Linear.Projection:inversePerspective from linear-1.19.1.3, B

Percentage Accurate: 76.2% → 100.0%

Time: 4.8s

Alternatives: 3

Speedup: 1.3×

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: 76.2% 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, 1.3× 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 80.8%

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

   1. remove-double-neg 80.8%

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

   2. distribute-rgt-neg-out 80.8%

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

   3. distribute-frac-neg2 80.8%

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

   4. neg-mul-1 80.8%

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

   5. div-sub 80.3%

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

   6. distribute-lft-out-- 80.3%

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

   7. neg-mul-1 80.3%

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

   8. distribute-frac-neg2 80.3%

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

   9. distribute-rgt-neg-out 80.3%

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

   10. remove-double-neg 80.3%

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

   11. cancel-sign-sub-inv 80.3%

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

   12. associate-/r* 86.3%

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

   13. associate-/r* 86.3%

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

   14. *-inverses 86.3%

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

   15. metadata-eval 86.3%

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

   16. metadata-eval 86.3%

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

   17. metadata-eval 86.3%

       \[\leadsto \frac{\color{blue}{0.5}}{y} + \left(--1\right) \cdot \frac{y}{\left(x \cdot 2\right) \cdot \left(-y\right)} \]

   18. metadata-eval 86.3%

       \[\leadsto \frac{0.5}{y} + \color{blue}{1} \cdot \frac{y}{\left(x \cdot 2\right) \cdot \left(-y\right)} \]

3. Simplified 100.0%

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

Alternative 2: 73.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.3 \cdot 10^{-95} \lor \neg \left(y \leq 2.6 \cdot 10^{+35}\right):\\ \;\;\;\;\frac{-0.5}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -2.3e-95) (not (<= y 2.6e+35))) (/ -0.5 x) (/ 0.5 y)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -2.3e-95) || !(y <= 2.6e+35)) {
		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 ((y <= (-2.3d-95)) .or. (.not. (y <= 2.6d+35))) 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 ((y <= -2.3e-95) || !(y <= 2.6e+35)) {
		tmp = -0.5 / x;
	} else {
		tmp = 0.5 / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -2.3e-95) or not (y <= 2.6e+35):
		tmp = -0.5 / x
	else:
		tmp = 0.5 / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -2.3e-95) || !(y <= 2.6e+35))
		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 ((y <= -2.3e-95) || ~((y <= 2.6e+35)))
		tmp = -0.5 / x;
	else
		tmp = 0.5 / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -2.3e-95], N[Not[LessEqual[y, 2.6e+35]], $MachinePrecision]], N[(-0.5 / x), $MachinePrecision], N[(0.5 / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -2.29999999999999999e-95 or 2.60000000000000007e35 < y

   1. Initial program 78.8%

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

      1. remove-double-neg 78.8%

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

      2. distribute-rgt-neg-out 78.8%

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

      3. distribute-frac-neg2 78.8%

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

      4. neg-mul-1 78.8%

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

      5. div-sub 78.7%

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

      6. distribute-lft-out-- 78.7%

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

      7. neg-mul-1 78.7%

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

      8. distribute-frac-neg2 78.7%

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

      9. distribute-rgt-neg-out 78.7%

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

      10. remove-double-neg 78.7%

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

      11. cancel-sign-sub-inv 78.7%

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

      12. associate-/r* 89.8%

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

      13. associate-/r* 89.8%

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

      14. *-inverses 89.8%

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

      15. metadata-eval 89.8%

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

      16. metadata-eval 89.8%

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

      17. metadata-eval 89.8%

          \[\leadsto \frac{\color{blue}{0.5}}{y} + \left(--1\right) \cdot \frac{y}{\left(x \cdot 2\right) \cdot \left(-y\right)} \]

      18. metadata-eval 89.8%

          \[\leadsto \frac{0.5}{y} + \color{blue}{1} \cdot \frac{y}{\left(x \cdot 2\right) \cdot \left(-y\right)} \]

   3. Simplified 100.0%

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

   5. Taylor expanded in y around inf 77.1%

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

   if -2.29999999999999999e-95 < y < 2.60000000000000007e35

   1. Initial program 82.6%

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

      1. remove-double-neg 82.6%

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

      2. distribute-rgt-neg-out 82.6%

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

      3. distribute-frac-neg2 82.6%

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

      4. neg-mul-1 82.6%

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

      5. div-sub 81.7%

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

      6. distribute-lft-out-- 81.7%

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

      7. neg-mul-1 81.7%

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

      8. distribute-frac-neg2 81.7%

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

      9. distribute-rgt-neg-out 81.7%

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

      10. remove-double-neg 81.7%

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

      11. cancel-sign-sub-inv 81.7%

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

      12. associate-/r* 83.1%

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

      13. associate-/r* 83.1%

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

      14. *-inverses 83.1%

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

      15. metadata-eval 83.1%

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

      16. metadata-eval 83.1%

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

      17. metadata-eval 83.1%

          \[\leadsto \frac{\color{blue}{0.5}}{y} + \left(--1\right) \cdot \frac{y}{\left(x \cdot 2\right) \cdot \left(-y\right)} \]

      18. metadata-eval 83.1%

          \[\leadsto \frac{0.5}{y} + \color{blue}{1} \cdot \frac{y}{\left(x \cdot 2\right) \cdot \left(-y\right)} \]

   3. Simplified 100.0%

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

   5. Taylor expanded in y around 0 85.2%

      \[\leadsto \color{blue}{\frac{0.5}{y}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 81.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.3 \cdot 10^{-95} \lor \neg \left(y \leq 2.6 \cdot 10^{+35}\right):\\ \;\;\;\;\frac{-0.5}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{y}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 50.6% accurate, 3.0× 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 80.8%

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

   1. remove-double-neg 80.8%

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

   2. distribute-rgt-neg-out 80.8%

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

   3. distribute-frac-neg2 80.8%

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

   4. neg-mul-1 80.8%

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

   5. div-sub 80.3%

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

   6. distribute-lft-out-- 80.3%

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

   7. neg-mul-1 80.3%

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

   8. distribute-frac-neg2 80.3%

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

   9. distribute-rgt-neg-out 80.3%

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

   10. remove-double-neg 80.3%

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

   11. cancel-sign-sub-inv 80.3%

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

   12. associate-/r* 86.3%

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

   13. associate-/r* 86.3%

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

   14. *-inverses 86.3%

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

   15. metadata-eval 86.3%

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

   16. metadata-eval 86.3%

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

   17. metadata-eval 86.3%

       \[\leadsto \frac{\color{blue}{0.5}}{y} + \left(--1\right) \cdot \frac{y}{\left(x \cdot 2\right) \cdot \left(-y\right)} \]

   18. metadata-eval 86.3%

       \[\leadsto \frac{0.5}{y} + \color{blue}{1} \cdot \frac{y}{\left(x \cdot 2\right) \cdot \left(-y\right)} \]

3. Simplified 100.0%

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

5. Taylor expanded in y around inf 45.4%

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

Developer Target 1: 100.0% accurate, 1.3× 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 2024163 
(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)))