Linear.Projection:inversePerspective from linear-1.19.1.3, C

Percentage Accurate: 76.7% → 100.0%

Time: 7.6s

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: 76.7% 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

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 77.9%

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

   1. lift-+.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. lift-*.f64 N/A

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

   4. clear-num N/A

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

   5. lower-/.f64 N/A

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

   6. clear-num N/A

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

   7. lift-*.f64 N/A

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

   8. associate-/r* N/A

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

   9. clear-num N/A

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

   10. lift-*.f64 N/A

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

   11. associate-/l/ N/A

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

   12. associate-/r/ N/A

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

   13. lower-*.f64 N/A

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

   14. lower-/.f64 N/A

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

   15. div-inv N/A

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

   16. metadata-eval N/A

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

   17. metadata-eval N/A

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

   18. lower-*.f64 N/A

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

   19. metadata-eval 91.7

       \[\leadsto \frac{1}{\frac{y}{\left(x + y\right) \cdot \color{blue}{0.5}} \cdot x} \]

4. Applied egg-rr 91.7%

   \[\leadsto \color{blue}{\frac{1}{\frac{y}{\left(x + y\right) \cdot 0.5} \cdot x}} \]

5. Taylor expanded in y around inf

   \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{1}{x} + \frac{1}{2} \cdot \frac{1}{y}} \]
6. Step-by-step derivation

   1. lower-+.f64 N/A

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

   2. associate-*r/ N/A

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

   3. metadata-eval N/A

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

   4. lower-/.f64 N/A

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

   5. associate-*r/ N/A

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

   6. metadata-eval N/A

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

   7. lower-/.f64 100.0

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

7. Simplified 100.0%

   \[\leadsto \color{blue}{\frac{0.5}{x} + \frac{0.5}{y}} \]
8. Add Preprocessing

Alternative 2: 68.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.6 \cdot 10^{+105}:\\ \;\;\;\;\frac{0.5}{y}\\ \mathbf{elif}\;x \leq -2.7 \cdot 10^{-151}:\\ \;\;\;\;\frac{x + y}{y \cdot \left(x \cdot 2\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -1.6e+105)
   (/ 0.5 y)
   (if (<= x -2.7e-151) (/ (+ x y) (* y (* x 2.0))) (/ 0.5 x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.6e+105) {
		tmp = 0.5 / y;
	} else if (x <= -2.7e-151) {
		tmp = (x + y) / (y * (x * 2.0));
	} else {
		tmp = 0.5 / x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-1.6d+105)) then
        tmp = 0.5d0 / y
    else if (x <= (-2.7d-151)) then
        tmp = (x + y) / (y * (x * 2.0d0))
    else
        tmp = 0.5d0 / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -1.6e+105) {
		tmp = 0.5 / y;
	} else if (x <= -2.7e-151) {
		tmp = (x + y) / (y * (x * 2.0));
	} else {
		tmp = 0.5 / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -1.6e+105:
		tmp = 0.5 / y
	elif x <= -2.7e-151:
		tmp = (x + y) / (y * (x * 2.0))
	else:
		tmp = 0.5 / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1.6e+105)
		tmp = Float64(0.5 / y);
	elseif (x <= -2.7e-151)
		tmp = Float64(Float64(x + y) / Float64(y * Float64(x * 2.0)));
	else
		tmp = Float64(0.5 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -1.6e+105)
		tmp = 0.5 / y;
	elseif (x <= -2.7e-151)
		tmp = (x + y) / (y * (x * 2.0));
	else
		tmp = 0.5 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1.6e+105], N[(0.5 / y), $MachinePrecision], If[LessEqual[x, -2.7e-151], N[(N[(x + y), $MachinePrecision] / N[(y * N[(x * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(0.5 / x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.6e105

