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

Percentage Accurate: 99.7% → 99.7%

Time: 7.6s

Alternatives: 18

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, 0.9× speedup

Math

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

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(Float64(y / 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[(N[(y / N[Sqrt[x], $MachinePrecision]), $MachinePrecision] / 3.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
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. lift-*.f64 N/A

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

   3. associate-/l/ N/A

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

   4. lower-/.f64 N/A

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

   5. lower-/.f64 99.7

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

4. Applied rewrites 99.7%

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

5. Final simplification 99.7%

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

Alternative 2: 61.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(1 - \frac{1}{9 \cdot x}\right) - \frac{y}{3 \cdot \sqrt{x}} \leq -5000:\\ \;\;\;\;\frac{-0.1111111111111111}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.1111111111111111}{x} + 1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (- (- 1.0 (/ 1.0 (* 9.0 x))) (/ y (* 3.0 (sqrt x)))) -5000.0)
   (/ -0.1111111111111111 x)
   (+ (/ 0.1111111111111111 x) 1.0)))

C

double code(double x, double y) {
	double tmp;
	if (((1.0 - (1.0 / (9.0 * x))) - (y / (3.0 * sqrt(x)))) <= -5000.0) {
		tmp = -0.1111111111111111 / x;
	} else {
		tmp = (0.1111111111111111 / x) + 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 / (3.0d0 * sqrt(x)))) <= (-5000.0d0)) then
        tmp = (-0.1111111111111111d0) / x
    else
        tmp = (0.1111111111111111d0 / x) + 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 / (3.0 * Math.sqrt(x)))) <= -5000.0) {
		tmp = -0.1111111111111111 / x;
	} else {
		tmp = (0.1111111111111111 / x) + 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((1.0 - (1.0 / (9.0 * x))) - (y / (3.0 * math.sqrt(x)))) <= -5000.0:
		tmp = -0.1111111111111111 / x
	else:
		tmp = (0.1111111111111111 / x) + 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(3.0 * sqrt(x)))) <= -5000.0)
		tmp = Float64(-0.1111111111111111 / x);
	else
		tmp = Float64(Float64(0.1111111111111111 / x) + 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 / (3.0 * sqrt(x)))) <= -5000.0)
		tmp = -0.1111111111111111 / x;
	else
		tmp = (0.1111111111111111 / x) + 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[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -5000.0], N[(-0.1111111111111111 / x), $MachinePrecision], N[(N[(0.1111111111111111 / x), $MachinePrecision] + 1.0), $MachinePrecision]]

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)))) < -5e3

   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 61.9

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

   5. Applied rewrites 61.9%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 60.9%

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

   if -5e3 < (-.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 99.7%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   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 59.3

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

   5. Applied rewrites 59.3%

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

   7. Applied rewrites 59.3%

      \[\leadsto \frac{0.1111111111111111}{x} + \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 60.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(1 - \frac{1}{9 \cdot x}\right) - \frac{y}{3 \cdot \sqrt{x}} \leq -5000:\\ \;\;\;\;\frac{-0.1111111111111111}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.1111111111111111}{x} + 1\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 61.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(1 - \frac{1}{9 \cdot x}\right) - \frac{y}{3 \cdot \sqrt{x}} \leq -5000:\\ \;\;\;\;\frac{-0.1111111111111111}{x}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (- (- 1.0 (/ 1.0 (* 9.0 x))) (/ y (* 3.0 (sqrt x)))) -5000.0)
   (/ -0.1111111111111111 x)
   1.0))

C

double code(double x, double y) {
	double tmp;
	if (((1.0 - (1.0 / (9.0 * x))) - (y / (3.0 * sqrt(x)))) <= -5000.0) {
		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 / (3.0d0 * sqrt(x)))) <= (-5000.0d0)) 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 / (3.0 * Math.sqrt(x)))) <= -5000.0) {
		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 / (3.0 * math.sqrt(x)))) <= -5000.0:
		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(3.0 * sqrt(x)))) <= -5000.0)
		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 / (3.0 * sqrt(x)))) <= -5000.0)
		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[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -5000.0], 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)))) < -5e3

