Numeric.SpecFunctions:invIncompleteGamma from math-functions-0.1.5.2, D

Percentage Accurate: 99.7% → 99.7%

Time: 8.3s

Alternatives: 15

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (- (- 1.0 (/ 1.0 (* x 9.0))) (/ y (* 3.0 (sqrt x)))))

C

double code(double x, double y) {
	return (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * sqrt(x)));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - (1.0d0 / (x * 9.0d0))) - (y / (3.0d0 * sqrt(x)))
end function

Java

public static double code(double x, double y) {
	return (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * Math.sqrt(x)));
}

Python

def code(x, y):
	return (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * math.sqrt(x)))

Julia

function code(x, y)
	return Float64(Float64(1.0 - Float64(1.0 / Float64(x * 9.0))) - Float64(y / Float64(3.0 * sqrt(x))))
end

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * sqrt(x)));
end

Wolfram

code[x_, y_] := N[(N[(1.0 - N[(1.0 / N[(x * 9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y / N[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (- (- 1.0 (/ 1.0 (* x 9.0))) (/ y (* 3.0 (sqrt x)))))

C

double code(double x, double y) {
	return (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * sqrt(x)));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - (1.0d0 / (x * 9.0d0))) - (y / (3.0d0 * sqrt(x)))
end function

Java

public static double code(double x, double y) {
	return (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * Math.sqrt(x)));
}

Python

def code(x, y):
	return (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * math.sqrt(x)))

Julia

function code(x, y)
	return Float64(Float64(1.0 - Float64(1.0 / Float64(x * 9.0))) - Float64(y / Float64(3.0 * sqrt(x))))
end

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * sqrt(x)));
end

Wolfram

code[x_, y_] := N[(N[(1.0 - N[(1.0 / N[(x * 9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y / N[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \left(1 - \frac{1}{9 \cdot x}\right) - \frac{y \cdot {x}^{-0.5}}{3} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (- (- 1.0 (/ 1.0 (* 9.0 x))) (/ (* y (pow x -0.5)) 3.0)))

C

double code(double x, double y) {
	return (1.0 - (1.0 / (9.0 * x))) - ((y * pow(x, -0.5)) / 3.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - (1.0d0 / (9.0d0 * x))) - ((y * (x ** (-0.5d0))) / 3.0d0)
end function

Java

public static double code(double x, double y) {
	return (1.0 - (1.0 / (9.0 * x))) - ((y * Math.pow(x, -0.5)) / 3.0);
}

Python

def code(x, y):
	return (1.0 - (1.0 / (9.0 * x))) - ((y * math.pow(x, -0.5)) / 3.0)

Julia

function code(x, y)
	return Float64(Float64(1.0 - Float64(1.0 / Float64(9.0 * x))) - Float64(Float64(y * (x ^ -0.5)) / 3.0))
end

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - (1.0 / (9.0 * x))) - ((y * (x ^ -0.5)) / 3.0);
end

Wolfram

code[x_, y_] := N[(N[(1.0 - N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(y * N[Power[x, -0.5], $MachinePrecision]), $MachinePrecision] / 3.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \color{blue}{\frac{y}{3 \cdot \sqrt{x}}} \]

   2. clear-num N/A

      \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \color{blue}{\frac{1}{\frac{3 \cdot \sqrt{x}}{y}}} \]

   3. associate-/r/ N/A

      \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \color{blue}{\frac{1}{3 \cdot \sqrt{x}} \cdot y} \]

   4. lift-*.f64 N/A

      \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \frac{1}{\color{blue}{3 \cdot \sqrt{x}}} \cdot y \]

   5. associate-/l/ N/A

      \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \color{blue}{\frac{\frac{1}{\sqrt{x}}}{3}} \cdot y \]

   6. associate-*l/ N/A

      \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \color{blue}{\frac{\frac{1}{\sqrt{x}} \cdot y}{3}} \]

   7. lower-/.f64 N/A

      \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \color{blue}{\frac{\frac{1}{\sqrt{x}} \cdot y}{3}} \]

   8. lower-*.f64 N/A

      \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \frac{\color{blue}{\frac{1}{\sqrt{x}} \cdot y}}{3} \]

   9. lift-sqrt.f64 N/A

      \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \frac{\frac{1}{\color{blue}{\sqrt{x}}} \cdot y}{3} \]

   10. pow1/2 N/A

       \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \frac{\frac{1}{\color{blue}{{x}^{\frac{1}{2}}}} \cdot y}{3} \]

   11. pow-flip N/A

       \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \frac{\color{blue}{{x}^{\left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}} \cdot y}{3} \]

   12. metadata-eval N/A

       \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \frac{{x}^{\color{blue}{\frac{-1}{2}}} \cdot y}{3} \]

   13. metadata-eval N/A

       \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \frac{{x}^{\color{blue}{\left(\frac{-1}{2}\right)}} \cdot y}{3} \]

   14. lower-pow.f64 N/A

       \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \frac{\color{blue}{{x}^{\left(\frac{-1}{2}\right)}} \cdot y}{3} \]

   15. metadata-eval 99.7

       \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \frac{{x}^{\color{blue}{-0.5}} \cdot y}{3} \]

4. Applied rewrites 99.7%

   \[\leadsto \left(1 - \frac{1}{x \cdot 9}\right) - \color{blue}{\frac{{x}^{-0.5} \cdot y}{3}} \]

5. Final simplification 99.7%

   \[\leadsto \left(1 - \frac{1}{9 \cdot x}\right) - \frac{y \cdot {x}^{-0.5}}{3} \]
6. Add Preprocessing

Alternative 2: 62.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(1 - \frac{1}{9 \cdot x}\right) - \frac{y}{\sqrt{x} \cdot 3} \leq -10:\\ \;\;\;\;\frac{-0.1111111111111111}{x}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (- (- 1.0 (/ 1.0 (* 9.0 x))) (/ y (* (sqrt x) 3.0))) -10.0)
   (/ -0.1111111111111111 x)
   1.0))

C

double code(double x, double y) {
	double tmp;
	if (((1.0 - (1.0 / (9.0 * x))) - (y / (sqrt(x) * 3.0))) <= -10.0) {
		tmp = -0.1111111111111111 / x;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (((1.0d0 - (1.0d0 / (9.0d0 * x))) - (y / (sqrt(x) * 3.0d0))) <= (-10.0d0)) then
        tmp = (-0.1111111111111111d0) / x
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (((1.0 - (1.0 / (9.0 * x))) - (y / (Math.sqrt(x) * 3.0))) <= -10.0) {
		tmp = -0.1111111111111111 / x;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((1.0 - (1.0 / (9.0 * x))) - (y / (math.sqrt(x) * 3.0))) <= -10.0:
		tmp = -0.1111111111111111 / x
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(1.0 - Float64(1.0 / Float64(9.0 * x))) - Float64(y / Float64(sqrt(x) * 3.0))) <= -10.0)
		tmp = Float64(-0.1111111111111111 / x);
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (((1.0 - (1.0 / (9.0 * x))) - (y / (sqrt(x) * 3.0))) <= -10.0)
		tmp = -0.1111111111111111 / x;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(1.0 - N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y / N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -10.0], N[(-0.1111111111111111 / x), $MachinePrecision], 1.0]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (-.f64 #s(literal 1 binary64) (/.f64 #s(literal 1 binary64) (*.f64 x #s(literal 9 binary64)))) (/.f64 y (*.f64 #s(literal 3 binary64) (sqrt.f64 x)))) < -10

