arccos

Percentage Accurate: 100.0% → 100.0%

Time: 6.7s

Alternatives: 10

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, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(2.0 * N[ArcTan[N[(1.0 / N[Sqrt[N[(N[(1.0 + x), $MachinePrecision] / N[(1.0 - x), $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. clear-num 100.0%

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

   2. sqrt-div 100.0%

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

   3. metadata-eval 100.0%

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

4. Applied egg-rr 100.0%

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

5. Final simplification 100.0%

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

Alternative 2: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(2.0 * N[ArcTan[N[Power[N[(N[(1.0 + x), $MachinePrecision] / N[(1.0 - x), $MachinePrecision]), $MachinePrecision], -0.5], $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. clear-num 100.0%

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

   3. inv-pow 100.0%

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

   4. pow-pow 100.0%

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

   5. metadata-eval 100.0%

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

4. Applied egg-rr 100.0%

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

5. Final simplification 100.0%

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

Alternative 3: 100.0% accurate, 1.0× speedup

Math

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

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]

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%

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

Alternative 4: 99.5% accurate, 1.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(2.0 * N[ArcTan[N[(1.0 / N[(1.0 + N[(x * N[(1.0 + N[(x * N[(0.5 + N[(x * 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $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. clear-num 100.0%

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

   2. sqrt-div 100.0%

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

   3. metadata-eval 100.0%

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

4. Applied egg-rr 100.0%

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

5. Taylor expanded in x around 0 99.2%

   \[\leadsto 2 \cdot \tan^{-1} \left(\frac{1}{\color{blue}{1 + x \cdot \left(1 + x \cdot \left(0.5 + 0.5 \cdot x\right)\right)}}\right) \]
6. Step-by-step derivation

   1. *-commutative 99.2%

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

7. Simplified 99.2%

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

8. Final simplification 99.2%

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

Alternative 5: 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 99.2%

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

4. Final simplification 99.2%

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

Alternative 6: 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 99.0%

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

4. Final simplification 99.0%

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

Alternative 7: 99.0% accurate, 1.9× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(2.0 * N[ArcTan[N[(1.0 + N[(x * 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. Step-by-step derivation

   1. clear-num 100.0%

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

   2. sqrt-div 100.0%

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

   3. metadata-eval 100.0%

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

4. Applied egg-rr 100.0%

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

5. Taylor expanded in x around 0 98.8%

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

6. Taylor expanded in x around 0 98.8%

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

7. Final simplification 98.8%

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

Alternative 8: 99.0% accurate, 2.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(2.0 * N[ArcTan[N[(1.0 / N[(1.0 + x), $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. clear-num 100.0%

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

   2. sqrt-div 100.0%

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

   3. metadata-eval 100.0%

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

4. Applied egg-rr 100.0%

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

5. Taylor expanded in x around 0 98.8%

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

6. Final simplification 98.8%

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

Alternative 9: 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 98.8%

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

   1. neg-mul-1 98.8%

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

   2. sub-neg 98.8%

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

5. Simplified 98.8%

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

6. Final simplification 98.8%

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

Alternative 10: 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 98.1%

   \[\leadsto 2 \cdot \tan^{-1} \color{blue}{1} \]

4. Final simplification 98.1%

   \[\leadsto 2 \cdot \tan^{-1} 1 \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024077 
(FPCore (x)
  :name "arccos"
  :precision binary64
  (* 2.0 (atan (sqrt (/ (- 1.0 x) (+ 1.0 x))))))