   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 61.9

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

   5. Applied rewrites 61.9%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 60.9%

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

   if -5e3 < (-.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 99.7%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   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 59.3

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

   5. Applied rewrites 59.3%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 58.7%

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

4. Final simplification 59.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(1 - \frac{1}{9 \cdot x}\right) - \frac{y}{3 \cdot \sqrt{x}} \leq -5000:\\ \;\;\;\;\frac{-0.1111111111111111}{x}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y) {
	return (1.0 - (1.0 / (9.0 * x))) - (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 / (9.0d0 * x))) - (y / (3.0d0 * sqrt(x)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.7%

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

3. Final simplification 99.7%

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

Alternative 5: 98.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.00068:\\ \;\;\;\;\frac{-0.1111111111111111}{x} - \frac{y}{3 \cdot \sqrt{x}}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{\frac{y}{\sqrt{x}}}{3}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x 0.00068)
   (- (/ -0.1111111111111111 x) (/ y (* 3.0 (sqrt x))))
   (- 1.0 (/ (/ y (sqrt x)) 3.0))))

C

double code(double x, double y) {
	double tmp;
	if (x <= 0.00068) {
		tmp = (-0.1111111111111111 / x) - (y / (3.0 * sqrt(x)));
	} else {
		tmp = 1.0 - ((y / sqrt(x)) / 3.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= 0.00068d0) then
        tmp = ((-0.1111111111111111d0) / x) - (y / (3.0d0 * sqrt(x)))
    else
        tmp = 1.0d0 - ((y / sqrt(x)) / 3.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= 0.00068) {
		tmp = (-0.1111111111111111 / x) - (y / (3.0 * Math.sqrt(x)));
	} else {
		tmp = 1.0 - ((y / Math.sqrt(x)) / 3.0);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= 0.00068:
		tmp = (-0.1111111111111111 / x) - (y / (3.0 * math.sqrt(x)))
	else:
		tmp = 1.0 - ((y / math.sqrt(x)) / 3.0)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= 0.00068)
		tmp = Float64(Float64(-0.1111111111111111 / x) - Float64(y / Float64(3.0 * sqrt(x))));
	else
		tmp = Float64(1.0 - Float64(Float64(y / sqrt(x)) / 3.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= 0.00068)
		tmp = (-0.1111111111111111 / x) - (y / (3.0 * sqrt(x)));
	else
		tmp = 1.0 - ((y / sqrt(x)) / 3.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, 0.00068], N[(N[(-0.1111111111111111 / x), $MachinePrecision] - N[(y / N[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(1.0 - N[(N[(y / N[Sqrt[x], $MachinePrecision]), $MachinePrecision] / 3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 6.8e-4

   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 0

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

      1. lower-/.f64 97.9

         \[\leadsto \color{blue}{\frac{-0.1111111111111111}{x}} - \frac{y}{3 \cdot \sqrt{x}} \]

   5. Applied rewrites 97.9%

      \[\leadsto \color{blue}{\frac{-0.1111111111111111}{x}} - \frac{y}{3 \cdot \sqrt{x}} \]

   if 6.8e-4 < x

   1. Initial program 99.8%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   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. lift-*.f64 N/A

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

      3. associate-/l/ N/A

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

      4. lower-/.f64 N/A

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

      5. lower-/.f64 99.8

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

   4. Applied rewrites 99.8%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 98.6%

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

Alternative 6: 99.6% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.7%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
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. Final simplification 99.6%

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

Alternative 7: 94.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{y}{3 \cdot \sqrt{x}}\\ \mathbf{if}\;y \leq -1.1 \cdot 10^{+41}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1.02 \cdot 10^{+77}:\\ \;\;\;\;1 - \frac{1}{9 \cdot x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ y (* 3.0 (sqrt x))))))
   (if (<= y -1.1e+41)
     t_0
     (if (<= y 1.02e+77) (- 1.0 (/ 1.0 (* 9.0 x))) t_0))))

