \frac{z}{y}\right)} \]

   5. Simplified 94.3%

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

   if -2e80 < (*.f64 (+.f64 (*.f64 x y) z) y) < 4.0000000000000002e161

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. accelerator-lowering-fma.f64 88.8

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

   5. Simplified 88.8%

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

4. Final simplification 91.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot \left(x \cdot y + z\right) \leq -2 \cdot 10^{+80}:\\ \;\;\;\;y \cdot \mathsf{fma}\left(y, x, z\right)\\ \mathbf{elif}\;y \cdot \left(x \cdot y + z\right) \leq 4 \cdot 10^{+161}:\\ \;\;\;\;\mathsf{fma}\left(y, z, t\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \mathsf{fma}\left(y, x, z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 80.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(x \cdot y + z\right)\\ \mathbf{if}\;t\_1 \leq -1 \cdot 10^{+222}:\\ \;\;\;\;y \cdot \left(x \cdot y\right)\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{+177}:\\ \;\;\;\;\mathsf{fma}\left(y, z, t\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y \cdot y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (+ (* x y) z))))
   (if (<= t_1 -1e+222)
     (* y (* x y))
     (if (<= t_1 2e+177) (fma y z t) (* x (* y y))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * ((x * y) + z);
	double tmp;
	if (t_1 <= -1e+222) {
		tmp = y * (x * y);
	} else if (t_1 <= 2e+177) {
		tmp = fma(y, z, t);
	} else {
		tmp = x * (y * y);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(Float64(x * y) + z))
	tmp = 0.0
	if (t_1 <= -1e+222)
		tmp = Float64(y * Float64(x * y));
	elseif (t_1 <= 2e+177)
		tmp = fma(y, z, t);
	else
		tmp = Float64(x * Float64(y * y));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -1e+222], N[(y * N[(x * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 2e+177], N[(y * z + t), $MachinePrecision], N[(x * N[(y * y), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (+.f64 (*.f64 x y) z) y) < -1e222

   1. Initial program 99.8%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot {y}^{2}} \]
   4. Step-by-step derivation

      1. unpow2 N/A

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

      2. associate-*r* N/A

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

      3. *-commutative N/A

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

      4. *-lowering-*.f64 N/A

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

      5. *-commutative N/A

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

      6. *-lowering-*.f64 75.4

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

   5. Simplified 75.4%

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

   if -1e222 < (*.f64 (+.f64 (*.f64 x y) z) y) < 2e177

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. accelerator-lowering-fma.f64 86.1

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

   5. Simplified 86.1%

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

   if 2e177 < (*.f64 (+.f64 (*.f64 x y) z) y)

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. *-commutative N/A

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

      2. flip-+ N/A

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

      3. clear-num N/A

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

      4. un-div-inv N/A

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

      5. /-lowering-/.f64 N/A

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

      6. clear-num N/A

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

      7. flip-+ N/A

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

      8. /-lowering-/.f64 N/A

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

      9. accelerator-lowering-fma.f64 99.8

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

   4. Applied egg-rr 99.8%

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

   5. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x \cdot {y}^{2}} \]
   6. Step-by-step derivation

      1. *-lowering-*.f64 N/A

         \[\leadsto \color{blue}{x \cdot {y}^{2}} \]

      2. unpow2 N/A

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

      3. *-lowering-*.f64 79.2

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

   7. Simplified 79.2%

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

4. Final simplification 82.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot \left(x \cdot y + z\right) \leq -1 \cdot 10^{+222}:\\ \;\;\;\;y \cdot \left(x \cdot y\right)\\ \mathbf{elif}\;y \cdot \left(x \cdot y + z\right) \leq 2 \cdot 10^{+177}:\\ \;\;\;\;\mathsf{fma}\left(y, z, t\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y \cdot y\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 79.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(x \cdot y + z\right)\\ t_2 := x \cdot \left(y \cdot y\right)\\ \mathbf{if}\;t\_1 \leq -1 \cdot 10^{+222}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{+177}:\\ \;\;\;\;\mathsf{fma}\left(y, z, t\right)\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (+ (* x y) z))) (t_2 (* x (* y y))))
   (if (<= t_1 -1e+222) t_2 (if (<= t_1 2e+177) (fma y z t) t_2))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * ((x * y) + z);
	double t_2 = x * (y * y);
	double tmp;
	if (t_1 <= -1e+222) {
		tmp = t_2;
	} else if (t_1 <= 2e+177) {
		tmp = fma(y, z, t);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(Float64(x * y) + z))
	t_2 = Float64(x * Float64(y * y))
	tmp = 0.0
	if (t_1 <= -1e+222)
		tmp = t_2;
	elseif (t_1 <= 2e+177)
		tmp = fma(y, z, t);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(y * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -1e+222], t$95$2, If[LessEqual[t$95$1, 2e+177], N[(y * z + t), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (+.f64 (*.f64 x y) z) y) < -1e222 or 2e177 < (*.f64 (+.f64 (*.f64 x y) z) y)

