Linear.Projection:perspective from linear-1.19.1.3, B

Percentage Accurate: 76.5% → 93.5%

Time: 6.1s

Alternatives: 4

Speedup: 0.7×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 76.5% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 93.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \frac{y \cdot 2}{x - y}\\ \mathbf{if}\;y \leq -2.6 \cdot 10^{-121}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 2.3 \cdot 10^{-123}:\\ \;\;\;\;2 \cdot \mathsf{fma}\left(y, \frac{y}{x}, y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* x (/ (* y 2.0) (- x y)))))
   (if (<= y -2.6e-121)
     t_0
     (if (<= y 2.3e-123) (* 2.0 (fma y (/ y x) y)) t_0))))

C

double code(double x, double y) {
	double t_0 = x * ((y * 2.0) / (x - y));
	double tmp;
	if (y <= -2.6e-121) {
		tmp = t_0;
	} else if (y <= 2.3e-123) {
		tmp = 2.0 * fma(y, (y / x), y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(x * Float64(Float64(y * 2.0) / Float64(x - y)))
	tmp = 0.0
	if (y <= -2.6e-121)
		tmp = t_0;
	elseif (y <= 2.3e-123)
		tmp = Float64(2.0 * fma(y, Float64(y / x), y));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x * N[(N[(y * 2.0), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.6e-121], t$95$0, If[LessEqual[y, 2.3e-123], N[(2.0 * N[(y * N[(y / x), $MachinePrecision] + y), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -2.59999999999999986e-121 or 2.29999999999999987e-123 < y

   1. Initial program 79.4%

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

      1. associate-*l* N/A

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

      2. lift--.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 N/A

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

      7. lower-*.f64 97.6

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

   4. Applied egg-rr 97.6%

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

   if -2.59999999999999986e-121 < y < 2.29999999999999987e-123

   1. Initial program 83.5%

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

      1. associate-*l* N/A

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

      2. lift--.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 N/A

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

      7. lower-*.f64 65.3

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

   4. Applied egg-rr 65.3%

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

   5. Taylor expanded in y around 0

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

      1. distribute-rgt-in N/A

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

      2. associate-*l* N/A

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

      3. distribute-lft-out N/A

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

      4. associate-*l/ N/A

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

      5. unpow2 N/A

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

      6. lower-*.f64 N/A

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

      7. +-commutative N/A

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

      8. unpow2 N/A

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

      9. associate-/l* N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-/.f64 94.2

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

   7. Simplified 94.2%

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

4. Final simplification 96.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.6 \cdot 10^{-121}:\\ \;\;\;\;x \cdot \frac{y \cdot 2}{x - y}\\ \mathbf{elif}\;y \leq 2.3 \cdot 10^{-123}:\\ \;\;\;\;2 \cdot \mathsf{fma}\left(y, \frac{y}{x}, y\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{y \cdot 2}{x - y}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 73.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.55 \cdot 10^{-95}:\\ \;\;\;\;y \cdot 2\\ \mathbf{elif}\;x \leq 1.2 \cdot 10^{-33}:\\ \;\;\;\;-2 \cdot \mathsf{fma}\left(x, \frac{x}{y}, x\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -1.55e-95)
   (* y 2.0)
   (if (<= x 1.2e-33) (* -2.0 (fma x (/ x y) x)) (* y 2.0))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.55e-95) {
		tmp = y * 2.0;
	} else if (x <= 1.2e-33) {
		tmp = -2.0 * fma(x, (x / y), x);
	} else {
		tmp = y * 2.0;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1.55e-95)
		tmp = Float64(y * 2.0);
	elseif (x <= 1.2e-33)
		tmp = Float64(-2.0 * fma(x, Float64(x / y), x));
	else
		tmp = Float64(y * 2.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1.55e-95], N[(y * 2.0), $MachinePrecision], If[LessEqual[x, 1.2e-33], N[(-2.0 * N[(x * N[(x / y), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision], N[(y * 2.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.54999999999999996e-95 or 1.2e-33 < x