C

double code(double x, double y) {
	double t_0 = 1.0 - (y / (3.0 * sqrt(x)));
	double tmp;
	if (y <= -1.1e+41) {
		tmp = t_0;
	} else if (y <= 1.02e+77) {
		tmp = 1.0 - (1.0 / (9.0 * x));
	} 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 / (3.0d0 * sqrt(x)))
    if (y <= (-1.1d+41)) then
        tmp = t_0
    else if (y <= 1.02d+77) then
        tmp = 1.0d0 - (1.0d0 / (9.0d0 * x))
    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 / (3.0 * Math.sqrt(x)));
	double tmp;
	if (y <= -1.1e+41) {
		tmp = t_0;
	} else if (y <= 1.02e+77) {
		tmp = 1.0 - (1.0 / (9.0 * x));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - (y / (3.0 * math.sqrt(x)))
	tmp = 0
	if y <= -1.1e+41:
		tmp = t_0
	elif y <= 1.02e+77:
		tmp = 1.0 - (1.0 / (9.0 * x))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - Float64(y / Float64(3.0 * sqrt(x))))
	tmp = 0.0
	if (y <= -1.1e+41)
		tmp = t_0;
	elseif (y <= 1.02e+77)
		tmp = Float64(1.0 - Float64(1.0 / Float64(9.0 * x)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 1.0 - (y / (3.0 * sqrt(x)));
	tmp = 0.0;
	if (y <= -1.1e+41)
		tmp = t_0;
	elseif (y <= 1.02e+77)
		tmp = 1.0 - (1.0 / (9.0 * x));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 - N[(y / N[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.1e+41], t$95$0, If[LessEqual[y, 1.02e+77], N[(1.0 - N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.09999999999999995e41 or 1.02e77 < y

   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 x around inf

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

   5. Applied rewrites 94.8%

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

   if -1.09999999999999995e41 < y < 1.02e77

   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 98.7

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

   5. Applied rewrites 98.7%

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

   7. Applied rewrites 98.8%

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

Alternative 8: 98.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.00068:\\ \;\;\;\;\frac{\mathsf{fma}\left(\sqrt{x} \cdot y, -0.3333333333333333, -0.1111111111111111\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{\frac{y}{\sqrt{x}}}{3}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x 0.00068)
   (/ (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 <= 0.00068) {
		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 <= 0.00068)
		tmp = Float64(fma(Float64(sqrt(x) * y), -0.3333333333333333, -0.1111111111111111) / x);
	else
		tmp = Float64(1.0 - Float64(Float64(y / sqrt(x)) / 3.0));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, 0.00068], N[(N[(N[(N[Sqrt[x], $MachinePrecision] * y), $MachinePrecision] * -0.3333333333333333 + -0.1111111111111111), $MachinePrecision] / x), $MachinePrecision], N[(1.0 - N[(N[(y / N[Sqrt[x], $MachinePrecision]), $MachinePrecision] / 3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 6.8e-4

   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 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 6.8e-4 < x

   1. Initial program 99.8%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   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. lift-*.f64 N/A

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

      3. associate-/l/ N/A

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

      4. lower-/.f64 N/A

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

      5. lower-/.f64 99.8

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

   4. Applied rewrites 99.8%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 98.6%

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

Alternative 9: 99.6% accurate, 1.2× speedup

Math

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

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

Wolfram

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

Derivation

1. Initial program 99.7%

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

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

   5. associate-+r- N/A

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

   6. lower--.f64 N/A

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

   7. lift-/.f64 N/A

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

   8. distribute-neg-frac N/A

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

   9. neg-mul-1 N/A

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

   10. lift-*.f64 N/A

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

   11. times-frac N/A

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

   12. metadata-eval N/A

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

   13. metadata-eval N/A

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

   14. metadata-eval N/A

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

   15. lower-fma.f64 N/A

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

   16. metadata-eval N/A

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

   17. metadata-eval N/A

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

   18. lower-/.f64 99.7

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

   19. lift-/.f64 N/A

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

   20. lift-*.f64 N/A

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

   21. *-commutative N/A

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

   22. associate-/r* N/A

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

4. Applied rewrites 99.6%

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

Alternative 10: 94.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(\frac{y}{\sqrt{x}}, -0.3333333333333333, 1\right)\\ \mathbf{if}\;y \leq -1.1 \cdot 10^{+41}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1.02 \cdot 10^{+77}:\\ \;\;\;\;1 - \frac{1}{9 \cdot x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (fma (/ y (sqrt x)) -0.3333333333333333 1.0)))
   (if (<= y -1.1e+41)
     t_0
     (if (<= y 1.02e+77) (- 1.0 (/ 1.0 (* 9.0 x))) t_0))))