   1. Initial program 99.5%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]
   4. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]

      2. associate-*r/ N/A

         \[\leadsto 1 - \color{blue}{\frac{\frac{1}{9} \cdot 1}{x}} \]

      3. metadata-eval N/A

         \[\leadsto 1 - \frac{\color{blue}{\frac{1}{9}}}{x} \]

      4. lower-/.f64 59.8

         \[\leadsto 1 - \color{blue}{\frac{0.1111111111111111}{x}} \]

   5. Applied rewrites 59.8%

      \[\leadsto \color{blue}{1 - \frac{0.1111111111111111}{x}} \]

   6. Taylor expanded in x around 0

      \[\leadsto \frac{\frac{-1}{9}}{\color{blue}{x}} \]
   7. Step-by-step derivation

   8. Applied rewrites 58.9%

      \[\leadsto \frac{-0.1111111111111111}{\color{blue}{x}} \]

   if -10 < (-.f64 (-.f64 #s(literal 1 binary64) (/.f64 #s(literal 1 binary64) (*.f64 x #s(literal 9 binary64)))) (/.f64 y (*.f64 #s(literal 3 binary64) (sqrt.f64 x))))

   1. Initial program 99.8%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]
   4. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]

      2. associate-*r/ N/A

         \[\leadsto 1 - \color{blue}{\frac{\frac{1}{9} \cdot 1}{x}} \]

      3. metadata-eval N/A

         \[\leadsto 1 - \frac{\color{blue}{\frac{1}{9}}}{x} \]

      4. lower-/.f64 63.6

         \[\leadsto 1 - \color{blue}{\frac{0.1111111111111111}{x}} \]

   5. Applied rewrites 63.6%

      \[\leadsto \color{blue}{1 - \frac{0.1111111111111111}{x}} \]

   6. Taylor expanded in x around inf

      \[\leadsto 1 \]
   7. Step-by-step derivation

   8. Applied rewrites 63.5%

      \[\leadsto 1 \]
3. Recombined 2 regimes into one program.

4. Final simplification 61.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(1 - \frac{1}{9 \cdot x}\right) - \frac{y}{\sqrt{x} \cdot 3} \leq -10:\\ \;\;\;\;\frac{-0.1111111111111111}{x}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \left(1 - \frac{\frac{-1}{x}}{-9}\right) - \frac{y}{\sqrt{x} \cdot 3} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (- (- 1.0 (/ (/ -1.0 x) -9.0)) (/ y (* (sqrt x) 3.0))))

C

double code(double x, double y) {
	return (1.0 - ((-1.0 / x) / -9.0)) - (y / (sqrt(x) * 3.0));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - (((-1.0d0) / x) / (-9.0d0))) - (y / (sqrt(x) * 3.0d0))
end function

Java

public static double code(double x, double y) {
	return (1.0 - ((-1.0 / x) / -9.0)) - (y / (Math.sqrt(x) * 3.0));
}

Python

def code(x, y):
	return (1.0 - ((-1.0 / x) / -9.0)) - (y / (math.sqrt(x) * 3.0))

Julia

function code(x, y)
	return Float64(Float64(1.0 - Float64(Float64(-1.0 / x) / -9.0)) - Float64(y / Float64(sqrt(x) * 3.0)))
end

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - ((-1.0 / x) / -9.0)) - (y / (sqrt(x) * 3.0));
end

Wolfram

code[x_, y_] := N[(N[(1.0 - N[(N[(-1.0 / x), $MachinePrecision] / -9.0), $MachinePrecision]), $MachinePrecision] - N[(y / N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \left(1 - \color{blue}{\frac{1}{x \cdot 9}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

   2. lift-*.f64 N/A

      \[\leadsto \left(1 - \frac{1}{\color{blue}{x \cdot 9}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

   3. associate-/r* N/A

      \[\leadsto \left(1 - \color{blue}{\frac{\frac{1}{x}}{9}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

   4. frac-2neg N/A

      \[\leadsto \left(1 - \color{blue}{\frac{\mathsf{neg}\left(\frac{1}{x}\right)}{\mathsf{neg}\left(9\right)}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

   5. lower-/.f64 N/A

      \[\leadsto \left(1 - \color{blue}{\frac{\mathsf{neg}\left(\frac{1}{x}\right)}{\mathsf{neg}\left(9\right)}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

   6. neg-mul-1 N/A

      \[\leadsto \left(1 - \frac{\color{blue}{-1 \cdot \frac{1}{x}}}{\mathsf{neg}\left(9\right)}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

   7. un-div-inv N/A

      \[\leadsto \left(1 - \frac{\color{blue}{\frac{-1}{x}}}{\mathsf{neg}\left(9\right)}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

   8. lower-/.f64 N/A

      \[\leadsto \left(1 - \frac{\color{blue}{\frac{-1}{x}}}{\mathsf{neg}\left(9\right)}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

   9. metadata-eval 99.7

      \[\leadsto \left(1 - \frac{\frac{-1}{x}}{\color{blue}{-9}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

4. Applied rewrites 99.7%

   \[\leadsto \left(1 - \color{blue}{\frac{\frac{-1}{x}}{-9}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

5. Final simplification 99.7%

   \[\leadsto \left(1 - \frac{\frac{-1}{x}}{-9}\right) - \frac{y}{\sqrt{x} \cdot 3} \]
6. Add Preprocessing

Alternative 4: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(1 - \frac{1}{9 \cdot x}\right) - \frac{y}{\sqrt{x} \cdot 3} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (- (- 1.0 (/ 1.0 (* 9.0 x))) (/ y (* (sqrt x) 3.0))))

C

double code(double x, double y) {
	return (1.0 - (1.0 / (9.0 * x))) - (y / (sqrt(x) * 3.0));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - (1.0d0 / (9.0d0 * x))) - (y / (sqrt(x) * 3.0d0))
end function

Java

public static double code(double x, double y) {
	return (1.0 - (1.0 / (9.0 * x))) - (y / (Math.sqrt(x) * 3.0));
}

Python

def code(x, y):
	return (1.0 - (1.0 / (9.0 * x))) - (y / (math.sqrt(x) * 3.0))

