Numeric.Log:$clog1p from log-domain-0.10.2.1, B

Percentage Accurate: 99.7% → 99.7%

Time: 8.2s

Alternatives: 9

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.7%

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

Alternative 2: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 0.00138:\\ \;\;\;\;\frac{x}{2 + x \cdot \left(0.5 + x \cdot \left(x \cdot 0.0625 - 0.125\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;{\left(x + 1\right)}^{0.5} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 0.00138)
   (/ x (+ 2.0 (* x (+ 0.5 (* x (- (* x 0.0625) 0.125))))))
   (+ (pow (+ x 1.0) 0.5) -1.0)))

C

double code(double x) {
	double tmp;
	if (x <= 0.00138) {
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))));
	} else {
		tmp = pow((x + 1.0), 0.5) + -1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 0.00138d0) then
        tmp = x / (2.0d0 + (x * (0.5d0 + (x * ((x * 0.0625d0) - 0.125d0)))))
    else
        tmp = ((x + 1.0d0) ** 0.5d0) + (-1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= 0.00138) {
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))));
	} else {
		tmp = Math.pow((x + 1.0), 0.5) + -1.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 0.00138:
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))))
	else:
		tmp = math.pow((x + 1.0), 0.5) + -1.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 0.00138)
		tmp = Float64(x / Float64(2.0 + Float64(x * Float64(0.5 + Float64(x * Float64(Float64(x * 0.0625) - 0.125))))));
	else
		tmp = Float64((Float64(x + 1.0) ^ 0.5) + -1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 0.00138)
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))));
	else
		tmp = ((x + 1.0) ^ 0.5) + -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 0.00138], N[(x / N[(2.0 + N[(x * N[(0.5 + N[(x * N[(N[(x * 0.0625), $MachinePrecision] - 0.125), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Power[N[(x + 1.0), $MachinePrecision], 0.5], $MachinePrecision] + -1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 0.00137999999999999993

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 99.4%

      \[\leadsto \frac{x}{\color{blue}{2 + x \cdot \left(0.5 + x \cdot \left(0.0625 \cdot x - 0.125\right)\right)}} \]

   if 0.00137999999999999993 < x

   1. Initial program 99.2%

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

      1. add-log-exp 5.1%

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

      2. *-un-lft-identity 5.1%

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

      3. log-prod 5.1%

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

      4. metadata-eval 5.1%

         \[\leadsto \color{blue}{0} + \log \left(e^{\frac{x}{1 + \sqrt{x + 1}}}\right) \]

      5. add-log-exp 99.2%

         \[\leadsto 0 + \color{blue}{\frac{x}{1 + \sqrt{x + 1}}} \]

      6. frac-2neg 99.2%

         \[\leadsto 0 + \color{blue}{\frac{-x}{-\left(1 + \sqrt{x + 1}\right)}} \]

      7. distribute-frac-neg2 99.2%

         \[\leadsto 0 + \color{blue}{\left(-\frac{-x}{1 + \sqrt{x + 1}}\right)} \]

      8. neg-sub0 99.2%

         \[\leadsto 0 + \left(-\frac{\color{blue}{0 - x}}{1 + \sqrt{x + 1}}\right) \]

      9. metadata-eval 99.2%

         \[\leadsto 0 + \left(-\frac{\color{blue}{\left(1 - 1\right)} - x}{1 + \sqrt{x + 1}}\right) \]

      10. associate--r+ 99.2%

          \[\leadsto 0 + \left(-\frac{\color{blue}{1 - \left(1 + x\right)}}{1 + \sqrt{x + 1}}\right) \]

      11. metadata-eval 99.2%

          \[\leadsto 0 + \left(-\frac{\color{blue}{1 \cdot 1} - \left(1 + x\right)}{1 + \sqrt{x + 1}}\right) \]

      12. +-commutative 99.2%

          \[\leadsto 0 + \left(-\frac{1 \cdot 1 - \color{blue}{\left(x + 1\right)}}{1 + \sqrt{x + 1}}\right) \]

      13. add-sqr-sqrt 99.8%

          \[\leadsto 0 + \left(-\frac{1 \cdot 1 - \color{blue}{\sqrt{x + 1} \cdot \sqrt{x + 1}}}{1 + \sqrt{x + 1}}\right) \]

      14. flip-- 100.0%

          \[\leadsto 0 + \left(-\color{blue}{\left(1 - \sqrt{x + 1}\right)}\right) \]

