Linear.Projection:inversePerspective from linear-1.19.1.3, B

Percentage Accurate: 77.3% → 100.0%

Time: 6.7s

Alternatives: 5

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x - y}{\left(x \cdot 2\right) \cdot y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (- x y) (* (* x 2.0) y)))

C

double code(double x, double y) {
	return (x - y) / ((x * 2.0) * y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x - y) / ((x * 2.0d0) * y)
end function

Java

public static double code(double x, double y) {
	return (x - y) / ((x * 2.0) * y);
}

Python

def code(x, y):
	return (x - y) / ((x * 2.0) * y)

Julia

function code(x, y)
	return Float64(Float64(x - y) / Float64(Float64(x * 2.0) * y))
end

MATLAB

function tmp = code(x, y)
	tmp = (x - y) / ((x * 2.0) * y);
end

Wolfram

code[x_, y_] := N[(N[(x - y), $MachinePrecision] / N[(N[(x * 2.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]

Initial Program: 77.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - y}{\left(x \cdot 2\right) \cdot y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (- x y) (* (* x 2.0) y)))

C

double code(double x, double y) {
	return (x - y) / ((x * 2.0) * y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x - y) / ((x * 2.0d0) * y)
end function

Java

public static double code(double x, double y) {
	return (x - y) / ((x * 2.0) * y);
}

Python

def code(x, y):
	return (x - y) / ((x * 2.0) * y)

Julia

function code(x, y)
	return Float64(Float64(x - y) / Float64(Float64(x * 2.0) * y))
end

MATLAB

function tmp = code(x, y)
	tmp = (x - y) / ((x * 2.0) * y);
end

Wolfram

code[x_, y_] := N[(N[(x - y), $MachinePrecision] / N[(N[(x * 2.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{0.5}{y} - \frac{0.5}{x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- (/ 0.5 y) (/ 0.5 x)))

C

double code(double x, double y) {
	return (0.5 / y) - (0.5 / x);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (0.5d0 / y) - (0.5d0 / x)
end function

Java

public static double code(double x, double y) {
	return (0.5 / y) - (0.5 / x);
}

Python

def code(x, y):
	return (0.5 / y) - (0.5 / x)

Julia

function code(x, y)
	return Float64(Float64(0.5 / y) - Float64(0.5 / x))
end

MATLAB

function tmp = code(x, y)
	tmp = (0.5 / y) - (0.5 / x);
end

Wolfram

code[x_, y_] := N[(N[(0.5 / y), $MachinePrecision] - N[(0.5 / x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 81.0%

   \[\frac{x - y}{\left(x \cdot 2\right) \cdot y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. associate-/l/ N/A

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

   2. div-sub N/A

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

   3. *-inverses N/A

      \[\leadsto \frac{\frac{x}{y} - \color{blue}{1}}{x \cdot 2} \]

   4. sub-div N/A

      \[\leadsto \color{blue}{\frac{\frac{x}{y}}{x \cdot 2} - \frac{1}{x \cdot 2}} \]

   5. associate-/l/ N/A

      \[\leadsto \color{blue}{\frac{x}{\left(x \cdot 2\right) \cdot y}} - \frac{1}{x \cdot 2} \]

   6. --lowering--.f64 N/A

      \[\leadsto \color{blue}{\frac{x}{\left(x \cdot 2\right) \cdot y} - \frac{1}{x \cdot 2}} \]

   7. associate-/r* N/A

      \[\leadsto \color{blue}{\frac{\frac{x}{x \cdot 2}}{y}} - \frac{1}{x \cdot 2} \]

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

      \[\leadsto \color{blue}{\frac{\frac{x}{x \cdot 2}}{y}} - \frac{1}{x \cdot 2} \]

   9. associate-/r* N/A

      \[\leadsto \frac{\color{blue}{\frac{\frac{x}{x}}{2}}}{y} - \frac{1}{x \cdot 2} \]

   10. *-inverses N/A

       \[\leadsto \frac{\frac{\color{blue}{1}}{2}}{y} - \frac{1}{x \cdot 2} \]

   11. metadata-eval N/A

       \[\leadsto \frac{\color{blue}{\frac{1}{2}}}{y} - \frac{1}{x \cdot 2} \]

   12. *-commutative N/A

       \[\leadsto \frac{\frac{1}{2}}{y} - \frac{1}{\color{blue}{2 \cdot x}} \]

   13. associate-/r* N/A

       \[\leadsto \frac{\frac{1}{2}}{y} - \color{blue}{\frac{\frac{1}{2}}{x}} \]