Julia

function code(x, y)
	return Float64(Float64(1.0 - Float64(1.0 / Float64(9.0 * x))) - Float64(y / Float64(sqrt(x) * 3.0)))
end

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - (1.0 / (9.0 * x))) - (y / (sqrt(x) * 3.0));
end

Wolfram

code[x_, y_] := N[(N[(1.0 - N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y / N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
2. Add Preprocessing

3. Final simplification 99.6%

   \[\leadsto \left(1 - \frac{1}{9 \cdot x}\right) - \frac{y}{\sqrt{x} \cdot 3} \]
4. Add Preprocessing

Alternative 5: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\frac{-1}{x}, 0.1111111111111111, 1 - \frac{y}{\sqrt{x} \cdot 3}\right) \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (fma (/ -1.0 x) 0.1111111111111111 (- 1.0 (/ y (* (sqrt x) 3.0)))))

C

double code(double x, double y) {
	return fma((-1.0 / x), 0.1111111111111111, (1.0 - (y / (sqrt(x) * 3.0))));
}

Julia

function code(x, y)
	return fma(Float64(-1.0 / x), 0.1111111111111111, Float64(1.0 - Float64(y / Float64(sqrt(x) * 3.0))))
end

Wolfram

code[x_, y_] := N[(N[(-1.0 / x), $MachinePrecision] * 0.1111111111111111 + N[(1.0 - N[(y / N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto \color{blue}{\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}}} \]

   2. lift--.f64 N/A

      \[\leadsto \color{blue}{\left(1 - \frac{1}{x \cdot 9}\right)} - \frac{y}{3 \cdot \sqrt{x}} \]

   3. sub-neg N/A

      \[\leadsto \color{blue}{\left(1 + \left(\mathsf{neg}\left(\frac{1}{x \cdot 9}\right)\right)\right)} - \frac{y}{3 \cdot \sqrt{x}} \]

   4. +-commutative N/A

      \[\leadsto \color{blue}{\left(\left(\mathsf{neg}\left(\frac{1}{x \cdot 9}\right)\right) + 1\right)} - \frac{y}{3 \cdot \sqrt{x}} \]

   5. associate--l+ N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\frac{1}{x \cdot 9}\right)\right) + \left(1 - \frac{y}{3 \cdot \sqrt{x}}\right)} \]

   6. lift-/.f64 N/A

      \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\frac{1}{x \cdot 9}}\right)\right) + \left(1 - \frac{y}{3 \cdot \sqrt{x}}\right) \]

   7. inv-pow N/A

      \[\leadsto \left(\mathsf{neg}\left(\color{blue}{{\left(x \cdot 9\right)}^{-1}}\right)\right) + \left(1 - \frac{y}{3 \cdot \sqrt{x}}\right) \]

   8. lift-*.f64 N/A

      \[\leadsto \left(\mathsf{neg}\left({\color{blue}{\left(x \cdot 9\right)}}^{-1}\right)\right) + \left(1 - \frac{y}{3 \cdot \sqrt{x}}\right) \]

   9. unpow-prod-down N/A

      \[\leadsto \left(\mathsf{neg}\left(\color{blue}{{x}^{-1} \cdot {9}^{-1}}\right)\right) + \left(1 - \frac{y}{3 \cdot \sqrt{x}}\right) \]

   10. inv-pow N/A

       \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\frac{1}{x}} \cdot {9}^{-1}\right)\right) + \left(1 - \frac{y}{3 \cdot \sqrt{x}}\right) \]

   11. distribute-lft-neg-in N/A

       \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\frac{1}{x}\right)\right) \cdot {9}^{-1}} + \left(1 - \frac{y}{3 \cdot \sqrt{x}}\right) \]

   12. lower-fma.f64 N/A

       \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{neg}\left(\frac{1}{x}\right), {9}^{-1}, 1 - \frac{y}{3 \cdot \sqrt{x}}\right)} \]

   13. neg-mul-1 N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{-1 \cdot \frac{1}{x}}, {9}^{-1}, 1 - \frac{y}{3 \cdot \sqrt{x}}\right) \]

   14. un-div-inv N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{-1}{x}}, {9}^{-1}, 1 - \frac{y}{3 \cdot \sqrt{x}}\right) \]

   15. lower-/.f64 N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{-1}{x}}, {9}^{-1}, 1 - \frac{y}{3 \cdot \sqrt{x}}\right) \]

   16. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{x}, \color{blue}{\frac{1}{9}}, 1 - \frac{y}{3 \cdot \sqrt{x}}\right) \]

   17. lower--.f64 99.6

       \[\leadsto \mathsf{fma}\left(\frac{-1}{x}, 0.1111111111111111, \color{blue}{1 - \frac{y}{3 \cdot \sqrt{x}}}\right) \]

   18. lift-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{x}, \frac{1}{9}, 1 - \frac{y}{\color{blue}{3 \cdot \sqrt{x}}}\right) \]

   19. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{x}, \frac{1}{9}, 1 - \frac{y}{\color{blue}{\sqrt{x} \cdot 3}}\right) \]

   20. lower-*.f64 99.6

       \[\leadsto \mathsf{fma}\left(\frac{-1}{x}, 0.1111111111111111, 1 - \frac{y}{\color{blue}{\sqrt{x} \cdot 3}}\right) \]

4. Applied rewrites 99.6%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{x}, 0.1111111111111111, 1 - \frac{y}{\sqrt{x} \cdot 3}\right)} \]
5. Add Preprocessing

Alternative 6: 94.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{y}{\sqrt{x} \cdot 3}\\ \mathbf{if}\;y \leq -1 \cdot 10^{+64}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 3 \cdot 10^{+53}:\\ \;\;\;\;1 - \frac{\frac{-1}{x}}{-9}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ y (* (sqrt x) 3.0)))))
   (if (<= y -1e+64) t_0 (if (<= y 3e+53) (- 1.0 (/ (/ -1.0 x) -9.0)) t_0))))

C

double code(double x, double y) {
	double t_0 = 1.0 - (y / (sqrt(x) * 3.0));
	double tmp;
	if (y <= -1e+64) {
		tmp = t_0;
	} else if (y <= 3e+53) {
		tmp = 1.0 - ((-1.0 / x) / -9.0);
	} 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 = 1.0d0 - (y / (sqrt(x) * 3.0d0))
    if (y <= (-1d+64)) then
        tmp = t_0
    else if (y <= 3d+53) then
        tmp = 1.0d0 - (((-1.0d0) / x) / (-9.0d0))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = 1.0 - (y / (Math.sqrt(x) * 3.0));
	double tmp;
	if (y <= -1e+64) {
		tmp = t_0;
	} else if (y <= 3e+53) {
		tmp = 1.0 - ((-1.0 / x) / -9.0);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - (y / (math.sqrt(x) * 3.0))
	tmp = 0
	if y <= -1e+64:
		tmp = t_0
	elif y <= 3e+53:
		tmp = 1.0 - ((-1.0 / x) / -9.0)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - Float64(y / Float64(sqrt(x) * 3.0)))
	tmp = 0.0
	if (y <= -1e+64)
		tmp = t_0;
	elseif (y <= 3e+53)
		tmp = Float64(1.0 - Float64(Float64(-1.0 / x) / -9.0));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 1.0 - (y / (sqrt(x) * 3.0));
	tmp = 0.0;
	if (y <= -1e+64)
		tmp = t_0;
	elseif (y <= 3e+53)
		tmp = 1.0 - ((-1.0 / x) / -9.0);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 - N[(y / N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1e+64], t$95$0, If[LessEqual[y, 3e+53], N[(1.0 - N[(N[(-1.0 / x), $MachinePrecision] / -9.0), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.00000000000000002e64 or 2.99999999999999998e53 < y

