Graphics.Rendering.Chart.Drawing:drawTextsR from Chart-1.5.3

Percentage Accurate: 97.8% → 100.0%

Time: 5.4s

Alternatives: 10

Speedup: 1.4×

• 20.6% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 97.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.4× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 (fma (+ z y) x (- z)))

C

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

Julia

function code(x, y, z)
	return fma(Float64(z + y), x, Float64(-z))
end

Wolfram

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

Derivation

1. Initial program 97.2%

   \[x \cdot y + \left(x - 1\right) \cdot z \]
2. Add Preprocessing

3. Taylor expanded in z around 0

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

   1. sub-neg N/A

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

   2. metadata-eval N/A

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

   3. distribute-rgt-in N/A

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

   4. associate-+l+ N/A

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

   5. distribute-lft-in N/A

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

   6. *-commutative N/A

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

   7. lower-fma.f64 N/A

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

   8. +-commutative N/A

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

   9. lower-+.f64 N/A

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

   10. mul-1-neg N/A

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

   11. lower-neg.f64 100.0

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

5. Applied rewrites 100.0%

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

Alternative 2: 78.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(z + y\right) \cdot x\\ \mathbf{if}\;x \leq -1450:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 2.05 \cdot 10^{-240}:\\ \;\;\;\;z \cdot x - z\\ \mathbf{elif}\;x \leq 1.02 \cdot 10^{-97}:\\ \;\;\;\;\left(y - z\right) \cdot x\\ \mathbf{elif}\;x \leq 1.45 \cdot 10^{-45}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (+ z y) x)))
   (if (<= x -1450.0)
     t_0
     (if (<= x 2.05e-240)
       (- (* z x) z)
       (if (<= x 1.02e-97) (* (- y z) x) (if (<= x 1.45e-45) (- z) t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = (z + y) * x;
	double tmp;
	if (x <= -1450.0) {
		tmp = t_0;
	} else if (x <= 2.05e-240) {
		tmp = (z * x) - z;
	} else if (x <= 1.02e-97) {
		tmp = (y - z) * x;
	} else if (x <= 1.45e-45) {
		tmp = -z;
	} 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) :: tmp
    t_0 = (z + y) * x
    if (x <= (-1450.0d0)) then
        tmp = t_0
    else if (x <= 2.05d-240) then
        tmp = (z * x) - z
    else if (x <= 1.02d-97) then
        tmp = (y - z) * x
    else if (x <= 1.45d-45) then
        tmp = -z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (z + y) * x;
	double tmp;
	if (x <= -1450.0) {
		tmp = t_0;
	} else if (x <= 2.05e-240) {
		tmp = (z * x) - z;
	} else if (x <= 1.02e-97) {
		tmp = (y - z) * x;
	} else if (x <= 1.45e-45) {
		tmp = -z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (z + y) * x
	tmp = 0
	if x <= -1450.0:
		tmp = t_0
	elif x <= 2.05e-240:
		tmp = (z * x) - z
	elif x <= 1.02e-97:
		tmp = (y - z) * x
	elif x <= 1.45e-45:
		tmp = -z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(z + y) * x)
	tmp = 0.0
	if (x <= -1450.0)
		tmp = t_0;
	elseif (x <= 2.05e-240)
		tmp = Float64(Float64(z * x) - z);
	elseif (x <= 1.02e-97)
		tmp = Float64(Float64(y - z) * x);
	elseif (x <= 1.45e-45)
		tmp = Float64(-z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (z + y) * x;
	tmp = 0.0;
	if (x <= -1450.0)
		tmp = t_0;
	elseif (x <= 2.05e-240)
		tmp = (z * x) - z;
	elseif (x <= 1.02e-97)
		tmp = (y - z) * x;
	elseif (x <= 1.45e-45)
		tmp = -z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(z + y), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -1450.0], t$95$0, If[LessEqual[x, 2.05e-240], N[(N[(z * x), $MachinePrecision] - z), $MachinePrecision], If[LessEqual[x, 1.02e-97], N[(N[(y - z), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[x, 1.45e-45], (-z), t$95$0]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -1450 or 1.45e-45 < x

   1. Initial program 94.7%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 93.6

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

   5. Applied rewrites 93.6%

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

   if -1450 < x < 2.0500000000000001e-240

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 82.9

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

   5. Applied rewrites 82.9%

      \[\leadsto \color{blue}{\left(x - 1\right) \cdot z} \]
   6. Step-by-step derivation