   14. *-inverses N/A

       \[\leadsto \frac{\frac{1}{2}}{y} - \frac{\frac{\color{blue}{\frac{x}{x}}}{2}}{x} \]

   15. associate-/r* N/A

       \[\leadsto \frac{\frac{1}{2}}{y} - \frac{\color{blue}{\frac{x}{x \cdot 2}}}{x} \]

   16. /-lowering-/.f64 N/A

       \[\leadsto \frac{\frac{1}{2}}{y} - \color{blue}{\frac{\frac{x}{x \cdot 2}}{x}} \]

   17. associate-/r* N/A

       \[\leadsto \frac{\frac{1}{2}}{y} - \frac{\color{blue}{\frac{\frac{x}{x}}{2}}}{x} \]

   18. *-inverses N/A

       \[\leadsto \frac{\frac{1}{2}}{y} - \frac{\frac{\color{blue}{1}}{2}}{x} \]

   19. metadata-eval 100.0

       \[\leadsto \frac{0.5}{y} - \frac{\color{blue}{0.5}}{x} \]

4. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\frac{0.5}{y} - \frac{0.5}{x}} \]
5. Add Preprocessing

Alternative 2: 87.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x - y}{y \cdot \left(x \cdot 2\right)}\\ \mathbf{if}\;x \leq -2.35 \cdot 10^{+163}:\\ \;\;\;\;\frac{0.5}{y}\\ \mathbf{elif}\;x \leq -1.25 \cdot 10^{-114}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 8 \cdot 10^{-206}:\\ \;\;\;\;\frac{-0.5}{x}\\ \mathbf{elif}\;x \leq 3.1 \cdot 10^{+178}:\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (- x y) (* y (* x 2.0)))))
   (if (<= x -2.35e+163)
     (/ 0.5 y)
     (if (<= x -1.25e-114)
       t_0
       (if (<= x 8e-206) (/ -0.5 x) (if (<= x 3.1e+178) t_0 (/ 0.5 y)))))))

C

double code(double x, double y) {
	double t_0 = (x - y) / (y * (x * 2.0));
	double tmp;
	if (x <= -2.35e+163) {
		tmp = 0.5 / y;
	} else if (x <= -1.25e-114) {
		tmp = t_0;
	} else if (x <= 8e-206) {
		tmp = -0.5 / x;
	} else if (x <= 3.1e+178) {
		tmp = t_0;
	} else {
		tmp = 0.5 / y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x - y) / (y * (x * 2.0d0))
    if (x <= (-2.35d+163)) then
        tmp = 0.5d0 / y
    else if (x <= (-1.25d-114)) then
        tmp = t_0
    else if (x <= 8d-206) then
        tmp = (-0.5d0) / x
    else if (x <= 3.1d+178) then
        tmp = t_0
    else
        tmp = 0.5d0 / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (x - y) / (y * (x * 2.0));
	double tmp;
	if (x <= -2.35e+163) {
		tmp = 0.5 / y;
	} else if (x <= -1.25e-114) {
		tmp = t_0;
	} else if (x <= 8e-206) {
		tmp = -0.5 / x;
	} else if (x <= 3.1e+178) {
		tmp = t_0;
	} else {
		tmp = 0.5 / y;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (x - y) / (y * (x * 2.0))
	tmp = 0
	if x <= -2.35e+163:
		tmp = 0.5 / y
	elif x <= -1.25e-114:
		tmp = t_0
	elif x <= 8e-206:
		tmp = -0.5 / x
	elif x <= 3.1e+178:
		tmp = t_0
	else:
		tmp = 0.5 / y
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(x - y) / Float64(y * Float64(x * 2.0)))
	tmp = 0.0
	if (x <= -2.35e+163)
		tmp = Float64(0.5 / y);
	elseif (x <= -1.25e-114)
		tmp = t_0;
	elseif (x <= 8e-206)
		tmp = Float64(-0.5 / x);
	elseif (x <= 3.1e+178)
		tmp = t_0;
	else
		tmp = Float64(0.5 / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (x - y) / (y * (x * 2.0));
	tmp = 0.0;
	if (x <= -2.35e+163)
		tmp = 0.5 / y;
	elseif (x <= -1.25e-114)
		tmp = t_0;
	elseif (x <= 8e-206)
		tmp = -0.5 / x;
	elseif (x <= 3.1e+178)
		tmp = t_0;
	else
		tmp = 0.5 / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(x - y), $MachinePrecision] / N[(y * N[(x * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.35e+163], N[(0.5 / y), $MachinePrecision], If[LessEqual[x, -1.25e-114], t$95$0, If[LessEqual[x, 8e-206], N[(-0.5 / x), $MachinePrecision], If[LessEqual[x, 3.1e+178], t$95$0, N[(0.5 / y), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -2.35000000000000009e163 or 3.09999999999999991e178 < x