   1. Initial program 99.5%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{1} - \frac{y}{3 \cdot \sqrt{x}} \]
   4. Step-by-step derivation

   5. Applied rewrites 96.3%

      \[\leadsto \color{blue}{1} - \frac{y}{3 \cdot \sqrt{x}} \]

   if -1.00000000000000002e64 < y < 2.99999999999999998e53

   1. Initial program 99.7%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]
   4. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]

      2. associate-*r/ N/A

         \[\leadsto 1 - \color{blue}{\frac{\frac{1}{9} \cdot 1}{x}} \]

      3. metadata-eval N/A

         \[\leadsto 1 - \frac{\color{blue}{\frac{1}{9}}}{x} \]

      4. lower-/.f64 97.5

         \[\leadsto 1 - \color{blue}{\frac{0.1111111111111111}{x}} \]

   5. Applied rewrites 97.5%

      \[\leadsto \color{blue}{1 - \frac{0.1111111111111111}{x}} \]
   6. Step-by-step derivation

   7. Applied rewrites 97.3%

      \[\leadsto 1 - \frac{\frac{0.3333333333333333}{\sqrt{x}}}{\color{blue}{\sqrt{x} \cdot 3}} \]
   8. Step-by-step derivation

   9. Applied rewrites 97.6%

      \[\leadsto 1 - \frac{\frac{-1}{x}}{\color{blue}{-9}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1 \cdot 10^{+64}:\\ \;\;\;\;1 - \frac{y}{\sqrt{x} \cdot 3}\\ \mathbf{elif}\;y \leq 3 \cdot 10^{+53}:\\ \;\;\;\;1 - \frac{\frac{-1}{x}}{-9}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{y}{\sqrt{x} \cdot 3}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 99.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1.1 \cdot 10^{+15}:\\ \;\;\;\;\frac{x - \mathsf{fma}\left(\sqrt{x} \cdot y, 0.3333333333333333, 0.1111111111111111\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{y}{\sqrt{x} \cdot 3}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x 1.1e+15)
   (/ (- x (fma (* (sqrt x) y) 0.3333333333333333 0.1111111111111111)) x)
   (- 1.0 (/ y (* (sqrt x) 3.0)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= 1.1e+15) {
		tmp = (x - fma((sqrt(x) * y), 0.3333333333333333, 0.1111111111111111)) / x;
	} else {
		tmp = 1.0 - (y / (sqrt(x) * 3.0));
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (x <= 1.1e+15)
		tmp = Float64(Float64(x - fma(Float64(sqrt(x) * y), 0.3333333333333333, 0.1111111111111111)) / x);
	else
		tmp = Float64(1.0 - Float64(y / Float64(sqrt(x) * 3.0)));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, 1.1e+15], N[(N[(x - N[(N[(N[Sqrt[x], $MachinePrecision] * y), $MachinePrecision] * 0.3333333333333333 + 0.1111111111111111), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision], N[(1.0 - N[(y / N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.1e15

   1. Initial program 99.5%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{x - \left(\frac{1}{9} + \frac{1}{3} \cdot \left(\sqrt{x} \cdot y\right)\right)}{x}} \]
   4. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x - \left(\frac{1}{9} + \frac{1}{3} \cdot \left(\sqrt{x} \cdot y\right)\right)}{x}} \]

      2. lower--.f64 N/A

         \[\leadsto \frac{\color{blue}{x - \left(\frac{1}{9} + \frac{1}{3} \cdot \left(\sqrt{x} \cdot y\right)\right)}}{x} \]

      3. +-commutative N/A

         \[\leadsto \frac{x - \color{blue}{\left(\frac{1}{3} \cdot \left(\sqrt{x} \cdot y\right) + \frac{1}{9}\right)}}{x} \]

      4. *-commutative N/A

         \[\leadsto \frac{x - \left(\color{blue}{\left(\sqrt{x} \cdot y\right) \cdot \frac{1}{3}} + \frac{1}{9}\right)}{x} \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{x - \color{blue}{\mathsf{fma}\left(\sqrt{x} \cdot y, \frac{1}{3}, \frac{1}{9}\right)}}{x} \]

      6. lower-*.f64 N/A

         \[\leadsto \frac{x - \mathsf{fma}\left(\color{blue}{\sqrt{x} \cdot y}, \frac{1}{3}, \frac{1}{9}\right)}{x} \]

      7. lower-sqrt.f64 99.5

         \[\leadsto \frac{x - \mathsf{fma}\left(\color{blue}{\sqrt{x}} \cdot y, 0.3333333333333333, 0.1111111111111111\right)}{x} \]

   5. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\frac{x - \mathsf{fma}\left(\sqrt{x} \cdot y, 0.3333333333333333, 0.1111111111111111\right)}{x}} \]

   if 1.1e15 < x

   1. Initial program 99.8%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{1} - \frac{y}{3 \cdot \sqrt{x}} \]
   4. Step-by-step derivation

   5. Applied rewrites 99.8%

      \[\leadsto \color{blue}{1} - \frac{y}{3 \cdot \sqrt{x}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 1.1 \cdot 10^{+15}:\\ \;\;\;\;\frac{x - \mathsf{fma}\left(\sqrt{x} \cdot y, 0.3333333333333333, 0.1111111111111111\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{y}{\sqrt{x} \cdot 3}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 99.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(-0.3333333333333333, \frac{y}{\sqrt{x}}, 1 - \frac{0.1111111111111111}{x}\right) \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (fma -0.3333333333333333 (/ y (sqrt x)) (- 1.0 (/ 0.1111111111111111 x))))

C

double code(double x, double y) {
	return fma(-0.3333333333333333, (y / sqrt(x)), (1.0 - (0.1111111111111111 / x)));
}

Julia

function code(x, y)
	return fma(-0.3333333333333333, Float64(y / sqrt(x)), Float64(1.0 - Float64(0.1111111111111111 / x)))
end