      15. pow1/2 100.0%

          \[\leadsto 0 + \left(-\left(1 - \color{blue}{{\left(x + 1\right)}^{0.5}}\right)\right) \]

   4. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{0 + \left(-\left(1 - {\left(x + 1\right)}^{0.5}\right)\right)} \]
   5. Step-by-step derivation

      1. unsub-neg 100.0%

         \[\leadsto \color{blue}{0 - \left(1 - {\left(x + 1\right)}^{0.5}\right)} \]

      2. associate--r- 100.0%

         \[\leadsto \color{blue}{\left(0 - 1\right) + {\left(x + 1\right)}^{0.5}} \]

      3. metadata-eval 100.0%

         \[\leadsto \color{blue}{-1} + {\left(x + 1\right)}^{0.5} \]

      4. +-commutative 100.0%

         \[\leadsto \color{blue}{{\left(x + 1\right)}^{0.5} + -1} \]

      5. metadata-eval 100.0%

         \[\leadsto {\left(x + 1\right)}^{0.5} + \color{blue}{\left(-1\right)} \]

      6. sub-neg 100.0%

         \[\leadsto \color{blue}{{\left(x + 1\right)}^{0.5} - 1} \]

      7. exp-to-pow 92.1%

         \[\leadsto \color{blue}{e^{\log \left(x + 1\right) \cdot 0.5}} - 1 \]

      8. +-commutative 92.1%

         \[\leadsto e^{\log \color{blue}{\left(1 + x\right)} \cdot 0.5} - 1 \]

      9. log1p-undefine 92.1%

         \[\leadsto e^{\color{blue}{\mathsf{log1p}\left(x\right)} \cdot 0.5} - 1 \]

      10. *-commutative 92.1%

          \[\leadsto e^{\color{blue}{0.5 \cdot \mathsf{log1p}\left(x\right)}} - 1 \]

      11. expm1-define 92.2%

          \[\leadsto \color{blue}{\mathsf{expm1}\left(0.5 \cdot \mathsf{log1p}\left(x\right)\right)} \]

   6. Simplified 92.2%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(0.5 \cdot \mathsf{log1p}\left(x\right)\right)} \]
   7. Step-by-step derivation

      1. expm1-undefine 92.1%

         \[\leadsto \color{blue}{e^{0.5 \cdot \mathsf{log1p}\left(x\right)} - 1} \]

      2. *-commutative 92.1%

         \[\leadsto e^{\color{blue}{\mathsf{log1p}\left(x\right) \cdot 0.5}} - 1 \]

      3. log1p-undefine 92.1%

         \[\leadsto e^{\color{blue}{\log \left(1 + x\right)} \cdot 0.5} - 1 \]

      4. exp-to-pow 100.0%

         \[\leadsto \color{blue}{{\left(1 + x\right)}^{0.5}} - 1 \]

      5. +-commutative 100.0%

         \[\leadsto {\color{blue}{\left(x + 1\right)}}^{0.5} - 1 \]

   8. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{{\left(x + 1\right)}^{0.5} - 1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 0.00138:\\ \;\;\;\;\frac{x}{2 + x \cdot \left(0.5 + x \cdot \left(x \cdot 0.0625 - 0.125\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;{\left(x + 1\right)}^{0.5} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 3:\\ \;\;\;\;\frac{x}{2 + x \cdot \left(0.5 + x \cdot \left(x \cdot 0.0625 - 0.125\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{x} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 3.0)
   (/ x (+ 2.0 (* x (+ 0.5 (* x (- (* x 0.0625) 0.125))))))
   (+ (sqrt x) -1.0)))

C

double code(double x) {
	double tmp;
	if (x <= 3.0) {
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))));
	} else {
		tmp = sqrt(x) + -1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 3.0d0) then
        tmp = x / (2.0d0 + (x * (0.5d0 + (x * ((x * 0.0625d0) - 0.125d0)))))
    else
        tmp = sqrt(x) + (-1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= 3.0) {
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))));
	} else {
		tmp = Math.sqrt(x) + -1.0;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 3.0:
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))))
	else:
		tmp = math.sqrt(x) + -1.0
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 3.0)
		tmp = Float64(x / Float64(2.0 + Float64(x * Float64(0.5 + Float64(x * Float64(Float64(x * 0.0625) - 0.125))))));
	else
		tmp = Float64(sqrt(x) + -1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 3.0)
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))));
	else
		tmp = sqrt(x) + -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 3.0], N[(x / N[(2.0 + N[(x * N[(0.5 + N[(x * N[(N[(x * 0.0625), $MachinePrecision] - 0.125), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Sqrt[x], $MachinePrecision] + -1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 3

