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

Percentage Accurate: 99.4% → 99.4%

Time: 9.0s

Alternatives: 11

Speedup: 1.2×

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: 99.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. lift--.f64 N/A

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

   3. lift-+.f64 N/A

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

   4. associate--l+ N/A

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

   5. distribute-rgt-in N/A

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

   6. lift-*.f64 N/A

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

   7. *-commutative N/A

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

   8. associate-*r* N/A

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

   9. lower-fma.f64 N/A

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

   10. lower-*.f64 N/A

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

   11. lift-*.f64 N/A

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

   12. associate-*r* N/A

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

   13. lower-*.f64 N/A

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

4. Applied rewrites 99.4%

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

5. Taylor expanded in x around 0

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

   1. lower-/.f64 N/A

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

   2. +-commutative N/A

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

   3. lower-fma.f64 99.4

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

7. Applied rewrites 99.4%

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

8. Taylor expanded in y around 0

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

10. Applied rewrites 99.4%

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

12. Applied rewrites 99.4%

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

13. Final simplification 99.4%

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

Alternative 2: 90.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right)\\ \mathbf{if}\;t\_0 \leq -5 \cdot 10^{+64}:\\ \;\;\;\;\left(\left(y - 1\right) \cdot \sqrt{x}\right) \cdot 3\\ \mathbf{elif}\;t\_0 \leq 5 \cdot 10^{+152}:\\ \;\;\;\;\left(\frac{1}{3 \cdot x} + -3\right) \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot y\right) \cdot \sqrt{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (- (+ (/ 1.0 (* 9.0 x)) y) 1.0) (* (sqrt x) 3.0))))
   (if (<= t_0 -5e+64)
     (* (* (- y 1.0) (sqrt x)) 3.0)
     (if (<= t_0 5e+152)
       (* (+ (/ 1.0 (* 3.0 x)) -3.0) (sqrt x))
       (* (* 3.0 y) (sqrt x))))))

C

double code(double x, double y) {
	double t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (sqrt(x) * 3.0);
	double tmp;
	if (t_0 <= -5e+64) {
		tmp = ((y - 1.0) * sqrt(x)) * 3.0;
	} else if (t_0 <= 5e+152) {
		tmp = ((1.0 / (3.0 * x)) + -3.0) * sqrt(x);
	} else {
		tmp = (3.0 * y) * sqrt(x);
	}
	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 / (9.0d0 * x)) + y) - 1.0d0) * (sqrt(x) * 3.0d0)
    if (t_0 <= (-5d+64)) then
        tmp = ((y - 1.0d0) * sqrt(x)) * 3.0d0
    else if (t_0 <= 5d+152) then
        tmp = ((1.0d0 / (3.0d0 * x)) + (-3.0d0)) * sqrt(x)
    else
        tmp = (3.0d0 * y) * sqrt(x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (Math.sqrt(x) * 3.0);
	double tmp;
	if (t_0 <= -5e+64) {
		tmp = ((y - 1.0) * Math.sqrt(x)) * 3.0;
	} else if (t_0 <= 5e+152) {
		tmp = ((1.0 / (3.0 * x)) + -3.0) * Math.sqrt(x);
	} else {
		tmp = (3.0 * y) * Math.sqrt(x);
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (math.sqrt(x) * 3.0)
	tmp = 0
	if t_0 <= -5e+64:
		tmp = ((y - 1.0) * math.sqrt(x)) * 3.0
	elif t_0 <= 5e+152:
		tmp = ((1.0 / (3.0 * x)) + -3.0) * math.sqrt(x)
	else:
		tmp = (3.0 * y) * math.sqrt(x)
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(Float64(Float64(1.0 / Float64(9.0 * x)) + y) - 1.0) * Float64(sqrt(x) * 3.0))
	tmp = 0.0
	if (t_0 <= -5e+64)
		tmp = Float64(Float64(Float64(y - 1.0) * sqrt(x)) * 3.0);
	elseif (t_0 <= 5e+152)
		tmp = Float64(Float64(Float64(1.0 / Float64(3.0 * x)) + -3.0) * sqrt(x));
	else
		tmp = Float64(Float64(3.0 * y) * sqrt(x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (sqrt(x) * 3.0);
	tmp = 0.0;
	if (t_0 <= -5e+64)
		tmp = ((y - 1.0) * sqrt(x)) * 3.0;
	elseif (t_0 <= 5e+152)
		tmp = ((1.0 / (3.0 * x)) + -3.0) * sqrt(x);
	else
		tmp = (3.0 * y) * sqrt(x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[(N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - 1.0), $MachinePrecision] * N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -5e+64], N[(N[(N[(y - 1.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * 3.0), $MachinePrecision], If[LessEqual[t$95$0, 5e+152], N[(N[(N[(1.0 / N[(3.0 * x), $MachinePrecision]), $MachinePrecision] + -3.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision], N[(N[(3.0 * y), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 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))) < -5e64

