arccos

Percentage Accurate: 100.0% → 100.0%

Time: 6.6s

Alternatives: 7

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.5× speedup

Math

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

FPCore

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

C

double code(double x) {
	return 2.0 * atan(exp(((log1p(-x) - log1p(x)) * 0.5)));
}

Java

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

Python

def code(x):
	return 2.0 * math.atan(math.exp(((math.log1p(-x) - math.log1p(x)) * 0.5)))

Julia

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[2 \cdot \tan^{-1} \left(\sqrt{\frac{1 - x}{1 + x}}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. pow1/2 100.0%

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

   2. pow-to-exp 100.0%

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

   3. log-div 100.0%

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

   4. sub-neg 100.0%

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

   5. log1p-define 100.0%

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

   6. log1p-define 100.0%

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

4. Applied egg-rr 100.0%

   \[\leadsto 2 \cdot \tan^{-1} \color{blue}{\left(e^{\left(\mathsf{log1p}\left(-x\right) - \mathsf{log1p}\left(x\right)\right) \cdot 0.5}\right)} \]
5. Add Preprocessing

Alternative 2: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[2 \cdot \tan^{-1} \left(\sqrt{\frac{1 - x}{1 + x}}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. div-sub 100.0%

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

4. Applied egg-rr 100.0%

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

5. Final simplification 100.0%

   \[\leadsto 2 \cdot \tan^{-1} \left(\sqrt{\frac{1}{x + 1} - \frac{x}{x + 1}}\right) \]
6. Add Preprocessing

Alternative 3: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[2 \cdot \tan^{-1} \left(\sqrt{\frac{1 - x}{1 + x}}\right) \]
2. Add Preprocessing

3. Final simplification 100.0%

   \[\leadsto 2 \cdot \tan^{-1} \left(\sqrt{\frac{1 - x}{x + 1}}\right) \]
4. Add Preprocessing

Alternative 4: 99.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \tan^{-1} \left(1 + x \cdot \left(x \cdot \left(0.5 + x \cdot -0.5\right) + -1\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* 2.0 (atan (+ 1.0 (* x (+ (* x (+ 0.5 (* x -0.5))) -1.0))))))

C

double code(double x) {
	return 2.0 * atan((1.0 + (x * ((x * (0.5 + (x * -0.5))) + -1.0))));
}

Fortran

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

Java

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

Python

def code(x):
	return 2.0 * math.atan((1.0 + (x * ((x * (0.5 + (x * -0.5))) + -1.0))))

Julia

function code(x)
	return Float64(2.0 * atan(Float64(1.0 + Float64(x * Float64(Float64(x * Float64(0.5 + Float64(x * -0.5))) + -1.0)))))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 * atan((1.0 + (x * ((x * (0.5 + (x * -0.5))) + -1.0))));
end

Wolfram

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

Derivation

1. Initial program 100.0%

   \[2 \cdot \tan^{-1} \left(\sqrt{\frac{1 - x}{1 + x}}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 99.6%

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

4. Final simplification 99.6%

   \[\leadsto 2 \cdot \tan^{-1} \left(1 + x \cdot \left(x \cdot \left(0.5 + x \cdot -0.5\right) + -1\right)\right) \]
5. Add Preprocessing

Alternative 5: 99.3% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \tan^{-1} \left(1 + x \cdot \left(x \cdot 0.5 + -1\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* 2.0 (atan (+ 1.0 (* x (+ (* x 0.5) -1.0))))))

C

double code(double x) {
	return 2.0 * atan((1.0 + (x * ((x * 0.5) + -1.0))));
}

Fortran

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

Java

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

Python

def code(x):
	return 2.0 * math.atan((1.0 + (x * ((x * 0.5) + -1.0))))

Julia

function code(x)
	return Float64(2.0 * atan(Float64(1.0 + Float64(x * Float64(Float64(x * 0.5) + -1.0)))))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 * atan((1.0 + (x * ((x * 0.5) + -1.0))));
end

Wolfram

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

Derivation

1. Initial program 100.0%

   \[2 \cdot \tan^{-1} \left(\sqrt{\frac{1 - x}{1 + x}}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 99.4%

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

4. Final simplification 99.4%

   \[\leadsto 2 \cdot \tan^{-1} \left(1 + x \cdot \left(x \cdot 0.5 + -1\right)\right) \]
5. Add Preprocessing

Alternative 6: 99.0% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \tan^{-1} \left(1 - x\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 2.0 (atan (- 1.0 x))))

C

double code(double x) {
	return 2.0 * atan((1.0 - x));
}

Fortran

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

Java

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

Python

def code(x):
	return 2.0 * math.atan((1.0 - x))

Julia

function code(x)
	return Float64(2.0 * atan(Float64(1.0 - x)))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 * atan((1.0 - x));
end

Wolfram

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

Derivation

1. Initial program 100.0%

   \[2 \cdot \tan^{-1} \left(\sqrt{\frac{1 - x}{1 + x}}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 99.1%

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

   1. neg-mul-1 99.1%

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

   2. sub-neg 99.1%

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

5. Simplified 99.1%

   \[\leadsto 2 \cdot \tan^{-1} \color{blue}{\left(1 - x\right)} \]
6. Add Preprocessing

Alternative 7: 97.9% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \tan^{-1} 1 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 2.0 (atan 1.0)))

C

double code(double x) {
	return 2.0 * atan(1.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 2.0d0 * atan(1.0d0)
end function

Java

public static double code(double x) {
	return 2.0 * Math.atan(1.0);
}

Python

def code(x):
	return 2.0 * math.atan(1.0)

Julia

function code(x)
	return Float64(2.0 * atan(1.0))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 * atan(1.0);
end

Wolfram

code[x_] := N[(2.0 * N[ArcTan[1.0], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[2 \cdot \tan^{-1} \left(\sqrt{\frac{1 - x}{1 + x}}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0 98.0%

   \[\leadsto 2 \cdot \tan^{-1} \color{blue}{1} \]
4. Add Preprocessing

Reproduce

herbie shell --seed 2024172 
(FPCore (x)
  :name "arccos"
  :precision binary64
  (* 2.0 (atan (sqrt (/ (- 1.0 x) (+ 1.0 x))))))