Rust f64::atanh

Percentage Accurate: 100.0% → 100.0%

Time: 6.3s

Alternatives: 5

Speedup: 1.0×

Specification

Math

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

FPCore

(FPCore (x) :precision binary64 (atanh x))

C

double code(double x) {
	return atanh(x);
}

Python

def code(x):
	return math.atanh(x)

Julia

function code(x)
	return atanh(x)
end

MATLAB

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

Wolfram

code[x_] := N[ArcTanh[x], $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 0.5 (log1p (/ (* 2.0 x) (- 1.0 x)))))

C

double code(double x) {
	return 0.5 * log1p(((2.0 * x) / (1.0 - x)));
}

Java

public static double code(double x) {
	return 0.5 * Math.log1p(((2.0 * x) / (1.0 - x)));
}

Python

def code(x):
	return 0.5 * math.log1p(((2.0 * x) / (1.0 - x)))

Julia

function code(x)
	return Float64(0.5 * log1p(Float64(Float64(2.0 * x) / Float64(1.0 - x))))
end

Wolfram

code[x_] := N[(0.5 * N[Log[1 + N[(N[(2.0 * x), $MachinePrecision] / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 0.5 (log1p (/ (* 2.0 x) (- 1.0 x)))))

C

double code(double x) {
	return 0.5 * log1p(((2.0 * x) / (1.0 - x)));
}

Java

public static double code(double x) {
	return 0.5 * Math.log1p(((2.0 * x) / (1.0 - x)));
}

Python

def code(x):
	return 0.5 * math.log1p(((2.0 * x) / (1.0 - x)))

Julia

function code(x)
	return Float64(0.5 * log1p(Float64(Float64(2.0 * x) / Float64(1.0 - x))))
end

Wolfram

code[x_] := N[(0.5 * N[Log[1 + N[(N[(2.0 * x), $MachinePrecision] / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 2: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot \mathsf{log1p}\left(\frac{1}{\frac{0.5}{x} + -0.5}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 0.5 (log1p (/ 1.0 (+ (/ 0.5 x) -0.5)))))

C

double code(double x) {
	return 0.5 * log1p((1.0 / ((0.5 / x) + -0.5)));
}

Java

public static double code(double x) {
	return 0.5 * Math.log1p((1.0 / ((0.5 / x) + -0.5)));
}

Python

def code(x):
	return 0.5 * math.log1p((1.0 / ((0.5 / x) + -0.5)))

Julia

function code(x)
	return Float64(0.5 * log1p(Float64(1.0 / Float64(Float64(0.5 / x) + -0.5))))
end

Wolfram

code[x_] := N[(0.5 * N[Log[1 + N[(1.0 / N[(N[(0.5 / x), $MachinePrecision] + -0.5), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. add-log-exp 9.0%

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

   2. *-un-lft-identity 9.0%

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

   3. log-prod 9.0%

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

   4. metadata-eval 9.0%

      \[\leadsto \color{blue}{0} + \log \left(e^{0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right)}\right) \]

   5. add-log-exp 100.0%

      \[\leadsto 0 + \color{blue}{0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right)} \]

   6. associate-/l* 100.0%

      \[\leadsto 0 + 0.5 \cdot \mathsf{log1p}\left(\color{blue}{2 \cdot \frac{x}{1 - x}}\right) \]

4. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{0 + 0.5 \cdot \mathsf{log1p}\left(2 \cdot \frac{x}{1 - x}\right)} \]
5. Step-by-step derivation

   1. +-lft-identity 100.0%

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

   2. associate-*r/ 100.0%

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

   3. *-lft-identity 100.0%

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

   4. associate-*l/ 100.0%

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

   5. associate-/r/ 99.7%

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

   6. div-sub 99.7%

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

   7. sub-neg 99.7%

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

   8. associate-/r* 99.7%

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

   9. metadata-eval 99.7%

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

   10. *-commutative 99.7%

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

   11. associate-/r* 99.7%

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

   12. *-inverses 99.7%

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

   13. metadata-eval 99.7%

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

   14. metadata-eval 99.7%

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

6. Simplified 99.7%

   \[\leadsto \color{blue}{0.5 \cdot \mathsf{log1p}\left(\frac{1}{\frac{0.5}{x} + -0.5}\right)} \]
7. Add Preprocessing