   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. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 99.6

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

      8. lift-/.f64 N/A

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

      9. lift-*.f64 N/A

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

      10. *-commutative N/A

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

      11. associate-/r* N/A

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

      12. metadata-eval N/A

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

      13. metadata-eval N/A

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

      14. lower-/.f64 N/A

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

      15. metadata-eval 99.6

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

   4. Applied rewrites 99.6%

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

   5. Taylor expanded in x around inf

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

      1. lower--.f64 99.6

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

   7. Applied rewrites 99.6%

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

   if -5e64 < (*.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))) < 5e152

   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. 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 84.0

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

   5. Applied rewrites 84.0%

      \[\leadsto \color{blue}{\left(\frac{0.3333333333333333}{x} + -3\right) \cdot \sqrt{x}} \]
   6. Step-by-step derivation

   7. Applied rewrites 84.1%

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

   if 5e152 < (*.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 99.5%

      \[\left(3 \cdot \sqrt{x}\right) \cdot \left(\left(y + \frac{1}{x \cdot 9}\right) - 1\right) \]
   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 99.6

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

   5. Applied rewrites 99.6%

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

   7. Applied rewrites 99.6%

      \[\leadsto \left(y \cdot 3\right) \cdot \color{blue}{\sqrt{x}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 91.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right) \leq -5 \cdot 10^{+64}:\\ \;\;\;\;\left(\left(y - 1\right) \cdot \sqrt{x}\right) \cdot 3\\ \mathbf{elif}\;\left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right) \leq 5 \cdot 10^{+152}:\\ \;\;\;\;\left(\frac{1}{3 \cdot x} + -3\right) \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot y\right) \cdot \sqrt{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 91.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right)\\ \mathbf{if}\;t\_0 \leq -4 \cdot 10^{+43}:\\ \;\;\;\;\left(\left(y - 1\right) \cdot \sqrt{x}\right) \cdot 3\\ \mathbf{elif}\;t\_0 \leq 5 \cdot 10^{+152}:\\ \;\;\;\;\frac{\mathsf{fma}\left(-3, x, 0.3333333333333333\right)}{x} \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot y\right) \cdot \sqrt{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (- (+ (/ 1.0 (* 9.0 x)) y) 1.0) (* (sqrt x) 3.0))))
   (if (<= t_0 -4e+43)
     (* (* (- y 1.0) (sqrt x)) 3.0)
     (if (<= t_0 5e+152)
       (* (/ (fma -3.0 x 0.3333333333333333) x) (sqrt x))
       (* (* 3.0 y) (sqrt x))))))

