Hyperbolic arc-(co)secant

Percentage Accurate: 99.9% → 99.7%

Time: 8.1s

Alternatives: 4

Speedup: N/A×

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: 99.9% 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: 99.7% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \log \left(\frac{2 + \left(x \cdot x\right) \cdot \left(-0.5 + x \cdot \left(x \cdot \left(-0.125 + \left(x \cdot x\right) \cdot -0.0625\right)\right)\right)}{x}\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (log
  (/
   (+ 2.0 (* (* x x) (+ -0.5 (* x (* x (+ -0.125 (* (* x x) -0.0625)))))))
   x)))

C

double code(double x) {
	return log(((2.0 + ((x * x) * (-0.5 + (x * (x * (-0.125 + ((x * x) * -0.0625))))))) / x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = log(((2.0d0 + ((x * x) * ((-0.5d0) + (x * (x * ((-0.125d0) + ((x * x) * (-0.0625d0)))))))) / x))
end function

Java

public static double code(double x) {
	return Math.log(((2.0 + ((x * x) * (-0.5 + (x * (x * (-0.125 + ((x * x) * -0.0625))))))) / x));
}

Python

def code(x):
	return math.log(((2.0 + ((x * x) * (-0.5 + (x * (x * (-0.125 + ((x * x) * -0.0625))))))) / x))

Julia

function code(x)
	return log(Float64(Float64(2.0 + Float64(Float64(x * x) * Float64(-0.5 + Float64(x * Float64(x * Float64(-0.125 + Float64(Float64(x * x) * -0.0625))))))) / x))
end

MATLAB

function tmp = code(x)
	tmp = log(((2.0 + ((x * x) * (-0.5 + (x * (x * (-0.125 + ((x * x) * -0.0625))))))) / x));
end

Wolfram

code[x_] := N[Log[N[(N[(2.0 + N[(N[(x * x), $MachinePrecision] * N[(-0.5 + N[(x * N[(x * N[(-0.125 + N[(N[(x * x), $MachinePrecision] * -0.0625), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \mathsf{log.f64}\left(\color{blue}{\left(\frac{2 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{-1}{16} \cdot {x}^{2} - \frac{1}{8}\right) - \frac{1}{2}\right)}{x}\right)}\right) \]
4. Step-by-step derivation

   1. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\left(2 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{-1}{16} \cdot {x}^{2} - \frac{1}{8}\right) - \frac{1}{2}\right)\right), x\right)\right) \]

5. Simplified 100.0%

   \[\leadsto \log \color{blue}{\left(\frac{2 + \left(x \cdot x\right) \cdot \left(-0.5 + x \cdot \left(x \cdot \left(-0.125 + \left(x \cdot x\right) \cdot -0.0625\right)\right)\right)}{x}\right)} \]
6. Add Preprocessing

Alternative 2: 99.7% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \log \left(\frac{2 + x \cdot \left(x \cdot \left(-0.5 + x \cdot \left(x \cdot -0.125\right)\right)\right)}{x}\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (log (/ (+ 2.0 (* x (* x (+ -0.5 (* x (* x -0.125)))))) x)))

C

double code(double x) {
	return log(((2.0 + (x * (x * (-0.5 + (x * (x * -0.125)))))) / x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = log(((2.0d0 + (x * (x * ((-0.5d0) + (x * (x * (-0.125d0))))))) / x))
end function

Java

public static double code(double x) {
	return Math.log(((2.0 + (x * (x * (-0.5 + (x * (x * -0.125)))))) / x));
}

Python

def code(x):
	return math.log(((2.0 + (x * (x * (-0.5 + (x * (x * -0.125)))))) / x))

Julia

function code(x)
	return log(Float64(Float64(2.0 + Float64(x * Float64(x * Float64(-0.5 + Float64(x * Float64(x * -0.125)))))) / x))
end

MATLAB

function tmp = code(x)
	tmp = log(((2.0 + (x * (x * (-0.5 + (x * (x * -0.125)))))) / x));
end

Wolfram

code[x_] := N[Log[N[(N[(2.0 + N[(x * N[(x * N[(-0.5 + N[(x * N[(x * -0.125), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \mathsf{log.f64}\left(\color{blue}{\left(\frac{2 + {x}^{2} \cdot \left(\frac{-1}{8} \cdot {x}^{2} - \frac{1}{2}\right)}{x}\right)}\right) \]
4. Step-by-step derivation