   1. Initial program 60.4%

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

   3. Taylor expanded in x around inf

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

      1. lower-/.f64 80.3

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

   5. Simplified 80.3%

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

   if -1.6e105 < x < -2.70000000000000007e-151

   1. Initial program 95.4%

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

   if -2.70000000000000007e-151 < x

   1. Initial program 76.6%

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

   3. Taylor expanded in x around 0

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

      1. lower-/.f64 67.6

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

   5. Simplified 67.6%

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

4. Final simplification 75.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.6 \cdot 10^{+105}:\\ \;\;\;\;\frac{0.5}{y}\\ \mathbf{elif}\;x \leq -2.7 \cdot 10^{-151}:\\ \;\;\;\;\frac{x + y}{y \cdot \left(x \cdot 2\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 68.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5.8 \cdot 10^{+87}:\\ \;\;\;\;\frac{0.5}{y}\\ \mathbf{elif}\;x \leq -2.7 \cdot 10^{-151}:\\ \;\;\;\;\left(x + y\right) \cdot \frac{0.5}{x \cdot y}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -5.8e+87)
   (/ 0.5 y)
   (if (<= x -2.7e-151) (* (+ x y) (/ 0.5 (* x y))) (/ 0.5 x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -5.8e+87) {
		tmp = 0.5 / y;
	} else if (x <= -2.7e-151) {
		tmp = (x + y) * (0.5 / (x * y));
	} else {
		tmp = 0.5 / x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-5.8d+87)) then
        tmp = 0.5d0 / y
    else if (x <= (-2.7d-151)) then
        tmp = (x + y) * (0.5d0 / (x * y))
    else
        tmp = 0.5d0 / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -5.8e+87) {
		tmp = 0.5 / y;
	} else if (x <= -2.7e-151) {
		tmp = (x + y) * (0.5 / (x * y));
	} else {
		tmp = 0.5 / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -5.8e+87:
		tmp = 0.5 / y
	elif x <= -2.7e-151:
		tmp = (x + y) * (0.5 / (x * y))
	else:
		tmp = 0.5 / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -5.8e+87)
		tmp = Float64(0.5 / y);
	elseif (x <= -2.7e-151)
		tmp = Float64(Float64(x + y) * Float64(0.5 / Float64(x * y)));
	else
		tmp = Float64(0.5 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -5.8e+87)
		tmp = 0.5 / y;
	elseif (x <= -2.7e-151)
		tmp = (x + y) * (0.5 / (x * y));
	else
		tmp = 0.5 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -5.8e+87], N[(0.5 / y), $MachinePrecision], If[LessEqual[x, -2.7e-151], N[(N[(x + y), $MachinePrecision] * N[(0.5 / N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(0.5 / x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -5.7999999999999996e87

   1. Initial program 63.1%

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

   3. Taylor expanded in x around inf

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

      1. lower-/.f64 79.5

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

   5. Simplified 79.5%

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

   if -5.7999999999999996e87 < x < -2.70000000000000007e-151

   1. Initial program 95.1%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. div-inv N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. *-commutative N/A

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

      10. associate-*l* N/A

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

      11. associate-/r* N/A

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

      12. metadata-eval N/A

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

      13. metadata-eval N/A

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

      14. lower-/.f64 N/A

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

      15. metadata-eval N/A

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

      16. lower-*.f64 93.8

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

   4. Applied egg-rr 93.8%

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

   if -2.70000000000000007e-151 < x

   1. Initial program 76.6%

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

   3. Taylor expanded in x around 0

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

      1. lower-/.f64 67.6

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

   5. Simplified 67.6%

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

4. Final simplification 74.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5.8 \cdot 10^{+87}:\\ \;\;\;\;\frac{0.5}{y}\\ \mathbf{elif}\;x \leq -2.7 \cdot 10^{-151}:\\ \;\;\;\;\left(x + y\right) \cdot \frac{0.5}{x \cdot y}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 62.5% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.1 \cdot 10^{-41}:\\ \;\;\;\;\frac{0.5}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= x -1.1e-41) (/ 0.5 y) (/ 0.5 x)))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.1e-41) {
		tmp = 0.5 / y;
	} else {
		tmp = 0.5 / x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-1.1d-41)) then
        tmp = 0.5d0 / y
    else
        tmp = 0.5d0 / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -1.1e-41) {
		tmp = 0.5 / y;
	} else {
		tmp = 0.5 / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -1.1e-41:
		tmp = 0.5 / y
	else:
		tmp = 0.5 / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1.1e-41)
		tmp = Float64(0.5 / y);
	else
		tmp = Float64(0.5 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -1.1e-41)
		tmp = 0.5 / y;
	else
		tmp = 0.5 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1.1e-41], N[(0.5 / y), $MachinePrecision], N[(0.5 / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.1e-41

   1. Initial program 77.5%

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

   3. Taylor expanded in x around inf

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

      1. lower-/.f64 67.9

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

   5. Simplified 67.9%

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

   if -1.1e-41 < x

   1. Initial program 78.0%

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

   3. Taylor expanded in x around 0

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

      1. lower-/.f64 67.6

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

   5. Simplified 67.6%

      \[\leadsto \color{blue}{\frac{0.5}{x}} \]
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 77.9%

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

3. Taylor expanded in x around 0

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

   1. lower-/.f64 57.2

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

5. Simplified 57.2%

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

Developer Target 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024214 
(FPCore (x y)
  :name "Linear.Projection:inversePerspective from linear-1.19.1.3, C"
  :precision binary64

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

  (/ (+ x y) (* (* x 2.0) y)))