Wolfram

code[x_, y_] := N[(-0.3333333333333333 * N[(y / N[Sqrt[x], $MachinePrecision]), $MachinePrecision] + N[(1.0 - N[(0.1111111111111111 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto \color{blue}{\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}}} \]

   2. sub-neg N/A

      \[\leadsto \color{blue}{\left(1 - \frac{1}{x \cdot 9}\right) + \left(\mathsf{neg}\left(\frac{y}{3 \cdot \sqrt{x}}\right)\right)} \]

   3. +-commutative N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\frac{y}{3 \cdot \sqrt{x}}\right)\right) + \left(1 - \frac{1}{x \cdot 9}\right)} \]

   4. lift-/.f64 N/A

      \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\frac{y}{3 \cdot \sqrt{x}}}\right)\right) + \left(1 - \frac{1}{x \cdot 9}\right) \]

   5. distribute-neg-frac N/A

      \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(y\right)}{3 \cdot \sqrt{x}}} + \left(1 - \frac{1}{x \cdot 9}\right) \]

   6. neg-mul-1 N/A

      \[\leadsto \frac{\color{blue}{-1 \cdot y}}{3 \cdot \sqrt{x}} + \left(1 - \frac{1}{x \cdot 9}\right) \]

   7. lift-*.f64 N/A

      \[\leadsto \frac{-1 \cdot y}{\color{blue}{3 \cdot \sqrt{x}}} + \left(1 - \frac{1}{x \cdot 9}\right) \]

   8. times-frac N/A

      \[\leadsto \color{blue}{\frac{-1}{3} \cdot \frac{y}{\sqrt{x}}} + \left(1 - \frac{1}{x \cdot 9}\right) \]

   9. metadata-eval N/A

      \[\leadsto \color{blue}{\frac{-1}{3}} \cdot \frac{y}{\sqrt{x}} + \left(1 - \frac{1}{x \cdot 9}\right) \]

   10. metadata-eval N/A

       \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\frac{1}{3}\right)\right)} \cdot \frac{y}{\sqrt{x}} + \left(1 - \frac{1}{x \cdot 9}\right) \]

   11. metadata-eval N/A

       \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\frac{1}{3}}\right)\right) \cdot \frac{y}{\sqrt{x}} + \left(1 - \frac{1}{x \cdot 9}\right) \]

   12. lower-fma.f64 N/A

       \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{neg}\left(\frac{1}{3}\right), \frac{y}{\sqrt{x}}, 1 - \frac{1}{x \cdot 9}\right)} \]

   13. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{neg}\left(\color{blue}{\frac{1}{3}}\right), \frac{y}{\sqrt{x}}, 1 - \frac{1}{x \cdot 9}\right) \]

   14. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{-1}{3}}, \frac{y}{\sqrt{x}}, 1 - \frac{1}{x \cdot 9}\right) \]

   15. lower-/.f64 99.6

       \[\leadsto \mathsf{fma}\left(-0.3333333333333333, \color{blue}{\frac{y}{\sqrt{x}}}, 1 - \frac{1}{x \cdot 9}\right) \]

   16. lift-/.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \frac{y}{\sqrt{x}}, 1 - \color{blue}{\frac{1}{x \cdot 9}}\right) \]

   17. lift-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \frac{y}{\sqrt{x}}, 1 - \frac{1}{\color{blue}{x \cdot 9}}\right) \]

   18. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \frac{y}{\sqrt{x}}, 1 - \frac{1}{\color{blue}{9 \cdot x}}\right) \]

   19. associate-/r* N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \frac{y}{\sqrt{x}}, 1 - \color{blue}{\frac{\frac{1}{9}}{x}}\right) \]

   20. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \frac{y}{\sqrt{x}}, 1 - \frac{\color{blue}{\frac{1}{9}}}{x}\right) \]

   21. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \frac{y}{\sqrt{x}}, 1 - \frac{\color{blue}{{9}^{-1}}}{x}\right) \]

   22. lower-/.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \frac{y}{\sqrt{x}}, 1 - \color{blue}{\frac{{9}^{-1}}{x}}\right) \]

   23. metadata-eval 99.6

       \[\leadsto \mathsf{fma}\left(-0.3333333333333333, \frac{y}{\sqrt{x}}, 1 - \frac{\color{blue}{0.1111111111111111}}{x}\right) \]

4. Applied rewrites 99.6%

   \[\leadsto \color{blue}{\mathsf{fma}\left(-0.3333333333333333, \frac{y}{\sqrt{x}}, 1 - \frac{0.1111111111111111}{x}\right)} \]
5. Add Preprocessing

Alternative 9: 92.0% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-0.3333333333333333}{\sqrt{x}} \cdot y\\ \mathbf{if}\;y \leq -1.3 \cdot 10^{+90}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 3.4 \cdot 10^{+54}:\\ \;\;\;\;1 - \frac{\frac{-1}{x}}{-9}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (/ -0.3333333333333333 (sqrt x)) y)))
   (if (<= y -1.3e+90)
     t_0
     (if (<= y 3.4e+54) (- 1.0 (/ (/ -1.0 x) -9.0)) t_0))))

C

double code(double x, double y) {
	double t_0 = (-0.3333333333333333 / sqrt(x)) * y;
	double tmp;
	if (y <= -1.3e+90) {
		tmp = t_0;
	} else if (y <= 3.4e+54) {
		tmp = 1.0 - ((-1.0 / x) / -9.0);
	} 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 = ((-0.3333333333333333d0) / sqrt(x)) * y
    if (y <= (-1.3d+90)) then
        tmp = t_0
    else if (y <= 3.4d+54) then
        tmp = 1.0d0 - (((-1.0d0) / x) / (-9.0d0))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (-0.3333333333333333 / Math.sqrt(x)) * y;
	double tmp;
	if (y <= -1.3e+90) {
		tmp = t_0;
	} else if (y <= 3.4e+54) {
		tmp = 1.0 - ((-1.0 / x) / -9.0);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (-0.3333333333333333 / math.sqrt(x)) * y
	tmp = 0
	if y <= -1.3e+90:
		tmp = t_0
	elif y <= 3.4e+54:
		tmp = 1.0 - ((-1.0 / x) / -9.0)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(-0.3333333333333333 / sqrt(x)) * y)
	tmp = 0.0
	if (y <= -1.3e+90)
		tmp = t_0;
	elseif (y <= 3.4e+54)
		tmp = Float64(1.0 - Float64(Float64(-1.0 / x) / -9.0));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (-0.3333333333333333 / sqrt(x)) * y;
	tmp = 0.0;
	if (y <= -1.3e+90)
		tmp = t_0;
	elseif (y <= 3.4e+54)
		tmp = 1.0 - ((-1.0 / x) / -9.0);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(-0.3333333333333333 / N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[y, -1.3e+90], t$95$0, If[LessEqual[y, 3.4e+54], N[(1.0 - N[(N[(-1.0 / x), $MachinePrecision] / -9.0), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.2999999999999999e90 or 3.4000000000000001e54 < y