C

double code(double x, double y) {
	double t_0 = fma((y / sqrt(x)), -0.3333333333333333, 1.0);
	double tmp;
	if (y <= -1.1e+41) {
		tmp = t_0;
	} else if (y <= 1.02e+77) {
		tmp = 1.0 - (1.0 / (9.0 * x));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = fma(Float64(y / sqrt(x)), -0.3333333333333333, 1.0)
	tmp = 0.0
	if (y <= -1.1e+41)
		tmp = t_0;
	elseif (y <= 1.02e+77)
		tmp = Float64(1.0 - Float64(1.0 / Float64(9.0 * x)));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(y / N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * -0.3333333333333333 + 1.0), $MachinePrecision]}, If[LessEqual[y, -1.1e+41], t$95$0, If[LessEqual[y, 1.02e+77], N[(1.0 - N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.09999999999999995e41 or 1.02e77 < y

   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 x around inf

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

      1. cancel-sign-sub-inv N/A

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

      2. metadata-eval N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

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

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

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

      8. lower-fma.f64 N/A

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

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

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

      10. metadata-eval N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. lower-sqrt.f64 N/A

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

      14. lower-/.f64 94.7

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

   5. Applied rewrites 94.7%

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

   7. Applied rewrites 94.6%

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

   if -1.09999999999999995e41 < y < 1.02e77

   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 98.7

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

   5. Applied rewrites 98.7%

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

   7. Applied rewrites 98.8%

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

Alternative 11: 94.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(\frac{-0.3333333333333333}{\sqrt{x}}, y, 1\right)\\ \mathbf{if}\;y \leq -1.1 \cdot 10^{+41}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1.02 \cdot 10^{+77}:\\ \;\;\;\;1 - \frac{1}{9 \cdot x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (fma (/ -0.3333333333333333 (sqrt x)) y 1.0)))
   (if (<= y -1.1e+41)
     t_0
     (if (<= y 1.02e+77) (- 1.0 (/ 1.0 (* 9.0 x))) t_0))))

C

double code(double x, double y) {
	double t_0 = fma((-0.3333333333333333 / sqrt(x)), y, 1.0);
	double tmp;
	if (y <= -1.1e+41) {
		tmp = t_0;
	} else if (y <= 1.02e+77) {
		tmp = 1.0 - (1.0 / (9.0 * x));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = fma(Float64(-0.3333333333333333 / sqrt(x)), y, 1.0)
	tmp = 0.0
	if (y <= -1.1e+41)
		tmp = t_0;
	elseif (y <= 1.02e+77)
		tmp = Float64(1.0 - Float64(1.0 / Float64(9.0 * x)));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(-0.3333333333333333 / N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * y + 1.0), $MachinePrecision]}, If[LessEqual[y, -1.1e+41], t$95$0, If[LessEqual[y, 1.02e+77], N[(1.0 - N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.09999999999999995e41 or 1.02e77 < y

   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 x around inf

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

      1. cancel-sign-sub-inv N/A

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

      2. metadata-eval N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

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

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

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

      8. lower-fma.f64 N/A

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

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

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

      10. metadata-eval N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. lower-sqrt.f64 N/A