C

double code(double x, double y) {
	double t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (sqrt(x) * 3.0);
	double tmp;
	if (t_0 <= -4e+43) {
		tmp = ((y - 1.0) * sqrt(x)) * 3.0;
	} else if (t_0 <= 5e+152) {
		tmp = (fma(-3.0, x, 0.3333333333333333) / x) * sqrt(x);
	} else {
		tmp = (3.0 * y) * sqrt(x);
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(Float64(Float64(Float64(1.0 / Float64(9.0 * x)) + y) - 1.0) * Float64(sqrt(x) * 3.0))
	tmp = 0.0
	if (t_0 <= -4e+43)
		tmp = Float64(Float64(Float64(y - 1.0) * sqrt(x)) * 3.0);
	elseif (t_0 <= 5e+152)
		tmp = Float64(Float64(fma(-3.0, x, 0.3333333333333333) / x) * sqrt(x));
	else
		tmp = Float64(Float64(3.0 * y) * sqrt(x));
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[(N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - 1.0), $MachinePrecision] * N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -4e+43], N[(N[(N[(y - 1.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * 3.0), $MachinePrecision], If[LessEqual[t$95$0, 5e+152], N[(N[(N[(-3.0 * x + 0.3333333333333333), $MachinePrecision] / x), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision], N[(N[(3.0 * y), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 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))) < -4.00000000000000006e43

   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. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 99.5

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

      8. lift-/.f64 N/A

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

      9. lift-*.f64 N/A

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

      10. *-commutative N/A

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

      11. associate-/r* N/A

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

      12. metadata-eval N/A

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

      13. metadata-eval N/A

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

      14. lower-/.f64 N/A

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

      15. metadata-eval 99.5

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

   4. Applied rewrites 99.5%

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

   5. Taylor expanded in x around inf

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

      1. lower--.f64 99.5

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

   7. Applied rewrites 99.5%

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

   if -4.00000000000000006e43 < (*.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))) < 5e152

   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. 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 82.8

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

   5. Applied rewrites 82.8%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 82.9%

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

   if 5e152 < (*.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 99.5%

      \[\left(3 \cdot \sqrt{x}\right) \cdot \left(\left(y + \frac{1}{x \cdot 9}\right) - 1\right) \]
   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 99.6

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

   5. Applied rewrites 99.6%

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

   7. Applied rewrites 99.6%

      \[\leadsto \left(y \cdot 3\right) \cdot \color{blue}{\sqrt{x}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 91.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right) \leq -4 \cdot 10^{+43}:\\ \;\;\;\;\left(\left(y - 1\right) \cdot \sqrt{x}\right) \cdot 3\\ \mathbf{elif}\;\left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right) \leq 5 \cdot 10^{+152}:\\ \;\;\;\;\frac{\mathsf{fma}\left(-3, x, 0.3333333333333333\right)}{x} \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot y\right) \cdot \sqrt{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 90.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right)\\ \mathbf{if}\;t\_0 \leq -5 \cdot 10^{+64}:\\ \;\;\;\;\left(\left(y - 1\right) \cdot \sqrt{x}\right) \cdot 3\\ \mathbf{elif}\;t\_0 \leq 5 \cdot 10^{+152}:\\ \;\;\;\;\left(\frac{0.3333333333333333}{x} + -3\right) \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot y\right) \cdot \sqrt{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (- (+ (/ 1.0 (* 9.0 x)) y) 1.0) (* (sqrt x) 3.0))))
   (if (<= t_0 -5e+64)
     (* (* (- y 1.0) (sqrt x)) 3.0)
     (if (<= t_0 5e+152)
       (* (+ (/ 0.3333333333333333 x) -3.0) (sqrt x))
       (* (* 3.0 y) (sqrt x))))))

