Hyperbolic arcsine

Percentage Accurate: 17.6% → 99.5%

Time: 9.6s

Alternatives: 7

Speedup: 207.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 17.6% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.5% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.9:\\ \;\;\;\;0 - \log \left(x \cdot -2 + \frac{-0.5 + \frac{0.125}{x \cdot x}}{x}\right)\\ \mathbf{elif}\;x \leq 0.96:\\ \;\;\;\;x + \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -0.9)
   (- 0.0 (log (+ (* x -2.0) (/ (+ -0.5 (/ 0.125 (* x x))) x))))
   (if (<= x 0.96)
     (+ x (* (* x (* x x)) -0.16666666666666666))
     (log (+ (* x 2.0) (/ 0.5 x))))))

C

double code(double x) {
	double tmp;
	if (x <= -0.9) {
		tmp = 0.0 - log(((x * -2.0) + ((-0.5 + (0.125 / (x * x))) / x)));
	} else if (x <= 0.96) {
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	} else {
		tmp = log(((x * 2.0) + (0.5 / x)));
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-0.9d0)) then
        tmp = 0.0d0 - log(((x * (-2.0d0)) + (((-0.5d0) + (0.125d0 / (x * x))) / x)))
    else if (x <= 0.96d0) then
        tmp = x + ((x * (x * x)) * (-0.16666666666666666d0))
    else
        tmp = log(((x * 2.0d0) + (0.5d0 / x)))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -0.9) {
		tmp = 0.0 - Math.log(((x * -2.0) + ((-0.5 + (0.125 / (x * x))) / x)));
	} else if (x <= 0.96) {
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	} else {
		tmp = Math.log(((x * 2.0) + (0.5 / x)));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -0.9:
		tmp = 0.0 - math.log(((x * -2.0) + ((-0.5 + (0.125 / (x * x))) / x)))
	elif x <= 0.96:
		tmp = x + ((x * (x * x)) * -0.16666666666666666)
	else:
		tmp = math.log(((x * 2.0) + (0.5 / x)))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -0.9)
		tmp = Float64(0.0 - log(Float64(Float64(x * -2.0) + Float64(Float64(-0.5 + Float64(0.125 / Float64(x * x))) / x))));
	elseif (x <= 0.96)
		tmp = Float64(x + Float64(Float64(x * Float64(x * x)) * -0.16666666666666666));
	else
		tmp = log(Float64(Float64(x * 2.0) + Float64(0.5 / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -0.9)
		tmp = 0.0 - log(((x * -2.0) + ((-0.5 + (0.125 / (x * x))) / x)));
	elseif (x <= 0.96)
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	else
		tmp = log(((x * 2.0) + (0.5 / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -0.9], N[(0.0 - N[Log[N[(N[(x * -2.0), $MachinePrecision] + N[(N[(-0.5 + N[(0.125 / N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 0.96], N[(x + N[(N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] * -0.16666666666666666), $MachinePrecision]), $MachinePrecision], N[Log[N[(N[(x * 2.0), $MachinePrecision] + N[(0.5 / x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.900000000000000022

   1. Initial program 3.9%

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

   3. Taylor expanded in x around -inf

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

      1. associate-*r/ N/A

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

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

         \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\left(-1 \cdot \left(\left(\frac{1}{2} + \frac{\frac{1}{16}}{{x}^{4}}\right) - \frac{1}{8} \cdot \frac{1}{{x}^{2}}\right)\right), x\right)\right) \]

   5. Simplified 99.3%

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

      1. clear-num N/A

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

      2. log-rec N/A

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

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

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

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

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

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

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

      6. associate-+r+ N/A

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

      7. +-commutative N/A

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

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

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

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

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

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

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

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

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

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

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

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

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

   7. Applied egg-rr 99.4%

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

   8. Taylor expanded in x around inf

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

   10. Simplified 99.4%

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

   if -0.900000000000000022 < x < 0.95999999999999996

   1. Initial program 8.1%

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

   3. Taylor expanded in x around 0

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

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

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(1 + \frac{-1}{6} \cdot {x}^{2}\right)}\right) \]

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

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \color{blue}{\left(\frac{-1}{6} \cdot {x}^{2}\right)}\right)\right) \]

      3. *-commutative N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left({x}^{2} \cdot \color{blue}{\frac{-1}{6}}\right)\right)\right) \]