   1. Initial program 78.9%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 75.6

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

   5. Simplified 75.6%

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

   if -1.54999999999999996e-95 < x < 1.2e-33

   1. Initial program 83.3%

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

   3. Taylor expanded in x around 0

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

      1. sub-neg N/A

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

      2. metadata-eval N/A

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

      3. distribute-lft-in N/A

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

      4. *-commutative N/A

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

      5. associate-*r* N/A

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

      6. associate-/l* N/A

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

      7. unpow2 N/A

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

      8. distribute-rgt-out N/A

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

      9. +-commutative N/A

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

      10. lower-*.f64 N/A

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

      11. +-commutative N/A

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

      12. unpow2 N/A

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

      13. associate-/l* N/A

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

      14. lower-fma.f64 N/A

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

      15. lower-/.f64 88.0

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

   5. Simplified 88.0%

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

Alternative 3: 73.8% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.55 \cdot 10^{-95}:\\ \;\;\;\;y \cdot 2\\ \mathbf{elif}\;x \leq 1.28 \cdot 10^{-33}:\\ \;\;\;\;x \cdot -2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -1.55e-95) (* y 2.0) (if (<= x 1.28e-33) (* x -2.0) (* y 2.0))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.55e-95) {
		tmp = y * 2.0;
	} else if (x <= 1.28e-33) {
		tmp = x * -2.0;
	} else {
		tmp = y * 2.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-1.55d-95)) then
        tmp = y * 2.0d0
    else if (x <= 1.28d-33) then
        tmp = x * (-2.0d0)
    else
        tmp = y * 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -1.55e-95) {
		tmp = y * 2.0;
	} else if (x <= 1.28e-33) {
		tmp = x * -2.0;
	} else {
		tmp = y * 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -1.55e-95:
		tmp = y * 2.0
	elif x <= 1.28e-33:
		tmp = x * -2.0
	else:
		tmp = y * 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1.55e-95)
		tmp = Float64(y * 2.0);
	elseif (x <= 1.28e-33)
		tmp = Float64(x * -2.0);
	else
		tmp = Float64(y * 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -1.55e-95)
		tmp = y * 2.0;
	elseif (x <= 1.28e-33)
		tmp = x * -2.0;
	else
		tmp = y * 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1.55e-95], N[(y * 2.0), $MachinePrecision], If[LessEqual[x, 1.28e-33], N[(x * -2.0), $MachinePrecision], N[(y * 2.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.54999999999999996e-95 or 1.28000000000000001e-33 < x

   1. Initial program 78.9%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 75.6

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

   5. Simplified 75.6%

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

   if -1.54999999999999996e-95 < x < 1.28000000000000001e-33

   1. Initial program 83.3%

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

   3. Taylor expanded in x around 0

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

      1. lower-*.f64 87.8

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

   5. Simplified 87.8%

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

4. Final simplification 80.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.55 \cdot 10^{-95}:\\ \;\;\;\;y \cdot 2\\ \mathbf{elif}\;x \leq 1.28 \cdot 10^{-33}:\\ \;\;\;\;x \cdot -2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 50.2% accurate, 4.2× speedup

Math

\[\begin{array}{l} \\ x \cdot -2 \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 80.5%

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

3. Taylor expanded in x around 0

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

   1. lower-*.f64 48.8

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

5. Simplified 48.8%

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

6. Final simplification 48.8%

   \[\leadsto x \cdot -2 \]
7. Add Preprocessing

Developer Target 1: 99.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{2 \cdot x}{x - y} \cdot y\\ \mathbf{if}\;x < -1.7210442634149447 \cdot 10^{+81}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x < 83645045635564430:\\ \;\;\;\;\frac{x \cdot 2}{\frac{x - y}{y}}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (/ (* 2.0 x) (- x y)) y)))
   (if (< x -1.7210442634149447e+81)
     t_0
     (if (< x 83645045635564430.0) (/ (* x 2.0) (/ (- x y) y)) t_0))))

C

double code(double x, double y) {
	double t_0 = ((2.0 * x) / (x - y)) * y;
	double tmp;
	if (x < -1.7210442634149447e+81) {
		tmp = t_0;
	} else if (x < 83645045635564430.0) {
		tmp = (x * 2.0) / ((x - y) / y);
	} else {
		tmp = t_0;
	}
	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 = ((2.0d0 * x) / (x - y)) * y
    if (x < (-1.7210442634149447d+81)) then
        tmp = t_0
    else if (x < 83645045635564430.0d0) then
        tmp = (x * 2.0d0) / ((x - y) / y)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = ((2.0 * x) / (x - y)) * y;
	double tmp;
	if (x < -1.7210442634149447e+81) {
		tmp = t_0;
	} else if (x < 83645045635564430.0) {
		tmp = (x * 2.0) / ((x - y) / y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = ((2.0 * x) / (x - y)) * y
	tmp = 0
	if x < -1.7210442634149447e+81:
		tmp = t_0
	elif x < 83645045635564430.0:
		tmp = (x * 2.0) / ((x - y) / y)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(Float64(2.0 * x) / Float64(x - y)) * y)
	tmp = 0.0
	if (x < -1.7210442634149447e+81)
		tmp = t_0;
	elseif (x < 83645045635564430.0)
		tmp = Float64(Float64(x * 2.0) / Float64(Float64(x - y) / y));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = ((2.0 * x) / (x - y)) * y;
	tmp = 0.0;
	if (x < -1.7210442634149447e+81)
		tmp = t_0;
	elseif (x < 83645045635564430.0)
		tmp = (x * 2.0) / ((x - y) / y);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[(2.0 * x), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]}, If[Less[x, -1.7210442634149447e+81], t$95$0, If[Less[x, 83645045635564430.0], N[(N[(x * 2.0), $MachinePrecision] / N[(N[(x - y), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Reproduce

herbie shell --seed 2024207 
(FPCore (x y)
  :name "Linear.Projection:perspective from linear-1.19.1.3, B"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< x -1721044263414944700000000000000000000000000000000000000000000000000000000000000000) (* (/ (* 2 x) (- x y)) y) (if (< x 83645045635564430) (/ (* x 2) (/ (- x y) y)) (* (/ (* 2 x) (- x y)) y))))

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