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

      14. lower-/.f64 94.7

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

   5. Applied rewrites 94.7%

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

   7. Applied rewrites 94.6%

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

   if -1.09999999999999995e41 < y < 1.02e77

   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 98.7

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

   5. Applied rewrites 98.7%

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

   7. Applied rewrites 98.8%

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

Alternative 12: 92.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{y}{-3 \cdot \sqrt{x}}\\ \mathbf{if}\;y \leq -6 \cdot 10^{+90}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 3.4 \cdot 10^{+84}:\\ \;\;\;\;1 - \frac{1}{9 \cdot x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ y (* -3.0 (sqrt x)))))
   (if (<= y -6e+90) t_0 (if (<= y 3.4e+84) (- 1.0 (/ 1.0 (* 9.0 x))) t_0))))

C

double code(double x, double y) {
	double t_0 = y / (-3.0 * sqrt(x));
	double tmp;
	if (y <= -6e+90) {
		tmp = t_0;
	} else if (y <= 3.4e+84) {
		tmp = 1.0 - (1.0 / (9.0 * x));
	} 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 = y / ((-3.0d0) * sqrt(x))
    if (y <= (-6d+90)) then
        tmp = t_0
    else if (y <= 3.4d+84) then
        tmp = 1.0d0 - (1.0d0 / (9.0d0 * x))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = y / (-3.0 * Math.sqrt(x));
	double tmp;
	if (y <= -6e+90) {
		tmp = t_0;
	} else if (y <= 3.4e+84) {
		tmp = 1.0 - (1.0 / (9.0 * x));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = y / (-3.0 * math.sqrt(x))
	tmp = 0
	if y <= -6e+90:
		tmp = t_0
	elif y <= 3.4e+84:
		tmp = 1.0 - (1.0 / (9.0 * x))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(y / Float64(-3.0 * sqrt(x)))
	tmp = 0.0
	if (y <= -6e+90)
		tmp = t_0;
	elseif (y <= 3.4e+84)
		tmp = Float64(1.0 - Float64(1.0 / Float64(9.0 * x)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = y / (-3.0 * sqrt(x));
	tmp = 0.0;
	if (y <= -6e+90)
		tmp = t_0;
	elseif (y <= 3.4e+84)
		tmp = 1.0 - (1.0 / (9.0 * x));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(y / N[(-3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -6e+90], t$95$0, If[LessEqual[y, 3.4e+84], N[(1.0 - N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -5.99999999999999957e90 or 3.3999999999999998e84 < y