   1. Initial program 99.5%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \left(1 - \color{blue}{\frac{1}{x \cdot 9}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(1 - \frac{1}{\color{blue}{x \cdot 9}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

      3. associate-/r* N/A

         \[\leadsto \left(1 - \color{blue}{\frac{\frac{1}{x}}{9}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

      4. frac-2neg N/A

         \[\leadsto \left(1 - \color{blue}{\frac{\mathsf{neg}\left(\frac{1}{x}\right)}{\mathsf{neg}\left(9\right)}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

      5. lower-/.f64 N/A

         \[\leadsto \left(1 - \color{blue}{\frac{\mathsf{neg}\left(\frac{1}{x}\right)}{\mathsf{neg}\left(9\right)}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

      6. neg-mul-1 N/A

         \[\leadsto \left(1 - \frac{\color{blue}{-1 \cdot \frac{1}{x}}}{\mathsf{neg}\left(9\right)}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

      7. un-div-inv N/A

         \[\leadsto \left(1 - \frac{\color{blue}{\frac{-1}{x}}}{\mathsf{neg}\left(9\right)}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

      8. lower-/.f64 N/A

         \[\leadsto \left(1 - \frac{\color{blue}{\frac{-1}{x}}}{\mathsf{neg}\left(9\right)}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

      9. metadata-eval 99.5

         \[\leadsto \left(1 - \frac{\frac{-1}{x}}{\color{blue}{-9}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

   4. Applied rewrites 99.5%

      \[\leadsto \left(1 - \color{blue}{\frac{\frac{-1}{x}}{-9}}\right) - \frac{y}{3 \cdot \sqrt{x}} \]

   5. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\frac{-1}{3} \cdot \left(\sqrt{\frac{1}{x}} \cdot y\right)} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{-1}{3} \cdot \color{blue}{\left(y \cdot \sqrt{\frac{1}{x}}\right)} \]

      2. associate-*r* N/A

         \[\leadsto \color{blue}{\left(\frac{-1}{3} \cdot y\right) \cdot \sqrt{\frac{1}{x}}} \]

      3. metadata-eval N/A

         \[\leadsto \left(\color{blue}{\left(\frac{1}{3} \cdot -1\right)} \cdot y\right) \cdot \sqrt{\frac{1}{x}} \]

      4. associate-*r* N/A

         \[\leadsto \color{blue}{\left(\frac{1}{3} \cdot \left(-1 \cdot y\right)\right)} \cdot \sqrt{\frac{1}{x}} \]

      5. rem-square-sqrt N/A

         \[\leadsto \left(\frac{1}{3} \cdot \left(\color{blue}{\left(\sqrt{-1} \cdot \sqrt{-1}\right)} \cdot y\right)\right) \cdot \sqrt{\frac{1}{x}} \]

      6. unpow2 N/A

         \[\leadsto \left(\frac{1}{3} \cdot \left(\color{blue}{{\left(\sqrt{-1}\right)}^{2}} \cdot y\right)\right) \cdot \sqrt{\frac{1}{x}} \]

      7. *-commutative N/A

         \[\leadsto \left(\frac{1}{3} \cdot \color{blue}{\left(y \cdot {\left(\sqrt{-1}\right)}^{2}\right)}\right) \cdot \sqrt{\frac{1}{x}} \]

      8. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{3} \cdot \left(y \cdot {\left(\sqrt{-1}\right)}^{2}\right)\right) \cdot \sqrt{\frac{1}{x}}} \]

      9. *-commutative N/A

         \[\leadsto \left(\frac{1}{3} \cdot \color{blue}{\left({\left(\sqrt{-1}\right)}^{2} \cdot y\right)}\right) \cdot \sqrt{\frac{1}{x}} \]

      10. unpow2 N/A

          \[\leadsto \left(\frac{1}{3} \cdot \left(\color{blue}{\left(\sqrt{-1} \cdot \sqrt{-1}\right)} \cdot y\right)\right) \cdot \sqrt{\frac{1}{x}} \]

      11. rem-square-sqrt N/A

          \[\leadsto \left(\frac{1}{3} \cdot \left(\color{blue}{-1} \cdot y\right)\right) \cdot \sqrt{\frac{1}{x}} \]

      12. associate-*r* N/A

          \[\leadsto \color{blue}{\left(\left(\frac{1}{3} \cdot -1\right) \cdot y\right)} \cdot \sqrt{\frac{1}{x}} \]

      13. metadata-eval N/A

          \[\leadsto \left(\color{blue}{\frac{-1}{3}} \cdot y\right) \cdot \sqrt{\frac{1}{x}} \]

      14. *-commutative N/A

          \[\leadsto \color{blue}{\left(y \cdot \frac{-1}{3}\right)} \cdot \sqrt{\frac{1}{x}} \]

      15. lower-*.f64 N/A

          \[\leadsto \color{blue}{\left(y \cdot \frac{-1}{3}\right)} \cdot \sqrt{\frac{1}{x}} \]

      16. lower-sqrt.f64 N/A

          \[\leadsto \left(y \cdot \frac{-1}{3}\right) \cdot \color{blue}{\sqrt{\frac{1}{x}}} \]

      17. lower-/.f64 89.2

          \[\leadsto \left(y \cdot -0.3333333333333333\right) \cdot \sqrt{\color{blue}{\frac{1}{x}}} \]

   7. Applied rewrites 89.2%

      \[\leadsto \color{blue}{\left(y \cdot -0.3333333333333333\right) \cdot \sqrt{\frac{1}{x}}} \]
   8. Step-by-step derivation

   9. Applied rewrites 89.3%

      \[\leadsto \frac{-0.3333333333333333}{\sqrt{x}} \cdot \color{blue}{y} \]

   if -1.2999999999999999e90 < y < 3.4000000000000001e54

   1. Initial program 99.7%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]
   4. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]

      2. associate-*r/ N/A

         \[\leadsto 1 - \color{blue}{\frac{\frac{1}{9} \cdot 1}{x}} \]

      3. metadata-eval N/A

         \[\leadsto 1 - \frac{\color{blue}{\frac{1}{9}}}{x} \]

      4. lower-/.f64 95.8

         \[\leadsto 1 - \color{blue}{\frac{0.1111111111111111}{x}} \]

   5. Applied rewrites 95.8%

      \[\leadsto \color{blue}{1 - \frac{0.1111111111111111}{x}} \]
   6. Step-by-step derivation

   7. Applied rewrites 95.6%

      \[\leadsto 1 - \frac{\frac{0.3333333333333333}{\sqrt{x}}}{\color{blue}{\sqrt{x} \cdot 3}} \]
   8. Step-by-step derivation