   7. Applied rewrites 82.9%

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

   if 2.0500000000000001e-240 < x < 1.02000000000000004e-97

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 67.5

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

   5. Applied rewrites 67.5%

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

   7. Applied rewrites 68.0%

      \[\leadsto \left(y - z\right) \cdot x \]

   if 1.02000000000000004e-97 < x < 1.45e-45

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z\right)} \]

      2. lower-neg.f64 79.6

         \[\leadsto \color{blue}{-z} \]

   5. Applied rewrites 79.6%

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

Alternative 3: 78.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(z + y\right) \cdot x\\ \mathbf{if}\;x \leq -1450:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 2.05 \cdot 10^{-240}:\\ \;\;\;\;\left(x - 1\right) \cdot z\\ \mathbf{elif}\;x \leq 1.02 \cdot 10^{-97}:\\ \;\;\;\;\left(y - z\right) \cdot x\\ \mathbf{elif}\;x \leq 1.45 \cdot 10^{-45}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (+ z y) x)))
   (if (<= x -1450.0)
     t_0
     (if (<= x 2.05e-240)
       (* (- x 1.0) z)
       (if (<= x 1.02e-97) (* (- y z) x) (if (<= x 1.45e-45) (- z) t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = (z + y) * x;
	double tmp;
	if (x <= -1450.0) {
		tmp = t_0;
	} else if (x <= 2.05e-240) {
		tmp = (x - 1.0) * z;
	} else if (x <= 1.02e-97) {
		tmp = (y - z) * x;
	} else if (x <= 1.45e-45) {
		tmp = -z;
	} 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) :: tmp
    t_0 = (z + y) * x
    if (x <= (-1450.0d0)) then
        tmp = t_0
    else if (x <= 2.05d-240) then
        tmp = (x - 1.0d0) * z
    else if (x <= 1.02d-97) then
        tmp = (y - z) * x
    else if (x <= 1.45d-45) then
        tmp = -z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (z + y) * x;
	double tmp;
	if (x <= -1450.0) {
		tmp = t_0;
	} else if (x <= 2.05e-240) {
		tmp = (x - 1.0) * z;
	} else if (x <= 1.02e-97) {
		tmp = (y - z) * x;
	} else if (x <= 1.45e-45) {
		tmp = -z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (z + y) * x
	tmp = 0
	if x <= -1450.0:
		tmp = t_0
	elif x <= 2.05e-240:
		tmp = (x - 1.0) * z
	elif x <= 1.02e-97:
		tmp = (y - z) * x
	elif x <= 1.45e-45:
		tmp = -z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(z + y) * x)
	tmp = 0.0
	if (x <= -1450.0)
		tmp = t_0;
	elseif (x <= 2.05e-240)
		tmp = Float64(Float64(x - 1.0) * z);
	elseif (x <= 1.02e-97)
		tmp = Float64(Float64(y - z) * x);
	elseif (x <= 1.45e-45)
		tmp = Float64(-z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (z + y) * x;
	tmp = 0.0;
	if (x <= -1450.0)
		tmp = t_0;
	elseif (x <= 2.05e-240)
		tmp = (x - 1.0) * z;
	elseif (x <= 1.02e-97)
		tmp = (y - z) * x;
	elseif (x <= 1.45e-45)
		tmp = -z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(z + y), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -1450.0], t$95$0, If[LessEqual[x, 2.05e-240], N[(N[(x - 1.0), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[x, 1.02e-97], N[(N[(y - z), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[x, 1.45e-45], (-z), t$95$0]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -1450 or 1.45e-45 < x

   1. Initial program 94.7%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 93.6

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

   5. Applied rewrites 93.6%

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

   if -1450 < x < 2.0500000000000001e-240

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 82.9

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

   5. Applied rewrites 82.9%

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

   if 2.0500000000000001e-240 < x < 1.02000000000000004e-97

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 67.5

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

   5. Applied rewrites 67.5%

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

   7. Applied rewrites 68.0%

      \[\leadsto \left(y - z\right) \cdot x \]

   if 1.02000000000000004e-97 < x < 1.45e-45

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z\right)} \]

      2. lower-neg.f64 79.6

         \[\leadsto \color{blue}{-z} \]

