2sqrt (example 3.1)

Percentage Accurate: 6.7% → 99.1%

Time: 9.0s

Alternatives: 5

Speedup: 1.2×

Specification

\[x > 1 \land x < 10^{+308}\]

Math

\[\begin{array}{l} \\ \sqrt{x + 1} - \sqrt{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- (sqrt (+ x 1.0)) (sqrt x)))

C

double code(double x) {
	return sqrt((x + 1.0)) - sqrt(x);
}

Fortran

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

Java

public static double code(double x) {
	return Math.sqrt((x + 1.0)) - Math.sqrt(x);
}

Python

def code(x):
	return math.sqrt((x + 1.0)) - math.sqrt(x)

Julia

function code(x)
	return Float64(sqrt(Float64(x + 1.0)) - sqrt(x))
end

MATLAB

function tmp = code(x)
	tmp = sqrt((x + 1.0)) - sqrt(x);
end

Wolfram

code[x_] := N[(N[Sqrt[N[(x + 1.0), $MachinePrecision]], $MachinePrecision] - N[Sqrt[x], $MachinePrecision]), $MachinePrecision]

Initial Program: 6.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{x + 1} - \sqrt{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- (sqrt (+ x 1.0)) (sqrt x)))

C

double code(double x) {
	return sqrt((x + 1.0)) - sqrt(x);
}

Fortran

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

Java

public static double code(double x) {
	return Math.sqrt((x + 1.0)) - Math.sqrt(x);
}

Python

def code(x):
	return math.sqrt((x + 1.0)) - math.sqrt(x)

Julia

function code(x)
	return Float64(sqrt(Float64(x + 1.0)) - sqrt(x))
end

MATLAB

function tmp = code(x)
	tmp = sqrt((x + 1.0)) - sqrt(x);
end

Wolfram

code[x_] := N[(N[Sqrt[N[(x + 1.0), $MachinePrecision]], $MachinePrecision] - N[Sqrt[x], $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \frac{\sqrt{x} + \frac{0.25}{\sqrt{x}}}{x + \left(x + 1\right)} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/ (+ (sqrt x) (/ 0.25 (sqrt x))) (+ x (+ x 1.0))))

C

double code(double x) {
	return (sqrt(x) + (0.25 / sqrt(x))) / (x + (x + 1.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (sqrt(x) + (0.25d0 / sqrt(x))) / (x + (x + 1.0d0))
end function

Java

public static double code(double x) {
	return (Math.sqrt(x) + (0.25 / Math.sqrt(x))) / (x + (x + 1.0));
}

Python

def code(x):
	return (math.sqrt(x) + (0.25 / math.sqrt(x))) / (x + (x + 1.0))

Julia

function code(x)
	return Float64(Float64(sqrt(x) + Float64(0.25 / sqrt(x))) / Float64(x + Float64(x + 1.0)))
end

MATLAB

function tmp = code(x)
	tmp = (sqrt(x) + (0.25 / sqrt(x))) / (x + (x + 1.0));
end

Wolfram

code[x_] := N[(N[(N[Sqrt[x], $MachinePrecision] + N[(0.25 / N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + N[(x + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 5.6%

   \[\sqrt{x + 1} - \sqrt{x} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto \color{blue}{\sqrt{x + 1} - \sqrt{x}} \]

   2. flip-- N/A

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

   3. div-inv N/A

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

   4. lift-sqrt.f64 N/A

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

   5. lift-sqrt.f64 N/A

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

   6. rem-square-sqrt N/A

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

   7. lift-sqrt.f64 N/A

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

   8. lift-sqrt.f64 N/A

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

   9. rem-square-sqrt N/A

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

   10. flip-- N/A

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

   11. associate-*l/ N/A

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

   12. lower-/.f64 N/A

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

4. Applied rewrites 7.2%

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

5. Taylor expanded in x around inf

   \[\leadsto \frac{\color{blue}{\sqrt{x} + \left(\frac{-1}{4} \cdot \sqrt{\frac{1}{x}} + \frac{1}{2} \cdot \sqrt{\frac{1}{x}}\right)}}{x + \left(x + 1\right)} \]
6. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{\color{blue}{\left(\frac{-1}{4} \cdot \sqrt{\frac{1}{x}} + \frac{1}{2} \cdot \sqrt{\frac{1}{x}}\right) + \sqrt{x}}}{x + \left(x + 1\right)} \]

   2. distribute-rgt-out N/A

      \[\leadsto \frac{\color{blue}{\sqrt{\frac{1}{x}} \cdot \left(\frac{-1}{4} + \frac{1}{2}\right)} + \sqrt{x}}{x + \left(x + 1\right)} \]

   3. metadata-eval N/A

      \[\leadsto \frac{\sqrt{\frac{1}{x}} \cdot \color{blue}{\frac{1}{4}} + \sqrt{x}}{x + \left(x + 1\right)} \]