   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 99.6

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

   4. Applied rewrites 99.6%

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

   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. metadata-eval N/A

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

      2. associate-*r* N/A

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

      3. *-commutative N/A

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

      4. associate-*r* N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

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

      7. associate-*r* N/A

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

      8. rem-square-sqrt N/A

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

      9. unpow2 N/A

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

      10. *-commutative N/A

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

      11. neg-mul-1 N/A

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

      12. lower-*.f64 N/A

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

   7. Applied rewrites 95.2%

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

   9. Applied rewrites 95.3%

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

   if -5.99999999999999957e90 < y < 3.3999999999999998e84

   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 95.3

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

   5. Applied rewrites 95.3%

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

   7. Applied rewrites 95.4%

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

Alternative 13: 92.7% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6 \cdot 10^{+90}:\\ \;\;\;\;\frac{-0.3333333333333333}{\sqrt{x}} \cdot y\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{+84}:\\ \;\;\;\;1 - \frac{1}{9 \cdot x}\\ \mathbf{else}:\\ \;\;\;\;-0.3333333333333333 \cdot \frac{y}{\sqrt{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -6e+90)
   (* (/ -0.3333333333333333 (sqrt x)) y)
   (if (<= y 4.2e+84)
     (- 1.0 (/ 1.0 (* 9.0 x)))
     (* -0.3333333333333333 (/ y (sqrt x))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -6e+90) {
		tmp = (-0.3333333333333333 / sqrt(x)) * y;
	} else if (y <= 4.2e+84) {
		tmp = 1.0 - (1.0 / (9.0 * x));
	} else {
		tmp = -0.3333333333333333 * (y / sqrt(x));
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-6d+90)) then
        tmp = ((-0.3333333333333333d0) / sqrt(x)) * y
    else if (y <= 4.2d+84) then
        tmp = 1.0d0 - (1.0d0 / (9.0d0 * x))
    else
        tmp = (-0.3333333333333333d0) * (y / sqrt(x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -6e+90) {
		tmp = (-0.3333333333333333 / Math.sqrt(x)) * y;
	} else if (y <= 4.2e+84) {
		tmp = 1.0 - (1.0 / (9.0 * x));
	} else {
		tmp = -0.3333333333333333 * (y / Math.sqrt(x));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -6e+90:
		tmp = (-0.3333333333333333 / math.sqrt(x)) * y
	elif y <= 4.2e+84:
		tmp = 1.0 - (1.0 / (9.0 * x))
	else:
		tmp = -0.3333333333333333 * (y / math.sqrt(x))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -6e+90)
		tmp = Float64(Float64(-0.3333333333333333 / sqrt(x)) * y);
	elseif (y <= 4.2e+84)
		tmp = Float64(1.0 - Float64(1.0 / Float64(9.0 * x)));
	else
		tmp = Float64(-0.3333333333333333 * Float64(y / sqrt(x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -6e+90)
		tmp = (-0.3333333333333333 / sqrt(x)) * y;
	elseif (y <= 4.2e+84)
		tmp = 1.0 - (1.0 / (9.0 * x));
	else
		tmp = -0.3333333333333333 * (y / sqrt(x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -6e+90], N[(N[(-0.3333333333333333 / N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], If[LessEqual[y, 4.2e+84], N[(1.0 - N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(-0.3333333333333333 * N[(y / N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -5.99999999999999957e90

   1. Initial program 99.4%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   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 99.4

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

   4. Applied rewrites 99.4%

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

   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. metadata-eval N/A

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

      2. associate-*r* N/A

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

      3. *-commutative N/A

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

      4. associate-*r* N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

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

      7. associate-*r* N/A

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

      8. rem-square-sqrt N/A

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

      9. unpow2 N/A

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

      10. *-commutative N/A

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

      11. neg-mul-1 N/A

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

      12. lower-*.f64 N/A

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

   7. Applied rewrites 95.7%

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

   9. Applied rewrites 95.7%

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

   if -5.99999999999999957e90 < y < 4.20000000000000037e84

   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 95.3

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

   5. Applied rewrites 95.3%

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

   7. Applied rewrites 95.4%

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

   if 4.20000000000000037e84 < y

   1. Initial program 99.7%

      \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
   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. 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. metadata-eval N/A

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

      2. associate-*r* N/A

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

      3. *-commutative N/A

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

      4. associate-*r* N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

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

      7. associate-*r* N/A

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

      8. rem-square-sqrt N/A

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

      9. unpow2 N/A

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

      10. *-commutative N/A

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

      11. neg-mul-1 N/A

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

      12. lower-*.f64 N/A

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

   7. Applied rewrites 94.8%

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

   9. Applied rewrites 94.7%

      \[\leadsto \frac{y}{\sqrt{x}} \cdot \color{blue}{-0.3333333333333333} \]
3. Recombined 3 regimes into one program.

4. Final simplification 95.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6 \cdot 10^{+90}:\\ \;\;\;\;\frac{-0.3333333333333333}{\sqrt{x}} \cdot y\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{+84}:\\ \;\;\;\;1 - \frac{1}{9 \cdot x}\\ \mathbf{else}:\\ \;\;\;\;-0.3333333333333333 \cdot \frac{y}{\sqrt{x}}\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 92.7% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-0.3333333333333333}{\sqrt{x}} \cdot y\\ \mathbf{if}\;y \leq -6 \cdot 10^{+90}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{+84}:\\ \;\;\;\;1 - \frac{1}{9 \cdot x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (/ -0.3333333333333333 (sqrt x)) y)))
   (if (<= y -6e+90) t_0 (if (<= y 4.2e+84) (- 1.0 (/ 1.0 (* 9.0 x))) t_0))))