   5. Applied rewrites 79.6%

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

Alternative 4: 78.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(z + y\right) \cdot x\\ \mathbf{if}\;x \leq -1450:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 2.05 \cdot 10^{-240}:\\ \;\;\;\;\left(x - 1\right) \cdot z\\ \mathbf{elif}\;x \leq 6.4 \cdot 10^{-98}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;x \leq 1.45 \cdot 10^{-45}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (+ z y) x)))
   (if (<= x -1450.0)
     t_0
     (if (<= x 2.05e-240)
       (* (- x 1.0) z)
       (if (<= x 6.4e-98) (* y x) (if (<= x 1.45e-45) (- z) t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = (z + y) * x;
	double tmp;
	if (x <= -1450.0) {
		tmp = t_0;
	} else if (x <= 2.05e-240) {
		tmp = (x - 1.0) * z;
	} else if (x <= 6.4e-98) {
		tmp = y * x;
	} else if (x <= 1.45e-45) {
		tmp = -z;
	} 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) :: tmp
    t_0 = (z + y) * x
    if (x <= (-1450.0d0)) then
        tmp = t_0
    else if (x <= 2.05d-240) then
        tmp = (x - 1.0d0) * z
    else if (x <= 6.4d-98) then
        tmp = y * x
    else if (x <= 1.45d-45) then
        tmp = -z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (z + y) * x;
	double tmp;
	if (x <= -1450.0) {
		tmp = t_0;
	} else if (x <= 2.05e-240) {
		tmp = (x - 1.0) * z;
	} else if (x <= 6.4e-98) {
		tmp = y * x;
	} else if (x <= 1.45e-45) {
		tmp = -z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (z + y) * x
	tmp = 0
	if x <= -1450.0:
		tmp = t_0
	elif x <= 2.05e-240:
		tmp = (x - 1.0) * z
	elif x <= 6.4e-98:
		tmp = y * x
	elif x <= 1.45e-45:
		tmp = -z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(z + y) * x)
	tmp = 0.0
	if (x <= -1450.0)
		tmp = t_0;
	elseif (x <= 2.05e-240)
		tmp = Float64(Float64(x - 1.0) * z);
	elseif (x <= 6.4e-98)
		tmp = Float64(y * x);
	elseif (x <= 1.45e-45)
		tmp = Float64(-z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (z + y) * x;
	tmp = 0.0;
	if (x <= -1450.0)
		tmp = t_0;
	elseif (x <= 2.05e-240)
		tmp = (x - 1.0) * z;
	elseif (x <= 6.4e-98)
		tmp = y * x;
	elseif (x <= 1.45e-45)
		tmp = -z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(z + y), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -1450.0], t$95$0, If[LessEqual[x, 2.05e-240], N[(N[(x - 1.0), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[x, 6.4e-98], N[(y * x), $MachinePrecision], If[LessEqual[x, 1.45e-45], (-z), t$95$0]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -1450 or 1.45e-45 < x

   1. Initial program 94.7%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 93.6

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

   5. Applied rewrites 93.6%

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

   if -1450 < x < 2.0500000000000001e-240

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 82.9

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

   5. Applied rewrites 82.9%

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

   if 2.0500000000000001e-240 < x < 6.4000000000000001e-98

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 67.6

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

   5. Applied rewrites 67.6%

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

   if 6.4000000000000001e-98 < x < 1.45e-45

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z\right)} \]

      2. lower-neg.f64 79.6

         \[\leadsto \color{blue}{-z} \]