      4. unpow2 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left(\left(x \cdot x\right) \cdot \frac{-1}{6}\right)\right)\right) \]

      5. associate-*l* N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left(x \cdot \color{blue}{\left(x \cdot \frac{-1}{6}\right)}\right)\right)\right) \]

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

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, \color{blue}{\left(x \cdot \frac{-1}{6}\right)}\right)\right)\right) \]

      7. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, \color{blue}{\frac{-1}{6}}\right)\right)\right)\right) \]

   5. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(1 + x \cdot \left(x \cdot -0.16666666666666666\right)\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot \left(x \cdot \left(x \cdot \frac{-1}{6}\right) + \color{blue}{1}\right) \]

      2. distribute-rgt-in N/A

         \[\leadsto \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x + \color{blue}{1 \cdot x} \]

      3. *-lft-identity N/A

         \[\leadsto \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x + x \]

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

         \[\leadsto \mathsf{+.f64}\left(\left(\left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x\right), \color{blue}{x}\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right)\right), x\right) \]

      6. associate-*r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(\left(x \cdot x\right) \cdot \frac{-1}{6}\right)\right), x\right) \]

      7. associate-*r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\left(x \cdot \left(x \cdot x\right)\right) \cdot \frac{-1}{6}\right), x\right) \]

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

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\left(x \cdot \left(x \cdot x\right)\right), \frac{-1}{6}\right), x\right) \]

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

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\mathsf{*.f64}\left(x, \left(x \cdot x\right)\right), \frac{-1}{6}\right), x\right) \]

      10. *-lowering-*.f64 100.0%

          \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, x\right)\right), \frac{-1}{6}\right), x\right) \]

   7. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666 + x} \]

   if 0.95999999999999996 < x

   1. Initial program 53.1%

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

   3. Taylor expanded in x around inf

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

      1. distribute-rgt-in N/A

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

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

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

      3. *-commutative N/A

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

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

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

      5. associate-*r/ N/A

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

      6. metadata-eval N/A

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

      7. associate-*l/ N/A

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

      8. unpow2 N/A

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

      9. associate-/r* N/A

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

      10. associate-/l* N/A

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

      11. *-inverses N/A

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

      12. metadata-eval N/A

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

      13. /-lowering-/.f64 100.0%

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

   5. Simplified 100.0%

      \[\leadsto \log \color{blue}{\left(x \cdot 2 + \frac{0.5}{x}\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.9:\\ \;\;\;\;0 - \log \left(x \cdot -2 + \frac{-0.5 + \frac{0.125}{x \cdot x}}{x}\right)\\ \mathbf{elif}\;x \leq 0.96:\\ \;\;\;\;x + \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5}{x}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 99.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.95:\\ \;\;\;\;0 - \log \left(x \cdot -2 + \frac{-0.5}{x}\right)\\ \mathbf{elif}\;x \leq 0.96:\\ \;\;\;\;x + \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -0.95)
   (- 0.0 (log (+ (* x -2.0) (/ -0.5 x))))
   (if (<= x 0.96)
     (+ x (* (* x (* x x)) -0.16666666666666666))
     (log (+ (* x 2.0) (/ 0.5 x))))))

C

double code(double x) {
	double tmp;
	if (x <= -0.95) {
		tmp = 0.0 - log(((x * -2.0) + (-0.5 / x)));
	} else if (x <= 0.96) {
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	} else {
		tmp = log(((x * 2.0) + (0.5 / x)));
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-0.95d0)) then
        tmp = 0.0d0 - log(((x * (-2.0d0)) + ((-0.5d0) / x)))
    else if (x <= 0.96d0) then
        tmp = x + ((x * (x * x)) * (-0.16666666666666666d0))
    else
        tmp = log(((x * 2.0d0) + (0.5d0 / x)))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -0.95) {
		tmp = 0.0 - Math.log(((x * -2.0) + (-0.5 / x)));
	} else if (x <= 0.96) {
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	} else {
		tmp = Math.log(((x * 2.0) + (0.5 / x)));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -0.95:
		tmp = 0.0 - math.log(((x * -2.0) + (-0.5 / x)))
	elif x <= 0.96:
		tmp = x + ((x * (x * x)) * -0.16666666666666666)
	else:
		tmp = math.log(((x * 2.0) + (0.5 / x)))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -0.95)
		tmp = Float64(0.0 - log(Float64(Float64(x * -2.0) + Float64(-0.5 / x))));
	elseif (x <= 0.96)
		tmp = Float64(x + Float64(Float64(x * Float64(x * x)) * -0.16666666666666666));
	else
		tmp = log(Float64(Float64(x * 2.0) + Float64(0.5 / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -0.95)
		tmp = 0.0 - log(((x * -2.0) + (-0.5 / x)));
	elseif (x <= 0.96)
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	else
		tmp = log(((x * 2.0) + (0.5 / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -0.95], N[(0.0 - N[Log[N[(N[(x * -2.0), $MachinePrecision] + N[(-0.5 / x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 0.96], N[(x + N[(N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] * -0.16666666666666666), $MachinePrecision]), $MachinePrecision], N[Log[N[(N[(x * 2.0), $MachinePrecision] + N[(0.5 / x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.94999999999999996

