Hyperbolic arc-(co)tangent

Percentage Accurate: 8.3% → 100.0%

Time: 7.3s

Alternatives: 4

Speedup: 22.2×

Specification

Math

\[\begin{array}{l} \\ \frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (/ 1.0 2.0) (log (/ (+ 1.0 x) (- 1.0 x)))))

C

double code(double x) {
	return (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
}

Fortran

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

Java

public static double code(double x) {
	return (1.0 / 2.0) * Math.log(((1.0 + x) / (1.0 - x)));
}

Python

def code(x):
	return (1.0 / 2.0) * math.log(((1.0 + x) / (1.0 - x)))

Julia

function code(x)
	return Float64(Float64(1.0 / 2.0) * log(Float64(Float64(1.0 + x) / Float64(1.0 - x))))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
end

Wolfram

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

Initial Program: 8.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (/ 1.0 2.0) (log (/ (+ 1.0 x) (- 1.0 x)))))

C

double code(double x) {
	return (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
}

Fortran

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

Java

public static double code(double x) {
	return (1.0 / 2.0) * Math.log(((1.0 + x) / (1.0 - x)));
}

Python

def code(x):
	return (1.0 / 2.0) * math.log(((1.0 + x) / (1.0 - x)))

Julia

function code(x)
	return Float64(Float64(1.0 / 2.0) * log(Float64(Float64(1.0 + x) / Float64(1.0 - x))))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
end

Wolfram

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

Alternative 1: 100.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(-x\right)\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 0.5 (- (log1p x) (log1p (- x)))))

C

double code(double x) {
	return 0.5 * (log1p(x) - log1p(-x));
}

Java

public static double code(double x) {
	return 0.5 * (Math.log1p(x) - Math.log1p(-x));
}

Python

def code(x):
	return 0.5 * (math.log1p(x) - math.log1p(-x))

Julia

function code(x)
	return Float64(0.5 * Float64(log1p(x) - log1p(Float64(-x))))
end

Wolfram

code[x_] := N[(0.5 * N[(N[Log[1 + x], $MachinePrecision] - N[Log[1 + (-x)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 8.5%

   \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
2. Step-by-step derivation

   1. metadata-eval 8.5%

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

   2. log-div 8.5%

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

   3. log1p-def 21.1%

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

   4. sub-neg 21.1%

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

   5. log1p-def 100.0%

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

3. Simplified 100.0%

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

4. Final simplification 100.0%

   \[\leadsto 0.5 \cdot \left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(-x\right)\right) \]

Alternative 2: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot \left(0.6666666666666666 \cdot {x}^{3} + x \cdot 2\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* 0.5 (+ (* 0.6666666666666666 (pow x 3.0)) (* x 2.0))))

C

double code(double x) {
	return 0.5 * ((0.6666666666666666 * pow(x, 3.0)) + (x * 2.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.5d0 * ((0.6666666666666666d0 * (x ** 3.0d0)) + (x * 2.0d0))
end function

Java

public static double code(double x) {
	return 0.5 * ((0.6666666666666666 * Math.pow(x, 3.0)) + (x * 2.0));
}

Python

def code(x):
	return 0.5 * ((0.6666666666666666 * math.pow(x, 3.0)) + (x * 2.0))

Julia

function code(x)
	return Float64(0.5 * Float64(Float64(0.6666666666666666 * (x ^ 3.0)) + Float64(x * 2.0)))
end

MATLAB

function tmp = code(x)
	tmp = 0.5 * ((0.6666666666666666 * (x ^ 3.0)) + (x * 2.0));
end

Wolfram

code[x_] := N[(0.5 * N[(N[(0.6666666666666666 * N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision] + N[(x * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 8.5%

   \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
2. Step-by-step derivation

   1. metadata-eval 8.5%

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

3. Simplified 8.5%

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

4. Taylor expanded in x around 0 99.5%

   \[\leadsto 0.5 \cdot \color{blue}{\left(0.6666666666666666 \cdot {x}^{3} + 2 \cdot x\right)} \]

5. Final simplification 99.5%

   \[\leadsto 0.5 \cdot \left(0.6666666666666666 \cdot {x}^{3} + x \cdot 2\right) \]

Alternative 3: 99.1% accurate, 22.2× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot \left(x \cdot 2\right) \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 8.5%

   \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
2. Step-by-step derivation

   1. metadata-eval 8.5%

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

3. Simplified 8.5%

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

4. Taylor expanded in x around 0 98.9%

   \[\leadsto 0.5 \cdot \color{blue}{\left(2 \cdot x\right)} \]

5. Final simplification 98.9%

   \[\leadsto 0.5 \cdot \left(x \cdot 2\right) \]

Alternative 4: 5.4% accurate, 111.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (x) :precision binary64 0.0)

C

double code(double x) {
	return 0.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.0d0
end function

Java

public static double code(double x) {
	return 0.0;
}

Python

def code(x):
	return 0.0

Julia

function code(x)
	return 0.0
end

MATLAB

function tmp = code(x)
	tmp = 0.0;
end

Wolfram

code[x_] := 0.0

Derivation

1. Initial program 8.5%

   \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
2. Step-by-step derivation