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. *-commutative N/A

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

      2. flip-+ N/A

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

      3. clear-num N/A

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

      4. un-div-inv N/A

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

      5. /-lowering-/.f64 N/A

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

      6. clear-num N/A

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

      7. flip-+ N/A

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

      8. /-lowering-/.f64 N/A

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

      9. accelerator-lowering-fma.f64 99.8

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

   4. Applied egg-rr 99.8%

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

   5. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x \cdot {y}^{2}} \]
   6. Step-by-step derivation

      1. *-lowering-*.f64 N/A

         \[\leadsto \color{blue}{x \cdot {y}^{2}} \]

      2. unpow2 N/A

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

      3. *-lowering-*.f64 75.0

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

   7. Simplified 75.0%

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

   if -1e222 < (*.f64 (+.f64 (*.f64 x y) z) y) < 2e177

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. accelerator-lowering-fma.f64 86.1

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

   5. Simplified 86.1%

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

4. Final simplification 81.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot \left(x \cdot y + z\right) \leq -1 \cdot 10^{+222}:\\ \;\;\;\;x \cdot \left(y \cdot y\right)\\ \mathbf{elif}\;y \cdot \left(x \cdot y + z\right) \leq 2 \cdot 10^{+177}:\\ \;\;\;\;\mathsf{fma}\left(y, z, t\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y \cdot y\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 52.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(x \cdot y + z\right)\\ \mathbf{if}\;t\_1 \leq -0.01:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;t\_1 \leq 10^{+42}:\\ \;\;\;\;t\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (+ (* x y) z))))
   (if (<= t_1 -0.01) (* y z) (if (<= t_1 1e+42) t (* y z)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * ((x * y) + z);
	double tmp;
	if (t_1 <= -0.01) {
		tmp = y * z;
	} else if (t_1 <= 1e+42) {
		tmp = t;
	} else {
		tmp = y * z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * ((x * y) + z)
    if (t_1 <= (-0.01d0)) then
        tmp = y * z
    else if (t_1 <= 1d+42) then
        tmp = t
    else
        tmp = y * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * ((x * y) + z);
	double tmp;
	if (t_1 <= -0.01) {
		tmp = y * z;
	} else if (t_1 <= 1e+42) {
		tmp = t;
	} else {
		tmp = y * z;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * ((x * y) + z)
	tmp = 0
	if t_1 <= -0.01:
		tmp = y * z
	elif t_1 <= 1e+42:
		tmp = t
	else:
		tmp = y * z
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(Float64(x * y) + z))
	tmp = 0.0
	if (t_1 <= -0.01)
		tmp = Float64(y * z);
	elseif (t_1 <= 1e+42)
		tmp = t;
	else
		tmp = Float64(y * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * ((x * y) + z);
	tmp = 0.0;
	if (t_1 <= -0.01)
		tmp = y * z;
	elseif (t_1 <= 1e+42)
		tmp = t;
	else
		tmp = y * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -0.01], N[(y * z), $MachinePrecision], If[LessEqual[t$95$1, 1e+42], t, N[(y * z), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (+.f64 (*.f64 x y) z) y) < -0.0100000000000000002 or 1.00000000000000004e42 < (*.f64 (+.f64 (*.f64 x y) z) y)

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

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

      1. *-lowering-*.f64 38.5

         \[\leadsto \color{blue}{y \cdot z} \]

   5. Simplified 38.5%

      \[\leadsto \color{blue}{y \cdot z} \]

   if -0.0100000000000000002 < (*.f64 (+.f64 (*.f64 x y) z) y) < 1.00000000000000004e42

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

   5. Simplified 77.5%

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

4. Final simplification 53.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot \left(x \cdot y + z\right) \leq -0.01:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \cdot \left(x \cdot y + z\right) \leq 10^{+42}:\\ \;\;\;\;t\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 65.9% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y, z, t\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (fma y z t))

C

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

Julia

function code(x, y, z, t)
	return fma(y, z, t)
end

Wolfram

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

Derivation

1. Initial program 99.9%

   \[\left(x \cdot y + z\right) \cdot y + t \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. accelerator-lowering-fma.f64 64.3

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

5. Simplified 64.3%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, z, t\right)} \]
6. Add Preprocessing

Alternative 7: 38.9% accurate, 17.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := t

Derivation

1. Initial program 99.9%

   \[\left(x \cdot y + z\right) \cdot y + t \]
2. Add Preprocessing

3. Taylor expanded in y around 0

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

5. Simplified 36.7%

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

Reproduce

herbie shell --seed 2024205 
(FPCore (x y z t)
  :name "Language.Haskell.HsColour.ColourHighlight:unbase from hscolour-1.23"
  :precision binary64
  (+ (* (+ (* x y) z) y) t))