   4. lower-fma.f64 N/A

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\sqrt{\frac{1}{x}}, \frac{1}{4}, \sqrt{x}\right)}}{x + \left(x + 1\right)} \]

   5. lower-sqrt.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\sqrt{\frac{1}{x}}}, \frac{1}{4}, \sqrt{x}\right)}{x + \left(x + 1\right)} \]

   6. lower-/.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt{\color{blue}{\frac{1}{x}}}, \frac{1}{4}, \sqrt{x}\right)}{x + \left(x + 1\right)} \]

   7. lower-sqrt.f64 99.5

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt{\frac{1}{x}}, 0.25, \color{blue}{\sqrt{x}}\right)}{x + \left(x + 1\right)} \]

7. Applied rewrites 99.5%

   \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\sqrt{\frac{1}{x}}, 0.25, \sqrt{x}\right)}}{x + \left(x + 1\right)} \]
8. Step-by-step derivation

9. Applied rewrites 99.5%

   \[\leadsto \frac{\frac{0.25}{\sqrt{x}} + \color{blue}{\sqrt{x}}}{x + \left(x + 1\right)} \]

10. Final simplification 99.5%

    \[\leadsto \frac{\sqrt{x} + \frac{0.25}{\sqrt{x}}}{x + \left(x + 1\right)} \]
11. Add Preprocessing

Alternative 2: 97.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot \sqrt{\frac{1}{x}} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 0.5 (sqrt (/ 1.0 x))))

C

double code(double x) {
	return 0.5 * sqrt((1.0 / x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.5d0 * sqrt((1.0d0 / x))
end function

Java

public static double code(double x) {
	return 0.5 * Math.sqrt((1.0 / x));
}

Python

def code(x):
	return 0.5 * math.sqrt((1.0 / x))

Julia

function code(x)
	return Float64(0.5 * sqrt(Float64(1.0 / x)))
end

MATLAB

function tmp = code(x)
	tmp = 0.5 * sqrt((1.0 / x));
end

Wolfram

code[x_] := N[(0.5 * N[Sqrt[N[(1.0 / x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 5.6%

   \[\sqrt{x + 1} - \sqrt{x} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{1}{2} \cdot \sqrt{\frac{1}{x}}} \]
4. Step-by-step derivation

   1. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \sqrt{\frac{1}{x}}} \]

   2. lower-sqrt.f64 N/A

      \[\leadsto \frac{1}{2} \cdot \color{blue}{\sqrt{\frac{1}{x}}} \]

   3. lower-/.f64 98.7

      \[\leadsto 0.5 \cdot \sqrt{\color{blue}{\frac{1}{x}}} \]

5. Applied rewrites 98.7%

   \[\leadsto \color{blue}{0.5 \cdot \sqrt{\frac{1}{x}}} \]
6. Add Preprocessing

Alternative 3: 97.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \frac{0.5}{\sqrt{x}} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 0.5 (sqrt x)))

C

double code(double x) {
	return 0.5 / sqrt(x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.5d0 / sqrt(x)
end function

Java

public static double code(double x) {
	return 0.5 / Math.sqrt(x);
}

Python

def code(x):
	return 0.5 / math.sqrt(x)

Julia

function code(x)
	return Float64(0.5 / sqrt(x))
end

MATLAB

function tmp = code(x)
	tmp = 0.5 / sqrt(x);
end

Wolfram

code[x_] := N[(0.5 / N[Sqrt[x], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 5.6%

   \[\sqrt{x + 1} - \sqrt{x} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto \color{blue}{\sqrt{x + 1} - \sqrt{x}} \]

   2. flip-- N/A

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

   3. div-inv N/A

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

   4. lift-sqrt.f64 N/A

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

   5. lift-sqrt.f64 N/A

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

   6. rem-square-sqrt N/A

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

   7. lift-sqrt.f64 N/A

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

   8. lift-sqrt.f64 N/A

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

   9. rem-square-sqrt N/A

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

   10. flip-- N/A

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

   11. associate-*l/ N/A

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

   12. lower-/.f64 N/A

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

4. Applied rewrites 7.2%

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

5. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{1}{2} \cdot \sqrt{\frac{1}{x}}} \]
6. Step-by-step derivation

   1. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \sqrt{\frac{1}{x}}} \]

   2. lower-sqrt.f64 N/A

      \[\leadsto \frac{1}{2} \cdot \color{blue}{\sqrt{\frac{1}{x}}} \]

   3. lower-/.f64 98.7

      \[\leadsto 0.5 \cdot \sqrt{\color{blue}{\frac{1}{x}}} \]

7. Applied rewrites 98.7%

   \[\leadsto \color{blue}{0.5 \cdot \sqrt{\frac{1}{x}}} \]
8. Step-by-step derivation