   9. Applied rewrites 95.9%

      \[\leadsto 1 - \frac{\frac{-1}{x}}{\color{blue}{-9}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 98.4% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 25.5:\\ \;\;\;\;\frac{\mathsf{fma}\left(\sqrt{x} \cdot y, -0.3333333333333333, -0.1111111111111111\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{y}{\sqrt{x} \cdot 3}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x 25.5)
   (/ (fma (* (sqrt x) y) -0.3333333333333333 -0.1111111111111111) x)
   (- 1.0 (/ y (* (sqrt x) 3.0)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= 25.5) {
		tmp = fma((sqrt(x) * y), -0.3333333333333333, -0.1111111111111111) / x;
	} else {
		tmp = 1.0 - (y / (sqrt(x) * 3.0));
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (x <= 25.5)
		tmp = Float64(fma(Float64(sqrt(x) * y), -0.3333333333333333, -0.1111111111111111) / x);
	else
		tmp = Float64(1.0 - Float64(y / Float64(sqrt(x) * 3.0)));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, 25.5], N[(N[(N[(N[Sqrt[x], $MachinePrecision] * y), $MachinePrecision] * -0.3333333333333333 + -0.1111111111111111), $MachinePrecision] / x), $MachinePrecision], N[(1.0 - N[(y / N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 25.5

   1. Initial program 99.5%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-1 \cdot \frac{\frac{1}{9} + \frac{1}{3} \cdot \left(\sqrt{x} \cdot y\right)}{x}} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(\frac{\frac{1}{9} + \frac{1}{3} \cdot \left(\sqrt{x} \cdot y\right)}{x}\right)} \]

      2. distribute-neg-frac N/A

         \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(\left(\frac{1}{9} + \frac{1}{3} \cdot \left(\sqrt{x} \cdot y\right)\right)\right)}{x}} \]

      3. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(\left(\frac{1}{9} + \frac{1}{3} \cdot \left(\sqrt{x} \cdot y\right)\right)\right)}{x}} \]

      4. +-commutative N/A

         \[\leadsto \frac{\mathsf{neg}\left(\color{blue}{\left(\frac{1}{3} \cdot \left(\sqrt{x} \cdot y\right) + \frac{1}{9}\right)}\right)}{x} \]

      5. distribute-neg-in N/A

         \[\leadsto \frac{\color{blue}{\left(\mathsf{neg}\left(\frac{1}{3} \cdot \left(\sqrt{x} \cdot y\right)\right)\right) + \left(\mathsf{neg}\left(\frac{1}{9}\right)\right)}}{x} \]

      6. metadata-eval N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(\frac{1}{3} \cdot \left(\sqrt{x} \cdot y\right)\right)\right) + \color{blue}{\frac{-1}{9}}}{x} \]

      7. *-commutative N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(\color{blue}{\left(\sqrt{x} \cdot y\right) \cdot \frac{1}{3}}\right)\right) + \frac{-1}{9}}{x} \]

      8. distribute-rgt-neg-in N/A

         \[\leadsto \frac{\color{blue}{\left(\sqrt{x} \cdot y\right) \cdot \left(\mathsf{neg}\left(\frac{1}{3}\right)\right)} + \frac{-1}{9}}{x} \]

      9. metadata-eval N/A

         \[\leadsto \frac{\left(\sqrt{x} \cdot y\right) \cdot \color{blue}{\frac{-1}{3}} + \frac{-1}{9}}{x} \]

      10. lower-fma.f64 N/A

          \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\sqrt{x} \cdot y, \frac{-1}{3}, \frac{-1}{9}\right)}}{x} \]

      11. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\sqrt{x} \cdot y}, \frac{-1}{3}, \frac{-1}{9}\right)}{x} \]

      12. lower-sqrt.f64 97.9

          \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\sqrt{x}} \cdot y, -0.3333333333333333, -0.1111111111111111\right)}{x} \]

   5. Applied rewrites 97.9%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\sqrt{x} \cdot y, -0.3333333333333333, -0.1111111111111111\right)}{x}} \]

   if 25.5 < x

   1. Initial program 99.7%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{1} - \frac{y}{3 \cdot \sqrt{x}} \]
   4. Step-by-step derivation

   5. Applied rewrites 99.2%

      \[\leadsto \color{blue}{1} - \frac{y}{3 \cdot \sqrt{x}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 25.5:\\ \;\;\;\;\frac{\mathsf{fma}\left(\sqrt{x} \cdot y, -0.3333333333333333, -0.1111111111111111\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{y}{\sqrt{x} \cdot 3}\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 62.6% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ 1 - \frac{\frac{-1}{x}}{-9} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- 1.0 (/ (/ -1.0 x) -9.0)))

C

double code(double x, double y) {
	return 1.0 - ((-1.0 / x) / -9.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 - (((-1.0d0) / x) / (-9.0d0))
end function

Java

public static double code(double x, double y) {
	return 1.0 - ((-1.0 / x) / -9.0);
}

Python

def code(x, y):
	return 1.0 - ((-1.0 / x) / -9.0)

Julia

function code(x, y)
	return Float64(1.0 - Float64(Float64(-1.0 / x) / -9.0))
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 - ((-1.0 / x) / -9.0);
end

Wolfram

code[x_, y_] := N[(1.0 - N[(N[(-1.0 / x), $MachinePrecision] / -9.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]
4. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]

   2. associate-*r/ N/A

      \[\leadsto 1 - \color{blue}{\frac{\frac{1}{9} \cdot 1}{x}} \]

   3. metadata-eval N/A

      \[\leadsto 1 - \frac{\color{blue}{\frac{1}{9}}}{x} \]

   4. lower-/.f64 61.6

      \[\leadsto 1 - \color{blue}{\frac{0.1111111111111111}{x}} \]

5. Applied rewrites 61.6%

   \[\leadsto \color{blue}{1 - \frac{0.1111111111111111}{x}} \]
6. Step-by-step derivation

7. Applied rewrites 61.4%

   \[\leadsto 1 - \frac{\frac{0.3333333333333333}{\sqrt{x}}}{\color{blue}{\sqrt{x} \cdot 3}} \]
8. Step-by-step derivation

9. Applied rewrites 61.6%

   \[\leadsto 1 - \frac{\frac{-1}{x}}{\color{blue}{-9}} \]
10. Add Preprocessing

Alternative 12: 62.6% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ 1 - \frac{1}{9 \cdot x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- 1.0 (/ 1.0 (* 9.0 x))))

C

double code(double x, double y) {
	return 1.0 - (1.0 / (9.0 * x));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 - (1.0d0 / (9.0d0 * x))
end function

Java

public static double code(double x, double y) {
	return 1.0 - (1.0 / (9.0 * x));
}

Python

def code(x, y):
	return 1.0 - (1.0 / (9.0 * x))

Julia

function code(x, y)
	return Float64(1.0 - Float64(1.0 / Float64(9.0 * x)))
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 - (1.0 / (9.0 * x));
end