C

double code(double x, double y) {
	double t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (sqrt(x) * 3.0);
	double tmp;
	if (t_0 <= -5e+64) {
		tmp = ((y - 1.0) * sqrt(x)) * 3.0;
	} else if (t_0 <= 5e+152) {
		tmp = ((0.3333333333333333 / x) + -3.0) * sqrt(x);
	} else {
		tmp = (3.0 * y) * sqrt(x);
	}
	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 / (9.0d0 * x)) + y) - 1.0d0) * (sqrt(x) * 3.0d0)
    if (t_0 <= (-5d+64)) then
        tmp = ((y - 1.0d0) * sqrt(x)) * 3.0d0
    else if (t_0 <= 5d+152) then
        tmp = ((0.3333333333333333d0 / x) + (-3.0d0)) * sqrt(x)
    else
        tmp = (3.0d0 * y) * sqrt(x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (Math.sqrt(x) * 3.0);
	double tmp;
	if (t_0 <= -5e+64) {
		tmp = ((y - 1.0) * Math.sqrt(x)) * 3.0;
	} else if (t_0 <= 5e+152) {
		tmp = ((0.3333333333333333 / x) + -3.0) * Math.sqrt(x);
	} else {
		tmp = (3.0 * y) * Math.sqrt(x);
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (math.sqrt(x) * 3.0)
	tmp = 0
	if t_0 <= -5e+64:
		tmp = ((y - 1.0) * math.sqrt(x)) * 3.0
	elif t_0 <= 5e+152:
		tmp = ((0.3333333333333333 / x) + -3.0) * math.sqrt(x)
	else:
		tmp = (3.0 * y) * math.sqrt(x)
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(Float64(Float64(1.0 / Float64(9.0 * x)) + y) - 1.0) * Float64(sqrt(x) * 3.0))
	tmp = 0.0
	if (t_0 <= -5e+64)
		tmp = Float64(Float64(Float64(y - 1.0) * sqrt(x)) * 3.0);
	elseif (t_0 <= 5e+152)
		tmp = Float64(Float64(Float64(0.3333333333333333 / x) + -3.0) * sqrt(x));
	else
		tmp = Float64(Float64(3.0 * y) * sqrt(x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (sqrt(x) * 3.0);
	tmp = 0.0;
	if (t_0 <= -5e+64)
		tmp = ((y - 1.0) * sqrt(x)) * 3.0;
	elseif (t_0 <= 5e+152)
		tmp = ((0.3333333333333333 / x) + -3.0) * sqrt(x);
	else
		tmp = (3.0 * y) * sqrt(x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[(N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - 1.0), $MachinePrecision] * N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -5e+64], N[(N[(N[(y - 1.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * 3.0), $MachinePrecision], If[LessEqual[t$95$0, 5e+152], N[(N[(N[(0.3333333333333333 / x), $MachinePrecision] + -3.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision], N[(N[(3.0 * y), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 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))) < -5e64

   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. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 99.6

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

      8. lift-/.f64 N/A

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

      9. lift-*.f64 N/A

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

      10. *-commutative N/A

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

      11. associate-/r* N/A

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

      12. metadata-eval N/A

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

      13. metadata-eval N/A

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

      14. lower-/.f64 N/A

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

      15. metadata-eval 99.6

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

   4. Applied rewrites 99.6%

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

   5. Taylor expanded in x around inf

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

      1. lower--.f64 99.6

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

   7. Applied rewrites 99.6%

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

   if -5e64 < (*.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))) < 5e152

   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. 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 84.0

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

   5. Applied rewrites 84.0%

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

   if 5e152 < (*.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 99.5%

      \[\left(3 \cdot \sqrt{x}\right) \cdot \left(\left(y + \frac{1}{x \cdot 9}\right) - 1\right) \]
   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 99.6