   1. Initial program 3.9%

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

   3. Taylor expanded in x around -inf

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

      1. associate-*r/ N/A

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

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

         \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\left(-1 \cdot \left(\left(\frac{1}{2} + \frac{\frac{1}{16}}{{x}^{4}}\right) - \frac{1}{8} \cdot \frac{1}{{x}^{2}}\right)\right), x\right)\right) \]

   5. Simplified 99.3%

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

      1. clear-num N/A

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

      2. log-rec N/A

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

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

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

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

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

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

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

      6. associate-+r+ N/A

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

      7. +-commutative N/A

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

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

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

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

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

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

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

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

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

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

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

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

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

   7. Applied egg-rr 99.4%

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

   8. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

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

      2. distribute-lft-in N/A

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

      3. distribute-neg-in N/A

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

      4. *-commutative N/A

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

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

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

      6. distribute-rgt-neg-in N/A

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

      7. metadata-eval N/A

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

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

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

      9. *-commutative N/A

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

      10. associate-*r/ N/A

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

      11. metadata-eval N/A

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

      12. associate-*r/ N/A

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

      13. unpow2 N/A

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

      14. associate-/r* N/A

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

      15. distribute-neg-frac N/A

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

      16. *-rgt-identity N/A

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

      17. times-frac N/A

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

      18. *-inverses N/A

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

      19. metadata-eval N/A

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

      20. metadata-eval N/A

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

   10. Simplified 99.2%

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

   if -0.94999999999999996 < x < 0.95999999999999996

   1. Initial program 8.1%

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

   3. Taylor expanded in x around 0

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

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

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(1 + \frac{-1}{6} \cdot {x}^{2}\right)}\right) \]

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

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \color{blue}{\left(\frac{-1}{6} \cdot {x}^{2}\right)}\right)\right) \]

      3. *-commutative N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left({x}^{2} \cdot \color{blue}{\frac{-1}{6}}\right)\right)\right) \]

      4. unpow2 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left(\left(x \cdot x\right) \cdot \frac{-1}{6}\right)\right)\right) \]

      5. associate-*l* N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left(x \cdot \color{blue}{\left(x \cdot \frac{-1}{6}\right)}\right)\right)\right) \]

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

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, \color{blue}{\left(x \cdot \frac{-1}{6}\right)}\right)\right)\right) \]

      7. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, \color{blue}{\frac{-1}{6}}\right)\right)\right)\right) \]

   5. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(1 + x \cdot \left(x \cdot -0.16666666666666666\right)\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot \left(x \cdot \left(x \cdot \frac{-1}{6}\right) + \color{blue}{1}\right) \]

      2. distribute-rgt-in N/A

         \[\leadsto \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x + \color{blue}{1 \cdot x} \]

      3. *-lft-identity N/A

         \[\leadsto \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x + x \]

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

         \[\leadsto \mathsf{+.f64}\left(\left(\left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x\right), \color{blue}{x}\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right)\right), x\right) \]

      6. associate-*r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(\left(x \cdot x\right) \cdot \frac{-1}{6}\right)\right), x\right) \]

      7. associate-*r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\left(x \cdot \left(x \cdot x\right)\right) \cdot \frac{-1}{6}\right), x\right) \]

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

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\left(x \cdot \left(x \cdot x\right)\right), \frac{-1}{6}\right), x\right) \]

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

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\mathsf{*.f64}\left(x, \left(x \cdot x\right)\right), \frac{-1}{6}\right), x\right) \]

      10. *-lowering-*.f64 100.0%

          \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, x\right)\right), \frac{-1}{6}\right), x\right) \]