   1. Initial program 62.1%

      \[\frac{x - y}{\left(x \cdot 2\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2}}{y}} \]
   4. Step-by-step derivation

      1. /-lowering-/.f64 92.0

         \[\leadsto \color{blue}{\frac{0.5}{y}} \]

   5. Simplified 92.0%

      \[\leadsto \color{blue}{\frac{0.5}{y}} \]

   if -2.35000000000000009e163 < x < -1.24999999999999997e-114 or 8.00000000000000023e-206 < x < 3.09999999999999991e178

   1. Initial program 90.8%

      \[\frac{x - y}{\left(x \cdot 2\right) \cdot y} \]
   2. Add Preprocessing

   if -1.24999999999999997e-114 < x < 8.00000000000000023e-206

   1. Initial program 77.1%

      \[\frac{x - y}{\left(x \cdot 2\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{\frac{-1}{2}}{x}} \]
   4. Step-by-step derivation

      1. /-lowering-/.f64 92.9

         \[\leadsto \color{blue}{\frac{-0.5}{x}} \]

   5. Simplified 92.9%

      \[\leadsto \color{blue}{\frac{-0.5}{x}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 91.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.35 \cdot 10^{+163}:\\ \;\;\;\;\frac{0.5}{y}\\ \mathbf{elif}\;x \leq -1.25 \cdot 10^{-114}:\\ \;\;\;\;\frac{x - y}{y \cdot \left(x \cdot 2\right)}\\ \mathbf{elif}\;x \leq 8 \cdot 10^{-206}:\\ \;\;\;\;\frac{-0.5}{x}\\ \mathbf{elif}\;x \leq 3.1 \cdot 10^{+178}:\\ \;\;\;\;\frac{x - y}{y \cdot \left(x \cdot 2\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 87.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x - y\right) \cdot \frac{0.5}{y \cdot x}\\ \mathbf{if}\;x \leq -2.35 \cdot 10^{+163}:\\ \;\;\;\;\frac{0.5}{y}\\ \mathbf{elif}\;x \leq -1.25 \cdot 10^{-114}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{-206}:\\ \;\;\;\;\frac{-0.5}{x}\\ \mathbf{elif}\;x \leq 3.1 \cdot 10^{+178}:\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (- x y) (/ 0.5 (* y x)))))
   (if (<= x -2.35e+163)
     (/ 0.5 y)
     (if (<= x -1.25e-114)
       t_0
       (if (<= x 8.5e-206) (/ -0.5 x) (if (<= x 3.1e+178) t_0 (/ 0.5 y)))))))

C

double code(double x, double y) {
	double t_0 = (x - y) * (0.5 / (y * x));
	double tmp;
	if (x <= -2.35e+163) {
		tmp = 0.5 / y;
	} else if (x <= -1.25e-114) {
		tmp = t_0;
	} else if (x <= 8.5e-206) {
		tmp = -0.5 / x;
	} else if (x <= 3.1e+178) {
		tmp = t_0;
	} else {
		tmp = 0.5 / y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x - y) * (0.5d0 / (y * x))
    if (x <= (-2.35d+163)) then
        tmp = 0.5d0 / y
    else if (x <= (-1.25d-114)) then
        tmp = t_0
    else if (x <= 8.5d-206) then
        tmp = (-0.5d0) / x
    else if (x <= 3.1d+178) then
        tmp = t_0
    else
        tmp = 0.5d0 / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (x - y) * (0.5 / (y * x));
	double tmp;
	if (x <= -2.35e+163) {
		tmp = 0.5 / y;
	} else if (x <= -1.25e-114) {
		tmp = t_0;
	} else if (x <= 8.5e-206) {
		tmp = -0.5 / x;
	} else if (x <= 3.1e+178) {
		tmp = t_0;
	} else {
		tmp = 0.5 / y;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (x - y) * (0.5 / (y * x))
	tmp = 0
	if x <= -2.35e+163:
		tmp = 0.5 / y
	elif x <= -1.25e-114:
		tmp = t_0
	elif x <= 8.5e-206:
		tmp = -0.5 / x
	elif x <= 3.1e+178:
		tmp = t_0
	else:
		tmp = 0.5 / y
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(x - y) * Float64(0.5 / Float64(y * x)))
	tmp = 0.0
	if (x <= -2.35e+163)
		tmp = Float64(0.5 / y);
	elseif (x <= -1.25e-114)
		tmp = t_0;
	elseif (x <= 8.5e-206)
		tmp = Float64(-0.5 / x);
	elseif (x <= 3.1e+178)
		tmp = t_0;
	else
		tmp = Float64(0.5 / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (x - y) * (0.5 / (y * x));
	tmp = 0.0;
	if (x <= -2.35e+163)
		tmp = 0.5 / y;
	elseif (x <= -1.25e-114)
		tmp = t_0;
	elseif (x <= 8.5e-206)
		tmp = -0.5 / x;
	elseif (x <= 3.1e+178)
		tmp = t_0;
	else
		tmp = 0.5 / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(x - y), $MachinePrecision] * N[(0.5 / N[(y * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.35e+163], N[(0.5 / y), $MachinePrecision], If[LessEqual[x, -1.25e-114], t$95$0, If[LessEqual[x, 8.5e-206], N[(-0.5 / x), $MachinePrecision], If[LessEqual[x, 3.1e+178], t$95$0, N[(0.5 / y), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -2.35000000000000009e163 or 3.09999999999999991e178 < x