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

   5. Applied rewrites 99.6%

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

   7. Applied rewrites 99.6%

      \[\leadsto \left(y \cdot 3\right) \cdot \color{blue}{\sqrt{x}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 91.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right) \leq -5 \cdot 10^{+64}:\\ \;\;\;\;\left(\left(y - 1\right) \cdot \sqrt{x}\right) \cdot 3\\ \mathbf{elif}\;\left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right) \leq 5 \cdot 10^{+152}:\\ \;\;\;\;\left(\frac{0.3333333333333333}{x} + -3\right) \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot y\right) \cdot \sqrt{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 90.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \sqrt{x} \cdot 3\\ t_1 := \left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot t\_0\\ \mathbf{if}\;t\_1 \leq -20:\\ \;\;\;\;\left(\left(y - 1\right) \cdot \sqrt{x}\right) \cdot 3\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+152}:\\ \;\;\;\;\frac{1}{t\_0}\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot y\right) \cdot \sqrt{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (sqrt x) 3.0)) (t_1 (* (- (+ (/ 1.0 (* 9.0 x)) y) 1.0) t_0)))
   (if (<= t_1 -20.0)
     (* (* (- y 1.0) (sqrt x)) 3.0)
     (if (<= t_1 5e+152) (/ 1.0 t_0) (* (* 3.0 y) (sqrt x))))))

C

double code(double x, double y) {
	double t_0 = sqrt(x) * 3.0;
	double t_1 = (((1.0 / (9.0 * x)) + y) - 1.0) * t_0;
	double tmp;
	if (t_1 <= -20.0) {
		tmp = ((y - 1.0) * sqrt(x)) * 3.0;
	} else if (t_1 <= 5e+152) {
		tmp = 1.0 / t_0;
	} else {
		tmp = (3.0 * y) * sqrt(x);
	}
	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) :: t_1
    real(8) :: tmp
    t_0 = sqrt(x) * 3.0d0
    t_1 = (((1.0d0 / (9.0d0 * x)) + y) - 1.0d0) * t_0
    if (t_1 <= (-20.0d0)) then
        tmp = ((y - 1.0d0) * sqrt(x)) * 3.0d0
    else if (t_1 <= 5d+152) then
        tmp = 1.0d0 / t_0
    else
        tmp = (3.0d0 * y) * sqrt(x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = Math.sqrt(x) * 3.0;
	double t_1 = (((1.0 / (9.0 * x)) + y) - 1.0) * t_0;
	double tmp;
	if (t_1 <= -20.0) {
		tmp = ((y - 1.0) * Math.sqrt(x)) * 3.0;
	} else if (t_1 <= 5e+152) {
		tmp = 1.0 / t_0;
	} else {
		tmp = (3.0 * y) * Math.sqrt(x);
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = math.sqrt(x) * 3.0
	t_1 = (((1.0 / (9.0 * x)) + y) - 1.0) * t_0
	tmp = 0
	if t_1 <= -20.0:
		tmp = ((y - 1.0) * math.sqrt(x)) * 3.0
	elif t_1 <= 5e+152:
		tmp = 1.0 / t_0
	else:
		tmp = (3.0 * y) * math.sqrt(x)
	return tmp

Julia

function code(x, y)
	t_0 = Float64(sqrt(x) * 3.0)
	t_1 = Float64(Float64(Float64(Float64(1.0 / Float64(9.0 * x)) + y) - 1.0) * t_0)
	tmp = 0.0
	if (t_1 <= -20.0)
		tmp = Float64(Float64(Float64(y - 1.0) * sqrt(x)) * 3.0);
	elseif (t_1 <= 5e+152)
		tmp = Float64(1.0 / t_0);
	else
		tmp = Float64(Float64(3.0 * y) * sqrt(x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = sqrt(x) * 3.0;
	t_1 = (((1.0 / (9.0 * x)) + y) - 1.0) * t_0;
	tmp = 0.0;
	if (t_1 <= -20.0)
		tmp = ((y - 1.0) * sqrt(x)) * 3.0;
	elseif (t_1 <= 5e+152)
		tmp = 1.0 / t_0;
	else
		tmp = (3.0 * y) * sqrt(x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - 1.0), $MachinePrecision] * t$95$0), $MachinePrecision]}, If[LessEqual[t$95$1, -20.0], N[(N[(N[(y - 1.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * 3.0), $MachinePrecision], If[LessEqual[t$95$1, 5e+152], N[(1.0 / t$95$0), $MachinePrecision], N[(N[(3.0 * y), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 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))) < -20