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 99.0%

      \[\leadsto \frac{x}{\color{blue}{2 + x \cdot \left(0.5 + x \cdot \left(0.0625 \cdot x - 0.125\right)\right)}} \]

   if 3 < x

   1. Initial program 99.2%

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

   3. Taylor expanded in x around inf 99.6%

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

4. Final simplification 99.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 3:\\ \;\;\;\;\frac{x}{2 + x \cdot \left(0.5 + x \cdot \left(x \cdot 0.0625 - 0.125\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{x} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 98.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 3.7:\\ \;\;\;\;\frac{x}{2 + x \cdot \left(0.5 + x \cdot \left(x \cdot 0.0625 - 0.125\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 3.7)
   (/ x (+ 2.0 (* x (+ 0.5 (* x (- (* x 0.0625) 0.125))))))
   (sqrt x)))

C

double code(double x) {
	double tmp;
	if (x <= 3.7) {
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))));
	} else {
		tmp = sqrt(x);
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 3.7d0) then
        tmp = x / (2.0d0 + (x * (0.5d0 + (x * ((x * 0.0625d0) - 0.125d0)))))
    else
        tmp = sqrt(x)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= 3.7) {
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))));
	} else {
		tmp = Math.sqrt(x);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 3.7:
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))))
	else:
		tmp = math.sqrt(x)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 3.7)
		tmp = Float64(x / Float64(2.0 + Float64(x * Float64(0.5 + Float64(x * Float64(Float64(x * 0.0625) - 0.125))))));
	else
		tmp = sqrt(x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 3.7)
		tmp = x / (2.0 + (x * (0.5 + (x * ((x * 0.0625) - 0.125)))));
	else
		tmp = sqrt(x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 3.7], N[(x / N[(2.0 + N[(x * N[(0.5 + N[(x * N[(N[(x * 0.0625), $MachinePrecision] - 0.125), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[Sqrt[x], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 3.7000000000000002

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 99.0%

      \[\leadsto \frac{x}{\color{blue}{2 + x \cdot \left(0.5 + x \cdot \left(0.0625 \cdot x - 0.125\right)\right)}} \]

   if 3.7000000000000002 < x

   1. Initial program 99.2%

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

   3. Taylor expanded in x around inf 97.5%

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

4. Final simplification 98.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 3.7:\\ \;\;\;\;\frac{x}{2 + x \cdot \left(0.5 + x \cdot \left(x \cdot 0.0625 - 0.125\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 68.3% accurate, 11.9× speedup

Math

\[\begin{array}{l} \\ \frac{x}{1 + \left(1 + x \cdot 0.5\right)} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

public static double code(double x) {
	return x / (1.0 + (1.0 + (x * 0.5)));
}

Python

def code(x):
	return x / (1.0 + (1.0 + (x * 0.5)))

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.7%

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

3. Taylor expanded in x around 0 70.8%

   \[\leadsto \frac{x}{1 + \color{blue}{\left(1 + 0.5 \cdot x\right)}} \]

4. Final simplification 70.8%

   \[\leadsto \frac{x}{1 + \left(1 + x \cdot 0.5\right)} \]
5. Add Preprocessing

Alternative 6: 68.3% accurate, 15.3× speedup

Math

\[\begin{array}{l} \\ \frac{x}{2 + x \cdot 0.5} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ x (+ 2.0 (* x 0.5))))

C

double code(double x) {
	return x / (2.0 + (x * 0.5));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x / (2.0d0 + (x * 0.5d0))
end function

Java

public static double code(double x) {
	return x / (2.0 + (x * 0.5));
}

Python

def code(x):
	return x / (2.0 + (x * 0.5))

Julia

function code(x)
	return Float64(x / Float64(2.0 + Float64(x * 0.5)))
end

MATLAB

function tmp = code(x)
	tmp = x / (2.0 + (x * 0.5));
end

Wolfram

code[x_] := N[(x / N[(2.0 + N[(x * 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

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

3. Taylor expanded in x around 0 70.8%

   \[\leadsto \frac{x}{\color{blue}{2 + 0.5 \cdot x}} \]

4. Final simplification 70.8%

   \[\leadsto \frac{x}{2 + x \cdot 0.5} \]
5. Add Preprocessing

Alternative 7: 68.1% accurate, 15.3× speedup

Math

\[\begin{array}{l} \\ \frac{1}{0.5 + \frac{2}{x}} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 1.0 (+ 0.5 (/ 2.0 x))))