   1. Initial program 62.1%

      \[\frac{x - y}{\left(x \cdot 2\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2}}{y}} \]
   4. Step-by-step derivation

      1. /-lowering-/.f64 92.0

         \[\leadsto \color{blue}{\frac{0.5}{y}} \]

   5. Simplified 92.0%

      \[\leadsto \color{blue}{\frac{0.5}{y}} \]

   if -2.35000000000000009e163 < x < -1.24999999999999997e-114 or 8.5000000000000005e-206 < x < 3.09999999999999991e178

   1. Initial program 90.8%

      \[\frac{x - y}{\left(x \cdot 2\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. clear-num N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\left(x \cdot 2\right) \cdot y}{x - y}}} \]

      2. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{1}{\left(x \cdot 2\right) \cdot y} \cdot \left(x - y\right)} \]

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

         \[\leadsto \color{blue}{\frac{1}{\left(x \cdot 2\right) \cdot y} \cdot \left(x - y\right)} \]

      4. *-commutative N/A

         \[\leadsto \frac{1}{\color{blue}{\left(2 \cdot x\right)} \cdot y} \cdot \left(x - y\right) \]

      5. associate-*l* N/A

         \[\leadsto \frac{1}{\color{blue}{2 \cdot \left(x \cdot y\right)}} \cdot \left(x - y\right) \]

      6. associate-/r* N/A

         \[\leadsto \color{blue}{\frac{\frac{1}{2}}{x \cdot y}} \cdot \left(x - y\right) \]

      7. *-inverses N/A

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

      8. associate-/r* N/A

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

      9. /-lowering-/.f64 N/A

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

      10. associate-/r* N/A

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

      11. *-inverses N/A

          \[\leadsto \frac{\frac{\color{blue}{1}}{2}}{x \cdot y} \cdot \left(x - y\right) \]

      12. metadata-eval N/A

          \[\leadsto \frac{\color{blue}{\frac{1}{2}}}{x \cdot y} \cdot \left(x - y\right) \]

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

          \[\leadsto \frac{\frac{1}{2}}{\color{blue}{x \cdot y}} \cdot \left(x - y\right) \]

      14. --lowering--.f64 90.6

          \[\leadsto \frac{0.5}{x \cdot y} \cdot \color{blue}{\left(x - y\right)} \]

   4. Applied egg-rr 90.6%

      \[\leadsto \color{blue}{\frac{0.5}{x \cdot y} \cdot \left(x - y\right)} \]

   if -1.24999999999999997e-114 < x < 8.5000000000000005e-206

   1. Initial program 77.1%

      \[\frac{x - y}{\left(x \cdot 2\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{\frac{-1}{2}}{x}} \]
   4. Step-by-step derivation

      1. /-lowering-/.f64 92.9

         \[\leadsto \color{blue}{\frac{-0.5}{x}} \]