Alternative 3: 99.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + 0.3333333333333333 \cdot {x}^{3} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (+ x (* 0.3333333333333333 (pow x 3.0))))

C

double code(double x) {
	return x + (0.3333333333333333 * pow(x, 3.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x + (0.3333333333333333d0 * (x ** 3.0d0))
end function

Java

public static double code(double x) {
	return x + (0.3333333333333333 * Math.pow(x, 3.0));
}

Python

def code(x):
	return x + (0.3333333333333333 * math.pow(x, 3.0))

Julia

function code(x)
	return Float64(x + Float64(0.3333333333333333 * (x ^ 3.0)))
end

MATLAB

function tmp = code(x)
	tmp = x + (0.3333333333333333 * (x ^ 3.0));
end

Wolfram

code[x_] := N[(x + N[(0.3333333333333333 * N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 99.0%

   \[\leadsto \color{blue}{x \cdot \left(1 + 0.3333333333333333 \cdot {x}^{2}\right)} \]
4. Step-by-step derivation

   1. distribute-rgt-in 99.0%

      \[\leadsto \color{blue}{1 \cdot x + \left(0.3333333333333333 \cdot {x}^{2}\right) \cdot x} \]

   2. *-lft-identity 99.0%

      \[\leadsto \color{blue}{x} + \left(0.3333333333333333 \cdot {x}^{2}\right) \cdot x \]

   3. associate-*l* 99.0%

      \[\leadsto x + \color{blue}{0.3333333333333333 \cdot \left({x}^{2} \cdot x\right)} \]

   4. unpow2 99.0%

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

   5. unpow3 99.0%

      \[\leadsto x + 0.3333333333333333 \cdot \color{blue}{{x}^{3}} \]

5. Simplified 99.0%

   \[\leadsto \color{blue}{x + 0.3333333333333333 \cdot {x}^{3}} \]
6. Add Preprocessing

Alternative 4: 99.5% accurate, 12.1× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(1 + 0.3333333333333333 \cdot \left(x \cdot x\right)\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x (+ 1.0 (* 0.3333333333333333 (* x x)))))

C

double code(double x) {
	return x * (1.0 + (0.3333333333333333 * (x * x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x * (1.0d0 + (0.3333333333333333d0 * (x * x)))
end function

Java

public static double code(double x) {
	return x * (1.0 + (0.3333333333333333 * (x * x)));
}

Python

def code(x):
	return x * (1.0 + (0.3333333333333333 * (x * x)))

Julia

function code(x)
	return Float64(x * Float64(1.0 + Float64(0.3333333333333333 * Float64(x * x))))
end

MATLAB

function tmp = code(x)
	tmp = x * (1.0 + (0.3333333333333333 * (x * x)));
end

Wolfram

code[x_] := N[(x * N[(1.0 + N[(0.3333333333333333 * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 99.0%

   \[\leadsto \color{blue}{x \cdot \left(1 + 0.3333333333333333 \cdot {x}^{2}\right)} \]
4. Step-by-step derivation

   1. unpow2 99.0%

      \[\leadsto x \cdot \left(1 + 0.3333333333333333 \cdot \color{blue}{\left(x \cdot x\right)}\right) \]

5. Applied egg-rr 99.0%

   \[\leadsto x \cdot \left(1 + 0.3333333333333333 \cdot \color{blue}{\left(x \cdot x\right)}\right) \]
6. Add Preprocessing

Alternative 5: 99.0% accurate, 109.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 x)

C

double code(double x) {
	return x;
}

Fortran

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

Java

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

Python

def code(x):
	return x

Julia

function code(x)
	return x
end

MATLAB

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

Wolfram

code[x_] := x

Derivation

1. Initial program 100.0%

   \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 98.3%

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

Reproduce

herbie shell --seed 2024136 
(FPCore (x)
  :name "Rust f64::atanh"
  :precision binary64
  (* 0.5 (log1p (/ (* 2.0 x) (- 1.0 x)))))