   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. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 99.5

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

      8. lift-/.f64 N/A

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

      9. lift-*.f64 N/A

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

      10. *-commutative N/A

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

      11. associate-/r* N/A

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

      12. metadata-eval N/A

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

      13. metadata-eval N/A

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

      14. lower-/.f64 N/A

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

      15. metadata-eval 99.5

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

   4. Applied rewrites 99.5%

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

   5. Taylor expanded in x around inf

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

      1. lower--.f64 98.9

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

   7. Applied rewrites 98.9%

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

   if -20 < (*.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))) < 5e152

   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

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

      2. lift--.f64 N/A

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

      3. flip-- N/A

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

      4. associate-*r/ N/A

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

      5. clear-num N/A

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

      6. lower-/.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 50.0%

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

   5. Taylor expanded in x around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower-sqrt.f64 81.0

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

   7. Applied rewrites 81.0%

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

   if 5e152 < (*.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 99.5%

      \[\left(3 \cdot \sqrt{x}\right) \cdot \left(\left(y + \frac{1}{x \cdot 9}\right) - 1\right) \]
   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 99.6

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

   5. Applied rewrites 99.6%

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

   7. Applied rewrites 99.6%

      \[\leadsto \left(y \cdot 3\right) \cdot \color{blue}{\sqrt{x}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 91.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right) \leq -20:\\ \;\;\;\;\left(\left(y - 1\right) \cdot \sqrt{x}\right) \cdot 3\\ \mathbf{elif}\;\left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right) \leq 5 \cdot 10^{+152}:\\ \;\;\;\;\frac{1}{\sqrt{x} \cdot 3}\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot y\right) \cdot \sqrt{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 90.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right)\\ \mathbf{if}\;t\_0 \leq -20:\\ \;\;\;\;\left(\left(y - 1\right) \cdot \sqrt{x}\right) \cdot 3\\ \mathbf{elif}\;t\_0 \leq 5 \cdot 10^{+152}:\\ \;\;\;\;\frac{0.3333333333333333}{x} \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot y\right) \cdot \sqrt{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (- (+ (/ 1.0 (* 9.0 x)) y) 1.0) (* (sqrt x) 3.0))))
   (if (<= t_0 -20.0)
     (* (* (- y 1.0) (sqrt x)) 3.0)
     (if (<= t_0 5e+152)
       (* (/ 0.3333333333333333 x) (sqrt x))
       (* (* 3.0 y) (sqrt x))))))