   1. metadata-eval 8.5%

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

3. Simplified 8.5%

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

   1. clear-num 8.5%

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

   2. associate-/r/ 8.6%

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

   3. frac-2neg 8.6%

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

   4. metadata-eval 8.6%

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

   5. sub-neg 8.6%

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

   6. add-sqr-sqrt 4.3%

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

   7. sqrt-unprod 7.0%

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

   8. sqr-neg 7.0%

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

   9. sqrt-unprod 2.7%

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

   10. add-sqr-sqrt 5.4%

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

   11. *-un-lft-identity 5.4%

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

   12. *-un-lft-identity 5.4%

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

   13. +-commutative 5.4%

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

   14. distribute-neg-in 5.4%

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

   15. add-sqr-sqrt 2.8%

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

   16. sqrt-unprod 7.0%

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

   17. sqr-neg 7.0%

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

   18. sqrt-unprod 4.2%

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

   19. add-sqr-sqrt 8.6%

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

   20. metadata-eval 8.6%

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

5. Applied egg-rr 8.6%

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

   1. add-cbrt-cube 8.6%

      \[\leadsto 0.5 \cdot \color{blue}{\sqrt[3]{\left(\log \left(\frac{-1}{x + -1} \cdot \left(1 + x\right)\right) \cdot \log \left(\frac{-1}{x + -1} \cdot \left(1 + x\right)\right)\right) \cdot \log \left(\frac{-1}{x + -1} \cdot \left(1 + x\right)\right)}} \]

   2. pow3 8.6%

      \[\leadsto 0.5 \cdot \sqrt[3]{\color{blue}{{\log \left(\frac{-1}{x + -1} \cdot \left(1 + x\right)\right)}^{3}}} \]

   3. *-commutative 8.6%

      \[\leadsto 0.5 \cdot \sqrt[3]{{\log \color{blue}{\left(\left(1 + x\right) \cdot \frac{-1}{x + -1}\right)}}^{3}} \]

   4. frac-2neg 8.6%

      \[\leadsto 0.5 \cdot \sqrt[3]{{\log \left(\left(1 + x\right) \cdot \color{blue}{\frac{--1}{-\left(x + -1\right)}}\right)}^{3}} \]

   5. metadata-eval 8.6%

      \[\leadsto 0.5 \cdot \sqrt[3]{{\log \left(\left(1 + x\right) \cdot \frac{\color{blue}{1}}{-\left(x + -1\right)}\right)}^{3}} \]

   6. un-div-inv 8.5%

      \[\leadsto 0.5 \cdot \sqrt[3]{{\log \color{blue}{\left(\frac{1 + x}{-\left(x + -1\right)}\right)}}^{3}} \]

   7. log-div 8.5%

      \[\leadsto 0.5 \cdot \sqrt[3]{{\color{blue}{\left(\log \left(1 + x\right) - \log \left(-\left(x + -1\right)\right)\right)}}^{3}} \]

   8. log1p-udef 12.9%

      \[\leadsto 0.5 \cdot \sqrt[3]{{\left(\color{blue}{\mathsf{log1p}\left(x\right)} - \log \left(-\left(x + -1\right)\right)\right)}^{3}} \]

   9. +-commutative 12.9%

      \[\leadsto 0.5 \cdot \sqrt[3]{{\left(\mathsf{log1p}\left(x\right) - \log \left(-\color{blue}{\left(-1 + x\right)}\right)\right)}^{3}} \]

   10. distribute-neg-in 12.9%

       \[\leadsto 0.5 \cdot \sqrt[3]{{\left(\mathsf{log1p}\left(x\right) - \log \color{blue}{\left(\left(--1\right) + \left(-x\right)\right)}\right)}^{3}} \]

   11. metadata-eval 12.9%

       \[\leadsto 0.5 \cdot \sqrt[3]{{\left(\mathsf{log1p}\left(x\right) - \log \left(\color{blue}{1} + \left(-x\right)\right)\right)}^{3}} \]

   12. log1p-def 36.8%

       \[\leadsto 0.5 \cdot \sqrt[3]{{\left(\mathsf{log1p}\left(x\right) - \color{blue}{\mathsf{log1p}\left(-x\right)}\right)}^{3}} \]

7. Applied egg-rr 36.8%

   \[\leadsto 0.5 \cdot \color{blue}{\sqrt[3]{{\left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(-x\right)\right)}^{3}}} \]
8. Step-by-step derivation

   1. rem-cbrt-cube 100.0%

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

   2. sub-neg 100.0%

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

   3. add-sqr-sqrt 50.6%

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

   4. sqrt-unprod 36.8%

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

   5. sqr-neg 36.8%

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

   6. sqrt-unprod 3.3%

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

   7. add-sqr-sqrt 5.4%

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

9. Applied egg-rr 5.4%

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

    1. sub-neg 5.4%

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

    2. +-inverses 5.4%

       \[\leadsto 0.5 \cdot \color{blue}{0} \]

11. Simplified 5.4%

    \[\leadsto 0.5 \cdot \color{blue}{0} \]

12. Final simplification 5.4%

    \[\leadsto 0 \]

Reproduce

herbie shell --seed 2023271 
(FPCore (x)
  :name "Hyperbolic arc-(co)tangent"
  :precision binary64
  (* (/ 1.0 2.0) (log (/ (+ 1.0 x) (- 1.0 x)))))