Hyperbolic arc-(co)secant

Percentage Accurate: 100.0% → 100.0%

Time: 4.6s

Alternatives: 6

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.5× speedup

Math

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

FPCore

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

C

double code(double x) {
	return 2.0 * log((sqrt(fma(x, sqrt((1.0 - (x * x))), x)) / x));
}

Julia

function code(x)
	return Float64(2.0 * log(Float64(sqrt(fma(x, sqrt(Float64(1.0 - Float64(x * x))), x)) / x)))
end

Wolfram

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

Derivation

1. Initial program 99.6%

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

   1. add-sqr-sqrt 99.6%

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

   2. log-prod 99.6%

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

   3. frac-add 56.3%

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

   4. sqrt-div 56.3%

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

   5. *-un-lft-identity 56.3%

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

   6. +-commutative 56.3%

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

   7. fma-def 56.3%

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

   8. sqrt-prod 99.6%

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

   9. add-sqr-sqrt 99.6%

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

   10. frac-add 56.3%

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

3. Applied egg-rr 100.0%

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

   1. count-2 100.0%

      \[\leadsto \color{blue}{2 \cdot \log \left(\frac{\sqrt{\mathsf{fma}\left(x, \sqrt{1 - x \cdot x}, x\right)}}{x}\right)} \]

5. Simplified 100.0%

   \[\leadsto \color{blue}{2 \cdot \log \left(\frac{\sqrt{\mathsf{fma}\left(x, \sqrt{1 - x \cdot x}, x\right)}}{x}\right)} \]

6. Final simplification 100.0%

   \[\leadsto 2 \cdot \log \left(\frac{\sqrt{\mathsf{fma}\left(x, \sqrt{1 - x \cdot x}, x\right)}}{x}\right) \]

Alternative 2: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.6%

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

2. Final simplification 99.6%

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

Alternative 3: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.6%

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

   1. expm1-log1p-u 99.6%

      \[\leadsto \log \color{blue}{\left(\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{1}{x} + \frac{\sqrt{1 - x \cdot x}}{x}\right)\right)\right)} \]

   2. expm1-udef 99.6%

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

   3. *-un-lft-identity 99.6%

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

   4. div-inv 99.6%

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

   5. distribute-rgt-out 99.6%

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

3. Applied egg-rr 99.6%

   \[\leadsto \log \color{blue}{\left(e^{\mathsf{log1p}\left(\frac{1}{x} \cdot \left(1 + \sqrt{1 - x \cdot x}\right)\right)} - 1\right)} \]
4. Step-by-step derivation

   1. expm1-def 99.6%

      \[\leadsto \log \color{blue}{\left(\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{1}{x} \cdot \left(1 + \sqrt{1 - x \cdot x}\right)\right)\right)\right)} \]

   2. expm1-log1p 99.6%

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

   3. +-commutative 99.6%

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

   4. associate-*l/ 99.6%

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

   5. *-lft-identity 99.6%

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

   6. +-commutative 99.6%

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

5. Simplified 99.6%

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

6. Final simplification 99.6%

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

Alternative 4: 99.2% accurate, 2.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

function tmp = code(x)
	tmp = -log((x * 0.5));
end

Wolfram

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

Derivation

1. Initial program 99.6%

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

2. Taylor expanded in x around 0 98.9%

   \[\leadsto \log \color{blue}{\left(\frac{2}{x}\right)} \]
3. Step-by-step derivation

   1. clear-num 98.9%

      \[\leadsto \log \color{blue}{\left(\frac{1}{\frac{x}{2}}\right)} \]

   2. log-rec 99.3%

      \[\leadsto \color{blue}{-\log \left(\frac{x}{2}\right)} \]

   3. div-inv 99.3%

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

   4. metadata-eval 99.3%

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

4. Applied egg-rr 99.3%

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

5. Final simplification 99.3%

   \[\leadsto -\log \left(x \cdot 0.5\right) \]

Alternative 5: 99.2% accurate, 2.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 (log (/ 2.0 x)))