9. Applied rewrites 98.5%

   \[\leadsto \color{blue}{\frac{0.5}{\sqrt{x}}} \]
10. Add Preprocessing

Alternative 4: 4.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ x \cdot 0.5 - \sqrt{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- (* x 0.5) (sqrt x)))

C

double code(double x) {
	return (x * 0.5) - sqrt(x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * 0.5d0) - sqrt(x)
end function

Java

public static double code(double x) {
	return (x * 0.5) - Math.sqrt(x);
}

Python

def code(x):
	return (x * 0.5) - math.sqrt(x)

Julia

function code(x)
	return Float64(Float64(x * 0.5) - sqrt(x))
end

MATLAB

function tmp = code(x)
	tmp = (x * 0.5) - sqrt(x);
end

Wolfram

code[x_] := N[(N[(x * 0.5), $MachinePrecision] - N[Sqrt[x], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 5.6%

   \[\sqrt{x + 1} - \sqrt{x} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. unpow1 N/A

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

   2. metadata-eval N/A

      \[\leadsto {\left(\sqrt{x + 1}\right)}^{\color{blue}{\left(2 - 1\right)}} - \sqrt{x} \]

   3. pow-div N/A

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

   4. pow2 N/A

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

   5. lift-sqrt.f64 N/A

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

   6. lift-sqrt.f64 N/A

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

   7. rem-square-sqrt N/A

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

   8. unpow1 N/A

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

   9. lower-/.f64 5.5

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

4. Applied rewrites 5.5%

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

5. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\left(1 + \frac{1}{2} \cdot x\right)} - \sqrt{x} \]
6. Step-by-step derivation

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 4.4

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, 0.5, 1\right)} - \sqrt{x} \]

7. Applied rewrites 4.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, 0.5, 1\right)} - \sqrt{x} \]

8. Taylor expanded in x around inf

   \[\leadsto \frac{1}{2} \cdot \color{blue}{x} - \sqrt{x} \]
9. Step-by-step derivation

10. Applied rewrites 4.4%

    \[\leadsto x \cdot \color{blue}{0.5} - \sqrt{x} \]
11. Add Preprocessing

Alternative 5: 1.6% accurate, 1.9× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 (- 1.0 (sqrt x)))

C

double code(double x) {
	return 1.0 - sqrt(x);
}

Fortran

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

Java

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

Python

def code(x):
	return 1.0 - math.sqrt(x)

Julia

function code(x)
	return Float64(1.0 - sqrt(x))
end

MATLAB

function tmp = code(x)
	tmp = 1.0 - sqrt(x);
end

Wolfram

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

Derivation

1. Initial program 5.6%

   \[\sqrt{x + 1} - \sqrt{x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 1.6%

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

Developer Target 1: 99.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\sqrt{x + 1} + \sqrt{x}} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 1.0 (+ (sqrt (+ x 1.0)) (sqrt x))))

C

double code(double x) {
	return 1.0 / (sqrt((x + 1.0)) + sqrt(x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 1.0d0 / (sqrt((x + 1.0d0)) + sqrt(x))
end function

Java

public static double code(double x) {
	return 1.0 / (Math.sqrt((x + 1.0)) + Math.sqrt(x));
}

Python

def code(x):
	return 1.0 / (math.sqrt((x + 1.0)) + math.sqrt(x))

Julia

function code(x)
	return Float64(1.0 / Float64(sqrt(Float64(x + 1.0)) + sqrt(x)))
end

MATLAB

function tmp = code(x)
	tmp = 1.0 / (sqrt((x + 1.0)) + sqrt(x));
end

Wolfram

code[x_] := N[(1.0 / N[(N[Sqrt[N[(x + 1.0), $MachinePrecision]], $MachinePrecision] + N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Developer Target 2: 98.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot {x}^{-0.5} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 0.5 (pow x -0.5)))

C

double code(double x) {
	return 0.5 * pow(x, -0.5);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.5d0 * (x ** (-0.5d0))
end function

Java

public static double code(double x) {
	return 0.5 * Math.pow(x, -0.5);
}

Python

def code(x):
	return 0.5 * math.pow(x, -0.5)

Julia

function code(x)
	return Float64(0.5 * (x ^ -0.5))
end

MATLAB

function tmp = code(x)
	tmp = 0.5 * (x ^ -0.5);
end

Wolfram

code[x_] := N[(0.5 * N[Power[x, -0.5], $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024223 
(FPCore (x)
  :name "2sqrt (example 3.1)"
  :precision binary64
  :pre (and (> x 1.0) (< x 1e+308))

  :alt
  (! :herbie-platform default (/ 1 (+ (sqrt (+ x 1)) (sqrt x))))

  :alt
  (! :herbie-platform default (* 1/2 (pow x -1/2)))

  (- (sqrt (+ x 1.0)) (sqrt x)))