Hyperbolic arc-(co)tangent

Percentage Accurate: 8.4% → 100.0%

Time: 8.5s

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: 8.4% 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, 0.4× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot \left(\mathsf{log1p}\left(-{x}^{2}\right) - 2 \cdot \mathsf{log1p}\left(-x\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* 0.5 (- (log1p (- (pow x 2.0))) (* 2.0 (log1p (- x))))))

C

double code(double x) {
	return 0.5 * (log1p(-pow(x, 2.0)) - (2.0 * log1p(-x)));
}

Java

public static double code(double x) {
	return 0.5 * (Math.log1p(-Math.pow(x, 2.0)) - (2.0 * Math.log1p(-x)));
}

Python

def code(x):
	return 0.5 * (math.log1p(-math.pow(x, 2.0)) - (2.0 * math.log1p(-x)))

Julia

function code(x)
	return Float64(0.5 * Float64(log1p(Float64(-(x ^ 2.0))) - Float64(2.0 * log1p(Float64(-x)))))
end

Wolfram

code[x_] := N[(0.5 * N[(N[Log[1 + (-N[Power[x, 2.0], $MachinePrecision])], $MachinePrecision] - N[(2.0 * N[Log[1 + (-x)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 8.3%

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

   1. metadata-eval 8.3%

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

3. Simplified 8.3%

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

   1. flip-+ 8.3%

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

   2. associate-/l/ 8.3%

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

   3. log-div 8.3%

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

   4. metadata-eval 8.3%

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

   5. sub-neg 8.3%

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

   6. log1p-define 8.5%

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

   7. pow2 8.5%

      \[\leadsto 0.5 \cdot \left(\mathsf{log1p}\left(-\color{blue}{{x}^{2}}\right) - \log \left(\left(1 - x\right) \cdot \left(1 - x\right)\right)\right) \]

   8. pow2 8.5%

      \[\leadsto 0.5 \cdot \left(\mathsf{log1p}\left(-{x}^{2}\right) - \log \color{blue}{\left({\left(1 - x\right)}^{2}\right)}\right) \]

   9. log-pow 8.5%

      \[\leadsto 0.5 \cdot \left(\mathsf{log1p}\left(-{x}^{2}\right) - \color{blue}{2 \cdot \log \left(1 - x\right)}\right) \]

   10. sub-neg 8.5%

       \[\leadsto 0.5 \cdot \left(\mathsf{log1p}\left(-{x}^{2}\right) - 2 \cdot \log \color{blue}{\left(1 + \left(-x\right)\right)}\right) \]

   11. log1p-define 100.0%

       \[\leadsto 0.5 \cdot \left(\mathsf{log1p}\left(-{x}^{2}\right) - 2 \cdot \color{blue}{\mathsf{log1p}\left(-x\right)}\right) \]

6. Applied egg-rr 100.0%

   \[\leadsto 0.5 \cdot \color{blue}{\left(\mathsf{log1p}\left(-{x}^{2}\right) - 2 \cdot \mathsf{log1p}\left(-x\right)\right)} \]

7. Final simplification 100.0%

   \[\leadsto 0.5 \cdot \left(\mathsf{log1p}\left(-{x}^{2}\right) - 2 \cdot \mathsf{log1p}\left(-x\right)\right) \]
8. Add Preprocessing

Alternative 2: 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 8.3%

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

   1. metadata-eval 8.3%

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

3. Simplified 8.3%

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

   1. *-un-lft-identity 8.3%

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

   2. *-commutative 8.3%

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

   3. log-prod 8.3%

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

   4. log-div 8.3%

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

   5. log1p-define 20.9%

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

   6. sub-neg 20.9%

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

   7. log1p-define 100.0%

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

   8. metadata-eval 100.0%

      \[\leadsto 0.5 \cdot \left(\left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(-x\right)\right) + \color{blue}{0}\right) \]

6. Applied egg-rr 100.0%

   \[\leadsto 0.5 \cdot \color{blue}{\left(\left(\mathsf{log1p}\left(x\right) - \mathsf{log1p}\left(-x\right)\right) + 0\right)} \]
7. Step-by-step derivation

   1. +-rgt-identity 100.0%

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

8. Simplified 100.0%

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

9. Final simplification 100.0%

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

Alternative 3: 99.6% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 8.3%

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

   1. metadata-eval 8.3%

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

3. Simplified 8.3%

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

5. Taylor expanded in x around 0 99.5%

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

6. Final simplification 99.5%

   \[\leadsto 0.5 \cdot \left(x \cdot \left(2 + {x}^{2} \cdot 0.6666666666666666\right)\right) \]
7. Add Preprocessing

Alternative 4: 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 8.3%

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

   1. metadata-eval 8.3%

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

3. Simplified 8.3%

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

5. Taylor expanded in x around 0 99.1%

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

6. Final simplification 99.1%

   \[\leadsto 0.5 \cdot \left(x \cdot 2\right) \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024112 
(FPCore (x)
  :name "Hyperbolic arc-(co)tangent"
  :precision binary64
  (* (/ 1.0 2.0) (log (/ (+ 1.0 x) (- 1.0 x)))))