   1. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\left(2 + {x}^{2} \cdot \left(\frac{-1}{8} \cdot {x}^{2} - \frac{1}{2}\right)\right), x\right)\right) \]

5. Simplified 99.9%

   \[\leadsto \log \color{blue}{\left(\frac{2 + x \cdot \left(x \cdot \left(-0.5 + x \cdot \left(x \cdot -0.125\right)\right)\right)}{x}\right)} \]
6. Add Preprocessing

Alternative 3: 99.5% accurate, 2.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 (log (+ (/ 2.0 x) (* x -0.5))))

C

double code(double x) {
	return log(((2.0 / x) + (x * -0.5)));
}

Fortran

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

Java

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

Python

def code(x):
	return math.log(((2.0 / x) + (x * -0.5)))

Julia

function code(x)
	return log(Float64(Float64(2.0 / x) + Float64(x * -0.5)))
end

MATLAB

function tmp = code(x)
	tmp = log(((2.0 / x) + (x * -0.5)));
end

Wolfram

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

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \mathsf{log.f64}\left(\color{blue}{\left(\frac{2 + \frac{-1}{2} \cdot {x}^{2}}{x}\right)}\right) \]
4. Step-by-step derivation

   1. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\left(2 + \frac{-1}{2} \cdot {x}^{2}\right), x\right)\right) \]

   2. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\mathsf{+.f64}\left(2, \left(\frac{-1}{2} \cdot {x}^{2}\right)\right), x\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\mathsf{+.f64}\left(2, \mathsf{*.f64}\left(\frac{-1}{2}, \left({x}^{2}\right)\right)\right), x\right)\right) \]

   4. unpow2 N/A

      \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\mathsf{+.f64}\left(2, \mathsf{*.f64}\left(\frac{-1}{2}, \left(x \cdot x\right)\right)\right), x\right)\right) \]

   5. *-lowering-*.f64 99.6%

      \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\mathsf{+.f64}\left(2, \mathsf{*.f64}\left(\frac{-1}{2}, \mathsf{*.f64}\left(x, x\right)\right)\right), x\right)\right) \]

5. Simplified 99.6%

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

6. Taylor expanded in x around inf

   \[\leadsto \mathsf{log.f64}\left(\color{blue}{\left(x \cdot \left(2 \cdot \frac{1}{{x}^{2}} - \frac{1}{2}\right)\right)}\right) \]
7. Step-by-step derivation

   1. sub-neg N/A

      \[\leadsto \mathsf{log.f64}\left(\left(x \cdot \left(2 \cdot \frac{1}{{x}^{2}} + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)\right)\right)\right) \]

   2. metadata-eval N/A

      \[\leadsto \mathsf{log.f64}\left(\left(x \cdot \left(2 \cdot \frac{1}{{x}^{2}} + \frac{-1}{2}\right)\right)\right) \]

   3. +-commutative N/A

      \[\leadsto \mathsf{log.f64}\left(\left(x \cdot \left(\frac{-1}{2} + 2 \cdot \frac{1}{{x}^{2}}\right)\right)\right) \]

   4. distribute-rgt-in N/A

      \[\leadsto \mathsf{log.f64}\left(\left(\frac{-1}{2} \cdot x + \left(2 \cdot \frac{1}{{x}^{2}}\right) \cdot x\right)\right) \]

   5. cancel-sign-sub N/A

      \[\leadsto \mathsf{log.f64}\left(\left(\frac{-1}{2} \cdot x - \left(\mathsf{neg}\left(2 \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x\right)\right) \]

   6. *-commutative N/A

      \[\leadsto \mathsf{log.f64}\left(\left(\frac{-1}{2} \cdot x - x \cdot \left(\mathsf{neg}\left(2 \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right) \]

   7. cancel-sign-sub-inv N/A

      \[\leadsto \mathsf{log.f64}\left(\left(\frac{-1}{2} \cdot x + \left(\mathsf{neg}\left(x\right)\right) \cdot \left(\mathsf{neg}\left(2 \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right) \]