C

double code(double x, double y) {
	double t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (sqrt(x) * 3.0);
	double tmp;
	if (t_0 <= -20.0) {
		tmp = ((y - 1.0) * sqrt(x)) * 3.0;
	} else if (t_0 <= 5e+152) {
		tmp = (0.3333333333333333 / x) * sqrt(x);
	} else {
		tmp = (3.0 * y) * sqrt(x);
	}
	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 / (9.0d0 * x)) + y) - 1.0d0) * (sqrt(x) * 3.0d0)
    if (t_0 <= (-20.0d0)) then
        tmp = ((y - 1.0d0) * sqrt(x)) * 3.0d0
    else if (t_0 <= 5d+152) then
        tmp = (0.3333333333333333d0 / x) * sqrt(x)
    else
        tmp = (3.0d0 * y) * sqrt(x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (Math.sqrt(x) * 3.0);
	double tmp;
	if (t_0 <= -20.0) {
		tmp = ((y - 1.0) * Math.sqrt(x)) * 3.0;
	} else if (t_0 <= 5e+152) {
		tmp = (0.3333333333333333 / x) * Math.sqrt(x);
	} else {
		tmp = (3.0 * y) * Math.sqrt(x);
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (math.sqrt(x) * 3.0)
	tmp = 0
	if t_0 <= -20.0:
		tmp = ((y - 1.0) * math.sqrt(x)) * 3.0
	elif t_0 <= 5e+152:
		tmp = (0.3333333333333333 / x) * math.sqrt(x)
	else:
		tmp = (3.0 * y) * math.sqrt(x)
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(Float64(Float64(1.0 / Float64(9.0 * x)) + y) - 1.0) * Float64(sqrt(x) * 3.0))
	tmp = 0.0
	if (t_0 <= -20.0)
		tmp = Float64(Float64(Float64(y - 1.0) * sqrt(x)) * 3.0);
	elseif (t_0 <= 5e+152)
		tmp = Float64(Float64(0.3333333333333333 / x) * sqrt(x));
	else
		tmp = Float64(Float64(3.0 * y) * sqrt(x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (((1.0 / (9.0 * x)) + y) - 1.0) * (sqrt(x) * 3.0);
	tmp = 0.0;
	if (t_0 <= -20.0)
		tmp = ((y - 1.0) * sqrt(x)) * 3.0;
	elseif (t_0 <= 5e+152)
		tmp = (0.3333333333333333 / x) * sqrt(x);
	else
		tmp = (3.0 * y) * sqrt(x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[(N[(1.0 / N[(9.0 * x), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - 1.0), $MachinePrecision] * N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -20.0], N[(N[(N[(y - 1.0), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] * 3.0), $MachinePrecision], If[LessEqual[t$95$0, 5e+152], N[(N[(0.3333333333333333 / x), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision], N[(N[(3.0 * y), $MachinePrecision] * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 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))) < -20

   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. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 99.5

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

      8. lift-/.f64 N/A

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

      9. lift-*.f64 N/A

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

      10. *-commutative N/A

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

      11. associate-/r* N/A

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

      12. metadata-eval N/A

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

      13. metadata-eval N/A

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

      14. lower-/.f64 N/A

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

      15. metadata-eval 99.5

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

   4. Applied rewrites 99.5%

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

   5. Taylor expanded in x around inf

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

      1. lower--.f64 98.9

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

   7. Applied rewrites 98.9%

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

   if -20 < (*.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))) < 5e152

   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. 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 81.7

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

   5. Applied rewrites 81.7%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 80.9%

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

   if 5e152 < (*.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 99.5%

      \[\left(3 \cdot \sqrt{x}\right) \cdot \left(\left(y + \frac{1}{x \cdot 9}\right) - 1\right) \]
   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 99.6

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

   5. Applied rewrites 99.6%

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

   7. Applied rewrites 99.6%

      \[\leadsto \left(y \cdot 3\right) \cdot \color{blue}{\sqrt{x}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 91.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right) \leq -20:\\ \;\;\;\;\left(\left(y - 1\right) \cdot \sqrt{x}\right) \cdot 3\\ \mathbf{elif}\;\left(\left(\frac{1}{9 \cdot x} + y\right) - 1\right) \cdot \left(\sqrt{x} \cdot 3\right) \leq 5 \cdot 10^{+152}:\\ \;\;\;\;\frac{0.3333333333333333}{x} \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot y\right) \cdot \sqrt{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 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 99.4%

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

   1. lift-*.f64 N/A

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

   2. lift--.f64 N/A

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

   3. lift-+.f64 N/A

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

   4. associate--l+ N/A

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

   5. distribute-rgt-in N/A

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

   6. lift-*.f64 N/A

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

   7. *-commutative N/A

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

   8. associate-*r* N/A

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

   9. lower-fma.f64 N/A

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

   10. lower-*.f64 N/A

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

   11. lift-*.f64 N/A

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

   12. associate-*r* N/A

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

   13. lower-*.f64 N/A

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

4. Applied rewrites 99.4%

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

5. Taylor expanded in x around 0

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

   1. lower-/.f64 N/A

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

   2. +-commutative N/A

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

   3. lower-fma.f64 99.4

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

7. Applied rewrites 99.4%

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

8. Taylor expanded in y around 0

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

10. Applied rewrites 99.4%

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

11. Final simplification 99.4%

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

Alternative 8: 60.6% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\sqrt{x} \cdot 3\right) \cdot y\\ \mathbf{if}\;y \leq -5.6 \cdot 10^{-19}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1.08 \cdot 10^{-16}:\\ \;\;\;\;-3 \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (* (sqrt x) 3.0) y)))
   (if (<= y -5.6e-19) t_0 (if (<= y 1.08e-16) (* -3.0 (sqrt x)) t_0))))