   5. Applied rewrites 79.6%

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

Alternative 5: 78.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(z + y\right) \cdot x\\ \mathbf{if}\;x \leq -1.42 \cdot 10^{-8}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 2.05 \cdot 10^{-240}:\\ \;\;\;\;-z\\ \mathbf{elif}\;x \leq 6.4 \cdot 10^{-98}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;x \leq 1.45 \cdot 10^{-45}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (+ z y) x)))
   (if (<= x -1.42e-8)
     t_0
     (if (<= x 2.05e-240)
       (- z)
       (if (<= x 6.4e-98) (* y x) (if (<= x 1.45e-45) (- z) t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = (z + y) * x;
	double tmp;
	if (x <= -1.42e-8) {
		tmp = t_0;
	} else if (x <= 2.05e-240) {
		tmp = -z;
	} else if (x <= 6.4e-98) {
		tmp = y * x;
	} else if (x <= 1.45e-45) {
		tmp = -z;
	} 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) :: tmp
    t_0 = (z + y) * x
    if (x <= (-1.42d-8)) then
        tmp = t_0
    else if (x <= 2.05d-240) then
        tmp = -z
    else if (x <= 6.4d-98) then
        tmp = y * x
    else if (x <= 1.45d-45) then
        tmp = -z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (z + y) * x;
	double tmp;
	if (x <= -1.42e-8) {
		tmp = t_0;
	} else if (x <= 2.05e-240) {
		tmp = -z;
	} else if (x <= 6.4e-98) {
		tmp = y * x;
	} else if (x <= 1.45e-45) {
		tmp = -z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (z + y) * x
	tmp = 0
	if x <= -1.42e-8:
		tmp = t_0
	elif x <= 2.05e-240:
		tmp = -z
	elif x <= 6.4e-98:
		tmp = y * x
	elif x <= 1.45e-45:
		tmp = -z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(z + y) * x)
	tmp = 0.0
	if (x <= -1.42e-8)
		tmp = t_0;
	elseif (x <= 2.05e-240)
		tmp = Float64(-z);
	elseif (x <= 6.4e-98)
		tmp = Float64(y * x);
	elseif (x <= 1.45e-45)
		tmp = Float64(-z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (z + y) * x;
	tmp = 0.0;
	if (x <= -1.42e-8)
		tmp = t_0;
	elseif (x <= 2.05e-240)
		tmp = -z;
	elseif (x <= 6.4e-98)
		tmp = y * x;
	elseif (x <= 1.45e-45)
		tmp = -z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(z + y), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -1.42e-8], t$95$0, If[LessEqual[x, 2.05e-240], (-z), If[LessEqual[x, 6.4e-98], N[(y * x), $MachinePrecision], If[LessEqual[x, 1.45e-45], (-z), t$95$0]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.41999999999999998e-8 or 1.45e-45 < x

   1. Initial program 94.8%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 92.6

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

   5. Applied rewrites 92.6%

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

   if -1.41999999999999998e-8 < x < 2.0500000000000001e-240 or 6.4000000000000001e-98 < x < 1.45e-45

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z\right)} \]

      2. lower-neg.f64 82.2

         \[\leadsto \color{blue}{-z} \]

   5. Applied rewrites 82.2%

      \[\leadsto \color{blue}{-z} \]

   if 2.0500000000000001e-240 < x < 6.4000000000000001e-98

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 67.6

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

   5. Applied rewrites 67.6%

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

Alternative 6: 55.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.42 \cdot 10^{-8}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;x \leq 2.05 \cdot 10^{-240}:\\ \;\;\;\;-z\\ \mathbf{elif}\;x \leq 2.55 \cdot 10^{+150}:\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;z \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -1.42e-8)
   (* y x)
   (if (<= x 2.05e-240) (- z) (if (<= x 2.55e+150) (* y x) (* z x)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.42e-8) {
		tmp = y * x;
	} else if (x <= 2.05e-240) {
		tmp = -z;
	} else if (x <= 2.55e+150) {
		tmp = y * x;
	} else {
		tmp = z * x;
	}
	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) :: tmp
    if (x <= (-1.42d-8)) then
        tmp = y * x
    else if (x <= 2.05d-240) then
        tmp = -z
    else if (x <= 2.55d+150) then
        tmp = y * x
    else
        tmp = z * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.42e-8) {
		tmp = y * x;
	} else if (x <= 2.05e-240) {
		tmp = -z;
	} else if (x <= 2.55e+150) {
		tmp = y * x;
	} else {
		tmp = z * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -1.42e-8:
		tmp = y * x
	elif x <= 2.05e-240:
		tmp = -z
	elif x <= 2.55e+150:
		tmp = y * x
	else:
		tmp = z * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -1.42e-8)
		tmp = Float64(y * x);
	elseif (x <= 2.05e-240)
		tmp = Float64(-z);
	elseif (x <= 2.55e+150)
		tmp = Float64(y * x);
	else
		tmp = Float64(z * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -1.42e-8)
		tmp = y * x;
	elseif (x <= 2.05e-240)
		tmp = -z;
	elseif (x <= 2.55e+150)
		tmp = y * x;
	else
		tmp = z * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -1.42e-8], N[(y * x), $MachinePrecision], If[LessEqual[x, 2.05e-240], (-z), If[LessEqual[x, 2.55e+150], N[(y * x), $MachinePrecision], N[(z * x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.41999999999999998e-8 or 2.0500000000000001e-240 < x < 2.55000000000000005e150

