Numeric.SpecFunctions:incompleteGamma from math-functions-0.1.5.2, B

Percentage Accurate: 99.4% → 99.5%

Time: 8.7s

Alternatives: 8

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\sqrt{x}, \mathsf{fma}\left(y, 3, -3\right), \frac{0.3333333333333333}{\sqrt{x}}\right) \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (fma (sqrt x) (fma y 3.0 -3.0) (/ 0.3333333333333333 (sqrt x))))

C

double code(double x, double y) {
	return fma(sqrt(x), fma(y, 3.0, -3.0), (0.3333333333333333 / sqrt(x)));
}

Julia

function code(x, y)
	return fma(sqrt(x), fma(y, 3.0, -3.0), Float64(0.3333333333333333 / sqrt(x)))
end

Wolfram

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

Derivation

1. Initial program 99.5%

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

3. Taylor expanded in x around 0

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

   1. lower-/.f64 N/A

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

   2. +-commutative N/A

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

   3. associate-*r* N/A

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

   4. *-commutative N/A

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

   5. associate-*r* N/A

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

   6. lower-fma.f64 N/A

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

   7. *-commutative N/A

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

   8. sub-neg N/A

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

   9. metadata-eval N/A

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

   10. distribute-rgt-in N/A

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

   11. metadata-eval N/A

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

   12. lower-fma.f64 N/A

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

   13. lower-sqrt.f64 N/A

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

   14. lower-pow.f64 N/A

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

   15. lower-*.f64 N/A

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

   16. lower-sqrt.f64 63.7

       \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(y, 3, -3\right), \sqrt{{x}^{3}}, 0.3333333333333333 \cdot \color{blue}{\sqrt{x}}\right)}{x} \]

5. Applied rewrites 63.7%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(y, 3, -3\right), \sqrt{{x}^{3}}, 0.3333333333333333 \cdot \sqrt{x}\right)}{x}} \]

6. Taylor expanded in y around 0

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

8. Applied rewrites 99.6%

   \[\leadsto \mathsf{fma}\left(\sqrt{\frac{1}{x}}, \color{blue}{0.3333333333333333}, \mathsf{fma}\left(y, 3, -3\right) \cdot \sqrt{x}\right) \]

10. Applied rewrites 99.6%

    \[\leadsto \color{blue}{\mathsf{fma}\left(\sqrt{x}, \mathsf{fma}\left(y, 3, -3\right), \frac{0.3333333333333333}{\sqrt{x}}\right)} \]
11. Add Preprocessing

Alternative 2: 91.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(y, 3, -3\right) \cdot \sqrt{x}\\ t_1 := \left(\sqrt{x} \cdot 3\right) \cdot \left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right)\\ \mathbf{if}\;t\_1 \leq -0.05:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+150}:\\ \;\;\;\;\frac{0.3333333333333333}{\sqrt{x}}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (fma y 3.0 -3.0) (sqrt x)))
        (t_1 (* (* (sqrt x) 3.0) (- (+ (/ 1.0 (* 9.0 x)) y) 1.0))))
   (if (<= t_1 -0.05)
     t_0
     (if (<= t_1 5e+150) (/ 0.3333333333333333 (sqrt x)) t_0))))

C

double code(double x, double y) {
	double t_0 = fma(y, 3.0, -3.0) * sqrt(x);
	double t_1 = (sqrt(x) * 3.0) * (((1.0 / (9.0 * x)) + y) - 1.0);
	double tmp;
	if (t_1 <= -0.05) {
		tmp = t_0;
	} else if (t_1 <= 5e+150) {
		tmp = 0.3333333333333333 / sqrt(x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(fma(y, 3.0, -3.0) * sqrt(x))
	t_1 = Float64(Float64(sqrt(x) * 3.0) * Float64(Float64(Float64(1.0 / Float64(9.0 * x)) + y) - 1.0))
	tmp = 0.0
	if (t_1 <= -0.05)
		tmp = t_0;
	elseif (t_1 <= 5e+150)
		tmp = Float64(0.3333333333333333 / sqrt(x));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(y * 3.0 + -3.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision] * N[(N[(N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -0.05], t$95$0, If[LessEqual[t$95$1, 5e+150], N[(0.3333333333333333 / N[Sqrt[x], $MachinePrecision]), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 3 binary64) (sqrt.f64 x)) (-.f64 (+.f64 y (/.f64 #s(literal 1 binary64) (*.f64 x #s(literal 9 binary64)))) #s(literal 1 binary64))) < -0.050000000000000003 or 5.00000000000000009e150 < (*.f64 (*.f64 #s(literal 3 binary64) (sqrt.f64 x)) (-.f64 (+.f64 y (/.f64 #s(literal 1 binary64) (*.f64 x #s(literal 9 binary64)))) #s(literal 1 binary64)))