   5. Simplified 92.9%

      \[\leadsto \color{blue}{\frac{-0.5}{x}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 91.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.35 \cdot 10^{+163}:\\ \;\;\;\;\frac{0.5}{y}\\ \mathbf{elif}\;x \leq -1.25 \cdot 10^{-114}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{0.5}{y \cdot x}\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{-206}:\\ \;\;\;\;\frac{-0.5}{x}\\ \mathbf{elif}\;x \leq 3.1 \cdot 10^{+178}:\\ \;\;\;\;\left(x - y\right) \cdot \frac{0.5}{y \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 74.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.9 \cdot 10^{-23}:\\ \;\;\;\;\frac{0.5}{y}\\ \mathbf{elif}\;x \leq 1.08 \cdot 10^{-7}:\\ \;\;\;\;\frac{-0.5}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -3.9e-23) (/ 0.5 y) (if (<= x 1.08e-7) (/ -0.5 x) (/ 0.5 y))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -3.9e-23) {
		tmp = 0.5 / y;
	} else if (x <= 1.08e-7) {
		tmp = -0.5 / x;
	} else {
		tmp = 0.5 / y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-3.9d-23)) then
        tmp = 0.5d0 / y
    else if (x <= 1.08d-7) then
        tmp = (-0.5d0) / x
    else
        tmp = 0.5d0 / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -3.9e-23) {
		tmp = 0.5 / y;
	} else if (x <= 1.08e-7) {
		tmp = -0.5 / x;
	} else {
		tmp = 0.5 / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -3.9e-23:
		tmp = 0.5 / y
	elif x <= 1.08e-7:
		tmp = -0.5 / x
	else:
		tmp = 0.5 / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -3.9e-23)
		tmp = Float64(0.5 / y);
	elseif (x <= 1.08e-7)
		tmp = Float64(-0.5 / x);
	else
		tmp = Float64(0.5 / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -3.9e-23)
		tmp = 0.5 / y;
	elseif (x <= 1.08e-7)
		tmp = -0.5 / x;
	else
		tmp = 0.5 / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -3.9e-23], N[(0.5 / y), $MachinePrecision], If[LessEqual[x, 1.08e-7], N[(-0.5 / x), $MachinePrecision], N[(0.5 / y), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -3.9e-23 or 1.08000000000000001e-7 < x

   1. Initial program 77.1%

      \[\frac{x - y}{\left(x \cdot 2\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2}}{y}} \]
   4. Step-by-step derivation

      1. /-lowering-/.f64 82.7

         \[\leadsto \color{blue}{\frac{0.5}{y}} \]

   5. Simplified 82.7%

      \[\leadsto \color{blue}{\frac{0.5}{y}} \]

   if -3.9e-23 < x < 1.08000000000000001e-7

   1. Initial program 84.6%

      \[\frac{x - y}{\left(x \cdot 2\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{\frac{-1}{2}}{x}} \]
   4. Step-by-step derivation

      1. /-lowering-/.f64 79.8

         \[\leadsto \color{blue}{\frac{-0.5}{x}} \]

   5. Simplified 79.8%

      \[\leadsto \color{blue}{\frac{-0.5}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 50.5% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \frac{-0.5}{x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ -0.5 x))

C

double code(double x, double y) {
	return -0.5 / x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (-0.5d0) / x
end function

Java

public static double code(double x, double y) {
	return -0.5 / x;
}

Python

def code(x, y):
	return -0.5 / x

Julia

function code(x, y)
	return Float64(-0.5 / x)
end

MATLAB

function tmp = code(x, y)
	tmp = -0.5 / x;
end

Wolfram

code[x_, y_] := N[(-0.5 / x), $MachinePrecision]

Derivation

1. Initial program 81.0%

   \[\frac{x - y}{\left(x \cdot 2\right) \cdot y} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{\frac{-1}{2}}{x}} \]
4. Step-by-step derivation

   1. /-lowering-/.f64 50.9

      \[\leadsto \color{blue}{\frac{-0.5}{x}} \]

5. Simplified 50.9%

   \[\leadsto \color{blue}{\frac{-0.5}{x}} \]
6. Add Preprocessing

Developer Target 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{0.5}{y} - \frac{0.5}{x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- (/ 0.5 y) (/ 0.5 x)))

C

double code(double x, double y) {
	return (0.5 / y) - (0.5 / x);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (0.5d0 / y) - (0.5d0 / x)
end function

Java

public static double code(double x, double y) {
	return (0.5 / y) - (0.5 / x);
}

Python

def code(x, y):
	return (0.5 / y) - (0.5 / x)

Julia

function code(x, y)
	return Float64(Float64(0.5 / y) - Float64(0.5 / x))
end

MATLAB

function tmp = code(x, y)
	tmp = (0.5 / y) - (0.5 / x);
end

Wolfram

code[x_, y_] := N[(N[(0.5 / y), $MachinePrecision] - N[(0.5 / x), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024199 
(FPCore (x y)
  :name "Linear.Projection:inversePerspective from linear-1.19.1.3, B"
  :precision binary64

  :alt
  (! :herbie-platform default (- (/ 1/2 y) (/ 1/2 x)))

  (/ (- x y) (* (* x 2.0) y)))