C

double code(double x) {
	return log((2.0 / x));
}

Fortran

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

Java

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

Python

def code(x):
	return math.log((2.0 / x))

Julia

function code(x)
	return log(Float64(2.0 / x))
end

MATLAB

function tmp = code(x)
	tmp = log((2.0 / x));
end

Wolfram

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

Derivation

1. Initial program 99.6%

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

2. Taylor expanded in x around 0 98.9%

   \[\leadsto \log \color{blue}{\left(\frac{2}{x}\right)} \]

3. Final simplification 98.9%

   \[\leadsto \log \left(\frac{2}{x}\right) \]

Alternative 6: 2.7% accurate, 42.2× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(x \cdot -0.25\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x (* x -0.25)))

C

double code(double x) {
	return x * (x * -0.25);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x * (x * (-0.25d0))
end function

Java

public static double code(double x) {
	return x * (x * -0.25);
}

Python

def code(x):
	return x * (x * -0.25)

Julia

function code(x)
	return Float64(x * Float64(x * -0.25))
end

MATLAB

function tmp = code(x)
	tmp = x * (x * -0.25);
end

Wolfram

code[x_] := N[(x * N[(x * -0.25), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

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

2. Taylor expanded in x around 0 99.2%

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

3. Taylor expanded in x around 0 99.3%

   \[\leadsto \color{blue}{\log 2 + \left(-0.25 \cdot {x}^{2} + -1 \cdot \log x\right)} \]
4. Step-by-step derivation

   1. +-commutative 99.3%

      \[\leadsto \color{blue}{\left(-0.25 \cdot {x}^{2} + -1 \cdot \log x\right) + \log 2} \]

   2. neg-mul-1 99.3%

      \[\leadsto \left(-0.25 \cdot {x}^{2} + \color{blue}{\left(-\log x\right)}\right) + \log 2 \]

   3. associate-+l+ 99.3%

      \[\leadsto \color{blue}{-0.25 \cdot {x}^{2} + \left(\left(-\log x\right) + \log 2\right)} \]

   4. unpow2 99.3%

      \[\leadsto -0.25 \cdot \color{blue}{\left(x \cdot x\right)} + \left(\left(-\log x\right) + \log 2\right) \]

   5. *-commutative 99.3%

      \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot -0.25} + \left(\left(-\log x\right) + \log 2\right) \]

   6. associate-*l* 99.3%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot -0.25\right)} + \left(\left(-\log x\right) + \log 2\right) \]

   7. +-commutative 99.3%

      \[\leadsto x \cdot \left(x \cdot -0.25\right) + \color{blue}{\left(\log 2 + \left(-\log x\right)\right)} \]

   8. sub-neg 99.3%

      \[\leadsto x \cdot \left(x \cdot -0.25\right) + \color{blue}{\left(\log 2 - \log x\right)} \]

   9. log-div 99.2%

      \[\leadsto x \cdot \left(x \cdot -0.25\right) + \color{blue}{\log \left(\frac{2}{x}\right)} \]

5. Simplified 99.2%

   \[\leadsto \color{blue}{x \cdot \left(x \cdot -0.25\right) + \log \left(\frac{2}{x}\right)} \]

6. Taylor expanded in x around inf 2.7%

   \[\leadsto \color{blue}{-0.25 \cdot {x}^{2}} \]
7. Step-by-step derivation

   1. unpow2 2.7%

      \[\leadsto -0.25 \cdot \color{blue}{\left(x \cdot x\right)} \]

   2. *-commutative 2.7%

      \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot -0.25} \]

   3. associate-*r* 2.7%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot -0.25\right)} \]

8. Simplified 2.7%

   \[\leadsto \color{blue}{x \cdot \left(x \cdot -0.25\right)} \]

9. Final simplification 2.7%

   \[\leadsto x \cdot \left(x \cdot -0.25\right) \]

Reproduce

herbie shell --seed 2023174 
(FPCore (x)
  :name "Hyperbolic arc-(co)secant"
  :precision binary64
  (log (+ (/ 1.0 x) (/ (sqrt (- 1.0 (* x x))) x))))