   1. Initial program 99.6%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. associate-*l* N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. *-commutative N/A

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

      6. sub-neg N/A

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

      7. metadata-eval N/A

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

      8. distribute-rgt-in N/A

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

      9. metadata-eval N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-sqrt.f64 97.9

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

   5. Applied rewrites 97.9%

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

   if -0.050000000000000003 < (*.f64 (*.f64 #s(literal 3 binary64) (sqrt.f64 x)) (-.f64 (+.f64 y (/.f64 #s(literal 1 binary64) (*.f64 x #s(literal 9 binary64)))) #s(literal 1 binary64))) < 5.00000000000000009e150

   1. Initial program 99.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-/.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. associate-/r* N/A

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

      6. frac-2neg N/A

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

      7. div-inv N/A

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

      8. neg-mul-1 N/A

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

      9. metadata-eval N/A

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

      10. metadata-eval N/A

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

      11. metadata-eval N/A

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

      12. metadata-eval N/A

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

      13. lower-fma.f64 N/A

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

      14. un-div-inv N/A

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

      15. lower-/.f64 N/A

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

      16. metadata-eval N/A

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

      17. metadata-eval 99.2

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

   4. Applied rewrites 99.2%

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

   5. Taylor expanded in x around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower-sqrt.f64 N/A

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

      4. lower-/.f64 79.2

         \[\leadsto \sqrt{\color{blue}{\frac{1}{x}}} \cdot 0.3333333333333333 \]

   7. Applied rewrites 79.2%

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

   9. Applied rewrites 79.2%

      \[\leadsto \color{blue}{\frac{0.3333333333333333}{\sqrt{x}}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 90.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\sqrt{x} \cdot 3\right) \cdot \left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \leq -0.05:\\ \;\;\;\;\mathsf{fma}\left(y, 3, -3\right) \cdot \sqrt{x}\\ \mathbf{elif}\;\left(\sqrt{x} \cdot 3\right) \cdot \left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \leq 5 \cdot 10^{+150}:\\ \;\;\;\;\frac{0.3333333333333333}{\sqrt{x}}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y, 3, -3\right) \cdot \sqrt{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.5%

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

3. Final simplification 99.5%

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

Alternative 4: 85.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(y, 3, -3\right) \cdot \sqrt{x}\\ \mathbf{if}\;y \leq -9.5 \cdot 10^{+15}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1.25 \cdot 10^{-17}:\\ \;\;\;\;\left(\frac{0.3333333333333333}{x} + -3\right) \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (fma y 3.0 -3.0) (sqrt x))))
   (if (<= y -9.5e+15)
     t_0
     (if (<= y 1.25e-17) (* (+ (/ 0.3333333333333333 x) -3.0) (sqrt x)) t_0))))

C

double code(double x, double y) {
	double t_0 = fma(y, 3.0, -3.0) * sqrt(x);
	double tmp;
	if (y <= -9.5e+15) {
		tmp = t_0;
	} else if (y <= 1.25e-17) {
		tmp = ((0.3333333333333333 / x) + -3.0) * sqrt(x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(fma(y, 3.0, -3.0) * sqrt(x))
	tmp = 0.0
	if (y <= -9.5e+15)
		tmp = t_0;
	elseif (y <= 1.25e-17)
		tmp = Float64(Float64(Float64(0.3333333333333333 / x) + -3.0) * sqrt(x));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(y * 3.0 + -3.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -9.5e+15], t$95$0, If[LessEqual[y, 1.25e-17], N[(N[(N[(0.3333333333333333 / x), $MachinePrecision] + -3.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -9.5e15 or 1.25e-17 < y

   1. Initial program 99.5%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. associate-*l* N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. *-commutative N/A

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

      6. sub-neg N/A

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

      7. metadata-eval N/A

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

      8. distribute-rgt-in N/A

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

      9. metadata-eval N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-sqrt.f64 84.7

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

   5. Applied rewrites 84.7%

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

   if -9.5e15 < y < 1.25e-17

   1. Initial program 99.4%

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

   3. Taylor expanded in y around 0

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

      1. *-commutative N/A

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

      2. associate-*r* N/A

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

      3. lower-*.f64 N/A

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

      4. sub-neg N/A

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

      5. metadata-eval N/A

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

      6. distribute-rgt-in N/A

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

      7. metadata-eval N/A

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

      8. lower-+.f64 N/A

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

      9. associate-*r/ N/A

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

      10. metadata-eval N/A

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

      11. associate-*l/ N/A

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

      12. metadata-eval N/A

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

      13. lower-/.f64 N/A

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

      14. lower-sqrt.f64 99.1

          \[\leadsto \left(\frac{0.3333333333333333}{x} + -3\right) \cdot \color{blue}{\sqrt{x}} \]