Wolfram

code[x_, y_] := N[(1.0 - N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]
4. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]

   2. associate-*r/ N/A

      \[\leadsto 1 - \color{blue}{\frac{\frac{1}{9} \cdot 1}{x}} \]

   3. metadata-eval N/A

      \[\leadsto 1 - \frac{\color{blue}{\frac{1}{9}}}{x} \]

   4. lower-/.f64 61.6

      \[\leadsto 1 - \color{blue}{\frac{0.1111111111111111}{x}} \]

5. Applied rewrites 61.6%

   \[\leadsto \color{blue}{1 - \frac{0.1111111111111111}{x}} \]
6. Step-by-step derivation

7. Applied rewrites 61.6%

   \[\leadsto 1 - \frac{1}{\color{blue}{9 \cdot x}} \]
8. Add Preprocessing

Alternative 13: 62.5% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\frac{-1}{x}, 0.1111111111111111, 1\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma (/ -1.0 x) 0.1111111111111111 1.0))

C

double code(double x, double y) {
	return fma((-1.0 / x), 0.1111111111111111, 1.0);
}

Julia

function code(x, y)
	return fma(Float64(-1.0 / x), 0.1111111111111111, 1.0)
end

Wolfram

code[x_, y_] := N[(N[(-1.0 / x), $MachinePrecision] * 0.1111111111111111 + 1.0), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]
4. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]

   2. associate-*r/ N/A

      \[\leadsto 1 - \color{blue}{\frac{\frac{1}{9} \cdot 1}{x}} \]

   3. metadata-eval N/A

      \[\leadsto 1 - \frac{\color{blue}{\frac{1}{9}}}{x} \]

   4. lower-/.f64 61.6

      \[\leadsto 1 - \color{blue}{\frac{0.1111111111111111}{x}} \]

5. Applied rewrites 61.6%

   \[\leadsto \color{blue}{1 - \frac{0.1111111111111111}{x}} \]
6. Step-by-step derivation

7. Applied rewrites 61.4%

   \[\leadsto 1 - \frac{\frac{0.3333333333333333}{\sqrt{x}}}{\color{blue}{\sqrt{x} \cdot 3}} \]
8. Step-by-step derivation

9. Applied rewrites 61.6%

   \[\leadsto \mathsf{fma}\left(\frac{-1}{x}, \color{blue}{0.1111111111111111}, 1\right) \]
10. Add Preprocessing

Alternative 14: 62.6% accurate, 3.3× speedup

Math

\[\begin{array}{l} \\ 1 - \frac{0.1111111111111111}{x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- 1.0 (/ 0.1111111111111111 x)))

C

double code(double x, double y) {
	return 1.0 - (0.1111111111111111 / x);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 - (0.1111111111111111d0 / x)
end function

Java

public static double code(double x, double y) {
	return 1.0 - (0.1111111111111111 / x);
}

Python

def code(x, y):
	return 1.0 - (0.1111111111111111 / x)

Julia

function code(x, y)
	return Float64(1.0 - Float64(0.1111111111111111 / x))
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 - (0.1111111111111111 / x);
end

Wolfram

code[x_, y_] := N[(1.0 - N[(0.1111111111111111 / x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]
4. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]

   2. associate-*r/ N/A

      \[\leadsto 1 - \color{blue}{\frac{\frac{1}{9} \cdot 1}{x}} \]

   3. metadata-eval N/A

      \[\leadsto 1 - \frac{\color{blue}{\frac{1}{9}}}{x} \]

   4. lower-/.f64 61.6

      \[\leadsto 1 - \color{blue}{\frac{0.1111111111111111}{x}} \]

5. Applied rewrites 61.6%

   \[\leadsto \color{blue}{1 - \frac{0.1111111111111111}{x}} \]
6. Add Preprocessing

Alternative 15: 31.8% accurate, 49.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 1.0)

C

double code(double x, double y) {
	return 1.0;
}

Fortran

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

Java

public static double code(double x, double y) {
	return 1.0;
}

Python

def code(x, y):
	return 1.0

Julia

function code(x, y)
	return 1.0
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0;
end

Wolfram

code[x_, y_] := 1.0

Derivation

1. Initial program 99.6%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]
4. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto \color{blue}{1 - \frac{1}{9} \cdot \frac{1}{x}} \]

   2. associate-*r/ N/A

      \[\leadsto 1 - \color{blue}{\frac{\frac{1}{9} \cdot 1}{x}} \]

   3. metadata-eval N/A

      \[\leadsto 1 - \frac{\color{blue}{\frac{1}{9}}}{x} \]

   4. lower-/.f64 61.6

      \[\leadsto 1 - \color{blue}{\frac{0.1111111111111111}{x}} \]

5. Applied rewrites 61.6%

   \[\leadsto \color{blue}{1 - \frac{0.1111111111111111}{x}} \]

6. Taylor expanded in x around inf

   \[\leadsto 1 \]
7. Step-by-step derivation

8. Applied rewrites 30.3%

   \[\leadsto 1 \]
9. Add Preprocessing

Developer Target 1: 99.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \left(1 - \frac{\frac{1}{x}}{9}\right) - \frac{y}{3 \cdot \sqrt{x}} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (- (- 1.0 (/ (/ 1.0 x) 9.0)) (/ y (* 3.0 (sqrt x)))))

C

double code(double x, double y) {
	return (1.0 - ((1.0 / x) / 9.0)) - (y / (3.0 * sqrt(x)));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - ((1.0d0 / x) / 9.0d0)) - (y / (3.0d0 * sqrt(x)))
end function

Java

public static double code(double x, double y) {
	return (1.0 - ((1.0 / x) / 9.0)) - (y / (3.0 * Math.sqrt(x)));
}

Python

def code(x, y):
	return (1.0 - ((1.0 / x) / 9.0)) - (y / (3.0 * math.sqrt(x)))

Julia

function code(x, y)
	return Float64(Float64(1.0 - Float64(Float64(1.0 / x) / 9.0)) - Float64(y / Float64(3.0 * sqrt(x))))
end

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - ((1.0 / x) / 9.0)) - (y / (3.0 * sqrt(x)));
end

Wolfram

code[x_, y_] := N[(N[(1.0 - N[(N[(1.0 / x), $MachinePrecision] / 9.0), $MachinePrecision]), $MachinePrecision] - N[(y / N[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024243 
(FPCore (x y)
  :name "Numeric.SpecFunctions:invIncompleteGamma from math-functions-0.1.5.2, D"
  :precision binary64

  :alt
  (! :herbie-platform default (- (- 1 (/ (/ 1 x) 9)) (/ y (* 3 (sqrt x)))))

  (- (- 1.0 (/ 1.0 (* x 9.0))) (/ y (* 3.0 (sqrt x)))))