   1. Initial program 97.2%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 56.4

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

   5. Applied rewrites 56.4%

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

   if -1.41999999999999998e-8 < x < 2.0500000000000001e-240

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z\right)} \]

      2. lower-neg.f64 82.6

         \[\leadsto \color{blue}{-z} \]

   5. Applied rewrites 82.6%

      \[\leadsto \color{blue}{-z} \]

   if 2.55000000000000005e150 < x

   1. Initial program 89.7%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 70.1

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

   5. Applied rewrites 70.1%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 70.1%

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

Alternative 7: 98.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(z + y\right) \cdot x\\ \mathbf{if}\;x \leq -1:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(y, x, -z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (+ z y) x)))
   (if (<= x -1.0) t_0 (if (<= x 1.0) (fma y x (- z)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (z + y) * x;
	double tmp;
	if (x <= -1.0) {
		tmp = t_0;
	} else if (x <= 1.0) {
		tmp = fma(y, x, -z);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(z + y) * x)
	tmp = 0.0
	if (x <= -1.0)
		tmp = t_0;
	elseif (x <= 1.0)
		tmp = fma(y, x, Float64(-z));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(z + y), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -1.0], t$95$0, If[LessEqual[x, 1.0], N[(y * x + (-z)), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -1 or 1 < x

   1. Initial program 94.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 99.4

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

   5. Applied rewrites 99.4%

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

   if -1 < x < 1

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 98.4

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

   5. Applied rewrites 98.4%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 98.4

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

   7. Applied rewrites 98.4%

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

Alternative 8: 60.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;z \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -1.0) (* z x) (if (<= x 1.0) (- z) (* z x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.0) {
		tmp = z * x;
	} else if (x <= 1.0) {
		tmp = -z;
	} else {
		tmp = z * x;
	}
	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) :: tmp
    if (x <= (-1.0d0)) then
        tmp = z * x
    else if (x <= 1.0d0) then
        tmp = -z
    else
        tmp = z * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.0) {
		tmp = z * x;
	} else if (x <= 1.0) {
		tmp = -z;
	} else {
		tmp = z * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -1.0:
		tmp = z * x
	elif x <= 1.0:
		tmp = -z
	else:
		tmp = z * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -1.0)
		tmp = Float64(z * x);
	elseif (x <= 1.0)
		tmp = Float64(-z);
	else
		tmp = Float64(z * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -1.0)
		tmp = z * x;
	elseif (x <= 1.0)
		tmp = -z;
	else
		tmp = z * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -1.0], N[(z * x), $MachinePrecision], If[LessEqual[x, 1.0], (-z), N[(z * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -1 or 1 < x

   1. Initial program 94.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 50.0

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

   5. Applied rewrites 50.0%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 49.4%

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

   if -1 < x < 1

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z\right)} \]

      2. lower-neg.f64 67.7

         \[\leadsto \color{blue}{-z} \]

   5. Applied rewrites 67.7%

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

Alternative 9: 36.2% accurate, 5.7× speedup

Math

\[\begin{array}{l} \\ -z \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

function code(x, y, z)
	return Float64(-z)
end

MATLAB

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

Wolfram

code[x_, y_, z_] := (-z)

Derivation

1. Initial program 97.2%

   \[x \cdot y + \left(x - 1\right) \cdot z \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(z\right)} \]

   2. lower-neg.f64 38.2

      \[\leadsto \color{blue}{-z} \]

5. Applied rewrites 38.2%

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

Alternative 10: 2.6% accurate, 17.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := z

Derivation

1. Initial program 97.2%

   \[x \cdot y + \left(x - 1\right) \cdot z \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(z\right)} \]

   2. lower-neg.f64 38.2

      \[\leadsto \color{blue}{-z} \]

5. Applied rewrites 38.2%

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

7. Applied rewrites 2.7%

   \[\leadsto z \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2024268 
(FPCore (x y z)
  :name "Graphics.Rendering.Chart.Drawing:drawTextsR from Chart-1.5.3"
  :precision binary64
  (+ (* x y) (* (- x 1.0) z)))