   5. Applied rewrites 99.1%

      \[\leadsto \color{blue}{\left(\frac{0.3333333333333333}{x} + -3\right) \cdot \sqrt{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 99.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(1 - y, -3, \frac{0.3333333333333333}{x}\right) \cdot \sqrt{x} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (* (fma (- 1.0 y) -3.0 (/ 0.3333333333333333 x)) (sqrt x)))

C

double code(double x, double y) {
	return fma((1.0 - y), -3.0, (0.3333333333333333 / x)) * sqrt(x);
}

Julia

function code(x, y)
	return Float64(fma(Float64(1.0 - y), -3.0, Float64(0.3333333333333333 / x)) * sqrt(x))
end

Wolfram

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

Derivation

1. Initial program 99.5%

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

3. Taylor expanded in y around 0

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

   1. +-commutative N/A

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

   2. associate-*r* N/A

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

   3. *-commutative N/A

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

   4. associate-*l* N/A

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

   5. *-commutative N/A

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

   6. associate-*l* N/A

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

   7. distribute-lft-out N/A

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

   8. lower-*.f64 N/A

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

   9. lower-sqrt.f64 N/A

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

   10. *-commutative N/A

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

   11. distribute-lft-in N/A

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

   12. +-commutative N/A

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

   13. associate-+r- N/A

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

   14. +-commutative N/A

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

   15. associate-+r- N/A

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

   16. +-commutative N/A

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

   17. distribute-rgt-in N/A

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

5. Applied rewrites 99.5%

   \[\leadsto \color{blue}{\sqrt{x} \cdot \mathsf{fma}\left(1 - y, -3, \frac{0.3333333333333333}{x}\right)} \]

6. Final simplification 99.5%

   \[\leadsto \mathsf{fma}\left(1 - y, -3, \frac{0.3333333333333333}{x}\right) \cdot \sqrt{x} \]
7. Add Preprocessing

Alternative 6: 61.9% accurate, 2.0× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.5%

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

3. Taylor expanded in x around inf

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

   1. *-commutative N/A

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

   2. associate-*l* N/A

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

   3. *-commutative N/A

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

   4. lower-*.f64 N/A

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

   5. *-commutative N/A

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

   6. sub-neg N/A

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

   7. metadata-eval N/A

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

   8. distribute-rgt-in N/A

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

   9. metadata-eval N/A

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

   10. lower-fma.f64 N/A

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

   11. lower-sqrt.f64 66.9

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

5. Applied rewrites 66.9%

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

Alternative 7: 37.5% accurate, 2.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (* (* (sqrt x) 3.0) y))

C

double code(double x, double y) {
	return (sqrt(x) * 3.0) * y;
}

Fortran

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

Java

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

Python

def code(x, y):
	return (math.sqrt(x) * 3.0) * y

Julia

function code(x, y)
	return Float64(Float64(sqrt(x) * 3.0) * y)
end

MATLAB

function tmp = code(x, y)
	tmp = (sqrt(x) * 3.0) * y;
end

Wolfram

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

Derivation

1. Initial program 99.5%

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

3. Taylor expanded in y around inf

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. lower-*.f64 N/A

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

   5. lower-sqrt.f64 43.6

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

5. Applied rewrites 43.6%

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

7. Applied rewrites 43.6%

   \[\leadsto \left(\sqrt{x} \cdot 3\right) \cdot \color{blue}{y} \]
8. Add Preprocessing

Alternative 8: 37.5% accurate, 2.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (* (* y 3.0) (sqrt x)))

C

double code(double x, double y) {
	return (y * 3.0) * sqrt(x);
}

Fortran

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

Java

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

Python

def code(x, y):
	return (y * 3.0) * math.sqrt(x)

Julia

function code(x, y)
	return Float64(Float64(y * 3.0) * sqrt(x))
end

MATLAB

function tmp = code(x, y)
	tmp = (y * 3.0) * sqrt(x);
end

Wolfram

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

Derivation

1. Initial program 99.5%

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

3. Taylor expanded in y around inf

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. lower-*.f64 N/A

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

   5. lower-sqrt.f64 43.6

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

5. Applied rewrites 43.6%

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

7. Applied rewrites 43.6%

   \[\leadsto \left(y \cdot 3\right) \cdot \color{blue}{\sqrt{x}} \]
8. Add Preprocessing

Developer Target 1: 99.4% accurate, 0.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024249 
(FPCore (x y)
  :name "Numeric.SpecFunctions:incompleteGamma from math-functions-0.1.5.2, B"
  :precision binary64

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

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