   8. mul-1-neg N/A

      \[\leadsto \mathsf{log.f64}\left(\left(\frac{-1}{2} \cdot x + \left(-1 \cdot x\right) \cdot \left(\mathsf{neg}\left(2 \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right) \]

   9. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\left(\frac{-1}{2} \cdot x\right), \left(\left(-1 \cdot x\right) \cdot \left(\mathsf{neg}\left(2 \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right)\right) \]

   10. *-commutative N/A

       \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\left(x \cdot \frac{-1}{2}\right), \left(\left(-1 \cdot x\right) \cdot \left(\mathsf{neg}\left(2 \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right)\right) \]

   11. *-lowering-*.f64 N/A

       \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \frac{-1}{2}\right), \left(\left(-1 \cdot x\right) \cdot \left(\mathsf{neg}\left(2 \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right)\right) \]

   12. associate-*l* N/A

       \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \frac{-1}{2}\right), \left(-1 \cdot \left(x \cdot \left(\mathsf{neg}\left(2 \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right)\right)\right) \]

   13. neg-mul-1 N/A

       \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \frac{-1}{2}\right), \left(\mathsf{neg}\left(x \cdot \left(\mathsf{neg}\left(2 \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right)\right)\right) \]

   14. *-commutative N/A

       \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \frac{-1}{2}\right), \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(2 \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x\right)\right)\right)\right) \]

   15. distribute-lft-neg-in N/A

       \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \frac{-1}{2}\right), \left(\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(2 \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right) \cdot x\right)\right)\right) \]

   16. remove-double-neg N/A

       \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \frac{-1}{2}\right), \left(\left(2 \cdot \frac{1}{{x}^{2}}\right) \cdot x\right)\right)\right) \]

   17. associate-*l* N/A

       \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \frac{-1}{2}\right), \left(2 \cdot \left(\frac{1}{{x}^{2}} \cdot x\right)\right)\right)\right) \]

   18. unpow2 N/A

       \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \frac{-1}{2}\right), \left(2 \cdot \left(\frac{1}{x \cdot x} \cdot x\right)\right)\right)\right) \]

   19. associate-/r* N/A

       \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \frac{-1}{2}\right), \left(2 \cdot \left(\frac{\frac{1}{x}}{x} \cdot x\right)\right)\right)\right) \]

   20. associate-*l/ N/A

       \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{*.f64}\left(x, \frac{-1}{2}\right), \left(2 \cdot \frac{\frac{1}{x} \cdot x}{x}\right)\right)\right) \]

8. Simplified 99.6%

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

   1. log-lowering-log.f64 N/A

      \[\leadsto \mathsf{log.f64}\left(\left(x \cdot \frac{-1}{2} + 2 \cdot \frac{1}{x}\right)\right) \]

   2. +-commutative N/A

      \[\leadsto \mathsf{log.f64}\left(\left(2 \cdot \frac{1}{x} + x \cdot \frac{-1}{2}\right)\right) \]

   3. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\left(2 \cdot \frac{1}{x}\right), \left(x \cdot \frac{-1}{2}\right)\right)\right) \]

   4. un-div-inv N/A

      \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\left(\frac{2}{x}\right), \left(x \cdot \frac{-1}{2}\right)\right)\right) \]

   5. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{/.f64}\left(2, x\right), \left(x \cdot \frac{-1}{2}\right)\right)\right) \]

   6. *-lowering-*.f64 99.6%

      \[\leadsto \mathsf{log.f64}\left(\mathsf{+.f64}\left(\mathsf{/.f64}\left(2, x\right), \mathsf{*.f64}\left(x, \frac{-1}{2}\right)\right)\right) \]

10. Applied egg-rr 99.6%

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

Alternative 4: 99.1% 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 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \mathsf{log.f64}\left(\color{blue}{\left(\frac{2}{x}\right)}\right) \]
4. Step-by-step derivation

   1. /-lowering-/.f64 99.0%

      \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(2, x\right)\right) \]

5. Simplified 99.0%

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

Reproduce

herbie shell --seed 2024185 
(FPCore (x)
  :name "Hyperbolic arc-(co)secant"
  :precision binary64
  (log (+ (/ 1.0 x) (/ (sqrt (- 1.0 (* x x))) x))))