C

double code(double x, double y) {
	double t_0 = (-0.3333333333333333 / sqrt(x)) * y;
	double tmp;
	if (y <= -6e+90) {
		tmp = t_0;
	} else if (y <= 4.2e+84) {
		tmp = 1.0 - (1.0 / (9.0 * x));
	} 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 <= (-6d+90)) then
        tmp = t_0
    else if (y <= 4.2d+84) then
        tmp = 1.0d0 - (1.0d0 / (9.0d0 * x))
    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 <= -6e+90) {
		tmp = t_0;
	} else if (y <= 4.2e+84) {
		tmp = 1.0 - (1.0 / (9.0 * x));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (-0.3333333333333333 / math.sqrt(x)) * y
	tmp = 0
	if y <= -6e+90:
		tmp = t_0
	elif y <= 4.2e+84:
		tmp = 1.0 - (1.0 / (9.0 * x))
	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 <= -6e+90)
		tmp = t_0;
	elseif (y <= 4.2e+84)
		tmp = Float64(1.0 - Float64(1.0 / Float64(9.0 * x)));
	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 <= -6e+90)
		tmp = t_0;
	elseif (y <= 4.2e+84)
		tmp = 1.0 - (1.0 / (9.0 * x));
	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, -6e+90], t$95$0, If[LessEqual[y, 4.2e+84], N[(1.0 - N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -5.99999999999999957e90 or 4.20000000000000037e84 < y

   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 99.6

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

   4. Applied rewrites 99.6%

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

   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. metadata-eval N/A

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

      2. associate-*r* N/A

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

      3. *-commutative N/A

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

      4. associate-*r* N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

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

      7. associate-*r* N/A

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

      8. rem-square-sqrt N/A

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

      9. unpow2 N/A

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

      10. *-commutative N/A

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

      11. neg-mul-1 N/A

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

      12. lower-*.f64 N/A

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

   7. Applied rewrites 95.2%

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

   9. Applied rewrites 95.2%

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

   if -5.99999999999999957e90 < y < 4.20000000000000037e84

   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 95.3

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

   5. Applied rewrites 95.3%

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

   7. Applied rewrites 95.4%

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

4. Final simplification 95.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6 \cdot 10^{+90}:\\ \;\;\;\;\frac{-0.3333333333333333}{\sqrt{x}} \cdot y\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{+84}:\\ \;\;\;\;1 - \frac{1}{9 \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-0.3333333333333333}{\sqrt{x}} \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 15: 98.3% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.00068:\\ \;\;\;\;\frac{\mathsf{fma}\left(\sqrt{x} \cdot y, -0.3333333333333333, -0.1111111111111111\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{y}{3 \cdot \sqrt{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x 0.00068)
   (/ (fma (* (sqrt x) y) -0.3333333333333333 -0.1111111111111111) x)
   (- 1.0 (/ y (* 3.0 (sqrt x))))))

C

double code(double x, double y) {
	double tmp;
	if (x <= 0.00068) {
		tmp = fma((sqrt(x) * y), -0.3333333333333333, -0.1111111111111111) / x;
	} else {
		tmp = 1.0 - (y / (3.0 * sqrt(x)));
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (x <= 0.00068)
		tmp = Float64(fma(Float64(sqrt(x) * y), -0.3333333333333333, -0.1111111111111111) / x);
	else
		tmp = Float64(1.0 - Float64(y / Float64(3.0 * sqrt(x))));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, 0.00068], N[(N[(N[(N[Sqrt[x], $MachinePrecision] * y), $MachinePrecision] * -0.3333333333333333 + -0.1111111111111111), $MachinePrecision] / x), $MachinePrecision], N[(1.0 - N[(y / N[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 6.8e-4

   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 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 6.8e-4 < 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 98.6%

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

Alternative 16: 62.1% 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 99.7%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
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 60.6

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

5. Applied rewrites 60.6%

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

7. Applied rewrites 60.7%

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

Alternative 17: 62.0% 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 99.7%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
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 60.6

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

5. Applied rewrites 60.6%

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

Alternative 18: 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 99.7%

   \[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}} \]
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 60.6

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

5. Applied rewrites 60.6%

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

6. Taylor expanded in x around inf

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

8. Applied rewrites 29.4%

   \[\leadsto 1 \]
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 2024270 
(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)))))