C

double code(double x, double y) {
	double t_0 = (sqrt(x) * 3.0) * y;
	double tmp;
	if (y <= -5.6e-19) {
		tmp = t_0;
	} else if (y <= 1.08e-16) {
		tmp = -3.0 * sqrt(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 = (sqrt(x) * 3.0d0) * y
    if (y <= (-5.6d-19)) then
        tmp = t_0
    else if (y <= 1.08d-16) then
        tmp = (-3.0d0) * sqrt(x)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (Math.sqrt(x) * 3.0) * y;
	double tmp;
	if (y <= -5.6e-19) {
		tmp = t_0;
	} else if (y <= 1.08e-16) {
		tmp = -3.0 * Math.sqrt(x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (math.sqrt(x) * 3.0) * y
	tmp = 0
	if y <= -5.6e-19:
		tmp = t_0
	elif y <= 1.08e-16:
		tmp = -3.0 * math.sqrt(x)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(sqrt(x) * 3.0) * y)
	tmp = 0.0
	if (y <= -5.6e-19)
		tmp = t_0;
	elseif (y <= 1.08e-16)
		tmp = Float64(-3.0 * sqrt(x));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (sqrt(x) * 3.0) * y;
	tmp = 0.0;
	if (y <= -5.6e-19)
		tmp = t_0;
	elseif (y <= 1.08e-16)
		tmp = -3.0 * sqrt(x);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[Sqrt[x], $MachinePrecision] * 3.0), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[y, -5.6e-19], t$95$0, If[LessEqual[y, 1.08e-16], N[(-3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -5.60000000000000005e-19 or 1.08e-16 < y

   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 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 71.2

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

   5. Applied rewrites 71.2%

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

   7. Applied rewrites 71.2%

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

   if -5.60000000000000005e-19 < y < 1.08e-16

   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 99.4

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

   5. Applied rewrites 99.4%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 50.8%

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

4. Final simplification 61.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5.6 \cdot 10^{-19}:\\ \;\;\;\;\left(\sqrt{x} \cdot 3\right) \cdot y\\ \mathbf{elif}\;y \leq 1.08 \cdot 10^{-16}:\\ \;\;\;\;-3 \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;\left(\sqrt{x} \cdot 3\right) \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 62.7% accurate, 1.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-*l* N/A

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

   4. *-commutative N/A

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

   5. lower-*.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 99.3

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

   8. lift-/.f64 N/A

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

   9. lift-*.f64 N/A

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

   10. *-commutative N/A

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

   11. associate-/r* N/A

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

   12. metadata-eval N/A

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

   13. metadata-eval N/A

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

   14. lower-/.f64 N/A

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

   15. metadata-eval 99.3

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

4. Applied rewrites 99.3%

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

5. Taylor expanded in x around inf

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

   1. lower--.f64 63.0

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

7. Applied rewrites 63.0%

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

Alternative 10: 62.7% 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 99.4%

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

3. Taylor expanded in x around inf

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

   1. associate-*r* N/A

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

   2. *-commutative N/A

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

   3. associate-*r* 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 62.9

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

5. Applied rewrites 62.9%

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

Alternative 11: 25.5% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ -3 \cdot \sqrt{x} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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 61.1

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

5. Applied rewrites 61.1%

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

6. Taylor expanded in x around inf

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

8. Applied rewrites 25.2%

   \[\leadsto -3 \cdot \sqrt{\color{blue}{x}} \]
9. 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 2024235 
(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)))