Hyperbolic arc-(co)secant

Percentage Accurate: 100.0% → 100.0%

Time: 6.1s

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, 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 100.0%

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

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

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

   2. div-inv N/A

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

   3. distribute-rgt1-in N/A

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

   4. un-div-inv N/A

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

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

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

   6. +-commutative N/A

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

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

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

   8. rem-square-sqrt N/A

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

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

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

   10. rem-square-sqrt N/A

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

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

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

   12. *-lowering-*.f64 100.0%

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

4. Applied egg-rr 100.0%

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

Alternative 2: 99.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -0.5 + \left(x \cdot x\right) \cdot \left(-0.125 + x \cdot \left(x \cdot -0.0625\right)\right)\\ \log \left(\frac{4 - \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(t\_0 \cdot t\_0\right)}{x \cdot \left(2 - x \cdot \left(x \cdot t\_0\right)\right)}\right) \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (+ -0.5 (* (* x x) (+ -0.125 (* x (* x -0.0625)))))))
   (log
    (/
     (- 4.0 (* (* (* x x) (* x x)) (* t_0 t_0)))
     (* x (- 2.0 (* x (* x t_0))))))))

C

double code(double x) {
	double t_0 = -0.5 + ((x * x) * (-0.125 + (x * (x * -0.0625))));
	return log(((4.0 - (((x * x) * (x * x)) * (t_0 * t_0))) / (x * (2.0 - (x * (x * t_0))))));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    t_0 = (-0.5d0) + ((x * x) * ((-0.125d0) + (x * (x * (-0.0625d0)))))
    code = log(((4.0d0 - (((x * x) * (x * x)) * (t_0 * t_0))) / (x * (2.0d0 - (x * (x * t_0))))))
end function

Java

public static double code(double x) {
	double t_0 = -0.5 + ((x * x) * (-0.125 + (x * (x * -0.0625))));
	return Math.log(((4.0 - (((x * x) * (x * x)) * (t_0 * t_0))) / (x * (2.0 - (x * (x * t_0))))));
}

Python

def code(x):
	t_0 = -0.5 + ((x * x) * (-0.125 + (x * (x * -0.0625))))
	return math.log(((4.0 - (((x * x) * (x * x)) * (t_0 * t_0))) / (x * (2.0 - (x * (x * t_0))))))

Julia

function code(x)
	t_0 = Float64(-0.5 + Float64(Float64(x * x) * Float64(-0.125 + Float64(x * Float64(x * -0.0625)))))
	return log(Float64(Float64(4.0 - Float64(Float64(Float64(x * x) * Float64(x * x)) * Float64(t_0 * t_0))) / Float64(x * Float64(2.0 - Float64(x * Float64(x * t_0))))))
end

MATLAB

function tmp = code(x)
	t_0 = -0.5 + ((x * x) * (-0.125 + (x * (x * -0.0625))));
	tmp = log(((4.0 - (((x * x) * (x * x)) * (t_0 * t_0))) / (x * (2.0 - (x * (x * t_0))))));
end

Wolfram

code[x_] := Block[{t$95$0 = N[(-0.5 + N[(N[(x * x), $MachinePrecision] * N[(-0.125 + N[(x * N[(x * -0.0625), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[Log[N[(N[(4.0 - N[(N[(N[(x * x), $MachinePrecision] * N[(x * x), $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x * N[(2.0 - N[(x * N[(x * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $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

   \[\leadsto \mathsf{log.f64}\left(\color{blue}{\left(\frac{2 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{-1}{16} \cdot {x}^{2} - \frac{1}{8}\right) - \frac{1}{2}\right)}{x}\right)}\right) \]
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 99.8%

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

   1. flip-+ N/A

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

   2. associate-/l/ N/A

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

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

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

7. Applied egg-rr 99.8%

   \[\leadsto \log \color{blue}{\left(\frac{4 - \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(\left(-0.5 + \left(x \cdot x\right) \cdot \left(-0.125 + x \cdot \left(x \cdot -0.0625\right)\right)\right) \cdot \left(-0.5 + \left(x \cdot x\right) \cdot \left(-0.125 + x \cdot \left(x \cdot -0.0625\right)\right)\right)\right)}{x \cdot \left(2 - x \cdot \left(x \cdot \left(-0.5 + \left(x \cdot x\right) \cdot \left(-0.125 + x \cdot \left(x \cdot -0.0625\right)\right)\right)\right)\right)}\right)} \]
8. Add Preprocessing

Alternative 3: 99.8% accurate, 1.7× speedup

Math

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

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(Float64(x * 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[(N[(x * x), $MachinePrecision] * N[(-0.125 + N[(N[(x * x), $MachinePrecision] * -0.0625), $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 99.8%

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

Alternative 4: 99.7% accurate, 1.8× speedup

Math

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

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(Float64(x * 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[(N[(x * x), $MachinePrecision] * N[(-0.5 + N[(x * N[(x * -0.125), $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

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

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

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

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

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

   4. unpow2 N/A

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

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

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

   6. sub-neg N/A

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

   7. metadata-eval N/A

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

   8. +-commutative N/A

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

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

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

   10. unpow2 N/A

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

   11. associate-*r* N/A

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

   12. *-commutative N/A

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

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

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

   14. *-commutative N/A

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

   15. *-lowering-*.f64 99.8%

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

5. Simplified 99.8%

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

Alternative 5: 99.5% accurate, 1.9× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

   \[\leadsto \mathsf{log.f64}\left(\color{blue}{\left(\frac{2 + \frac{-1}{2} \cdot {x}^{2}}{x}\right)}\right) \]
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. *-commutative N/A

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

   4. unpow2 N/A

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

   5. associate-*l* N/A

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

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

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

   7. *-lowering-*.f64 99.7%

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

5. Simplified 99.7%

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

Alternative 6: 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 2024138 
(FPCore (x)
  :name "Hyperbolic arc-(co)secant"
  :precision binary64
  (log (+ (/ 1.0 x) (/ (sqrt (- 1.0 (* x x))) x))))