C

double code(double x) {
	return 1.0 / (0.5 + (2.0 / x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 1.0d0 / (0.5d0 + (2.0d0 / x))
end function

Java

public static double code(double x) {
	return 1.0 / (0.5 + (2.0 / x));
}

Python

def code(x):
	return 1.0 / (0.5 + (2.0 / x))

Julia

function code(x)
	return Float64(1.0 / Float64(0.5 + Float64(2.0 / x)))
end

MATLAB

function tmp = code(x)
	tmp = 1.0 / (0.5 + (2.0 / x));
end

Wolfram

code[x_] := N[(1.0 / N[(0.5 + N[(2.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

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

3. Taylor expanded in x around 0 70.8%

   \[\leadsto \frac{x}{1 + \color{blue}{\left(1 + 0.5 \cdot x\right)}} \]
4. Step-by-step derivation

   1. clear-num 70.6%

      \[\leadsto \color{blue}{\frac{1}{\frac{1 + \left(1 + 0.5 \cdot x\right)}{x}}} \]

   2. associate-+r+ 70.6%

      \[\leadsto \frac{1}{\frac{\color{blue}{\left(1 + 1\right) + 0.5 \cdot x}}{x}} \]

   3. metadata-eval 70.6%

      \[\leadsto \frac{1}{\frac{\color{blue}{2} + 0.5 \cdot x}{x}} \]

   4. +-commutative 70.6%

      \[\leadsto \frac{1}{\frac{\color{blue}{0.5 \cdot x + 2}}{x}} \]

   5. *-commutative 70.6%

      \[\leadsto \frac{1}{\frac{\color{blue}{x \cdot 0.5} + 2}{x}} \]

5. Applied egg-rr 70.6%

   \[\leadsto \color{blue}{\frac{1}{\frac{x \cdot 0.5 + 2}{x}}} \]

6. Taylor expanded in x around inf 70.6%

   \[\leadsto \frac{1}{\color{blue}{0.5 + 2 \cdot \frac{1}{x}}} \]
7. Step-by-step derivation

   1. associate-*r/ 70.6%

      \[\leadsto \frac{1}{0.5 + \color{blue}{\frac{2 \cdot 1}{x}}} \]

   2. metadata-eval 70.6%

      \[\leadsto \frac{1}{0.5 + \frac{\color{blue}{2}}{x}} \]

8. Simplified 70.6%

   \[\leadsto \frac{1}{\color{blue}{0.5 + \frac{2}{x}}} \]
9. Add Preprocessing

Alternative 8: 67.5% accurate, 35.7× speedup

Math

\[\begin{array}{l} \\ \frac{x}{2} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ x 2.0))

C

double code(double x) {
	return x / 2.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x / 2.0d0
end function

Java

public static double code(double x) {
	return x / 2.0;
}

Python

def code(x):
	return x / 2.0

Julia

function code(x)
	return Float64(x / 2.0)
end

MATLAB

function tmp = code(x)
	tmp = x / 2.0;
end

Wolfram

code[x_] := N[(x / 2.0), $MachinePrecision]

Derivation

1. Initial program 99.7%

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

3. Taylor expanded in x around 0 70.2%

   \[\leadsto \frac{x}{\color{blue}{2}} \]
4. Add Preprocessing

Alternative 9: 4.8% accurate, 107.0× speedup

Math

\[\begin{array}{l} \\ 2 \end{array} \]

FPCore

(FPCore (x) :precision binary64 2.0)

C

double code(double x) {
	return 2.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 2.0d0
end function

Java

public static double code(double x) {
	return 2.0;
}

Python

def code(x):
	return 2.0

Julia

function code(x)
	return 2.0
end

MATLAB

function tmp = code(x)
	tmp = 2.0;
end

Wolfram

code[x_] := 2.0

Derivation

1. Initial program 99.7%

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

3. Taylor expanded in x around 0 70.8%

   \[\leadsto \frac{x}{1 + \color{blue}{\left(1 + 0.5 \cdot x\right)}} \]

4. Taylor expanded in x around inf 4.6%

   \[\leadsto \color{blue}{2} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024097 
(FPCore (x)
  :name "Numeric.Log:$clog1p from log-domain-0.10.2.1, B"
  :precision binary64
  (/ x (+ 1.0 (sqrt (+ x 1.0)))))