   7. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666 + x} \]

   if 0.95999999999999996 < x

   1. Initial program 53.1%

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

   3. Taylor expanded in x around inf

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

      1. distribute-rgt-in N/A

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

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

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

      3. *-commutative N/A

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

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

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

      5. associate-*r/ N/A

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

      6. metadata-eval N/A

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

      7. associate-*l/ N/A

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

      8. unpow2 N/A

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

      9. associate-/r* N/A

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

      10. associate-/l* N/A

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

      11. *-inverses N/A

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

      12. metadata-eval N/A

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

      13. /-lowering-/.f64 100.0%

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

   5. Simplified 100.0%

      \[\leadsto \log \color{blue}{\left(x \cdot 2 + \frac{0.5}{x}\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.95:\\ \;\;\;\;0 - \log \left(x \cdot -2 + \frac{-0.5}{x}\right)\\ \mathbf{elif}\;x \leq 0.96:\\ \;\;\;\;x + \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5}{x}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.3:\\ \;\;\;\;0 - \log \left(\frac{x}{-0.5}\right)\\ \mathbf{elif}\;x \leq 0.96:\\ \;\;\;\;x + \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.3)
   (- 0.0 (log (/ x -0.5)))
   (if (<= x 0.96)
     (+ x (* (* x (* x x)) -0.16666666666666666))
     (log (+ (* x 2.0) (/ 0.5 x))))))

C

double code(double x) {
	double tmp;
	if (x <= -1.3) {
		tmp = 0.0 - log((x / -0.5));
	} else if (x <= 0.96) {
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	} else {
		tmp = log(((x * 2.0) + (0.5 / x)));
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-1.3d0)) then
        tmp = 0.0d0 - log((x / (-0.5d0)))
    else if (x <= 0.96d0) then
        tmp = x + ((x * (x * x)) * (-0.16666666666666666d0))
    else
        tmp = log(((x * 2.0d0) + (0.5d0 / x)))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -1.3) {
		tmp = 0.0 - Math.log((x / -0.5));
	} else if (x <= 0.96) {
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	} else {
		tmp = Math.log(((x * 2.0) + (0.5 / x)));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -1.3:
		tmp = 0.0 - math.log((x / -0.5))
	elif x <= 0.96:
		tmp = x + ((x * (x * x)) * -0.16666666666666666)
	else:
		tmp = math.log(((x * 2.0) + (0.5 / x)))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -1.3)
		tmp = Float64(0.0 - log(Float64(x / -0.5)));
	elseif (x <= 0.96)
		tmp = Float64(x + Float64(Float64(x * Float64(x * x)) * -0.16666666666666666));
	else
		tmp = log(Float64(Float64(x * 2.0) + Float64(0.5 / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -1.3)
		tmp = 0.0 - log((x / -0.5));
	elseif (x <= 0.96)
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	else
		tmp = log(((x * 2.0) + (0.5 / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -1.3], N[(0.0 - N[Log[N[(x / -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 0.96], N[(x + N[(N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] * -0.16666666666666666), $MachinePrecision]), $MachinePrecision], N[Log[N[(N[(x * 2.0), $MachinePrecision] + N[(0.5 / x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.30000000000000004

   1. Initial program 3.9%

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

   3. Taylor expanded in x around -inf

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

      1. /-lowering-/.f64 99.0%

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

   5. Simplified 99.0%

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

      1. clear-num N/A

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

      2. log-rec N/A

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

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

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

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

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

      5. /-lowering-/.f64 99.0%

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

   7. Applied egg-rr 99.0%

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

   if -1.30000000000000004 < x < 0.95999999999999996

   1. Initial program 8.1%

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

   3. Taylor expanded in x around 0

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

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

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(1 + \frac{-1}{6} \cdot {x}^{2}\right)}\right) \]

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

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \color{blue}{\left(\frac{-1}{6} \cdot {x}^{2}\right)}\right)\right) \]

      3. *-commutative N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left({x}^{2} \cdot \color{blue}{\frac{-1}{6}}\right)\right)\right) \]

      4. unpow2 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left(\left(x \cdot x\right) \cdot \frac{-1}{6}\right)\right)\right) \]

      5. associate-*l* N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left(x \cdot \color{blue}{\left(x \cdot \frac{-1}{6}\right)}\right)\right)\right) \]

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

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, \color{blue}{\left(x \cdot \frac{-1}{6}\right)}\right)\right)\right) \]

      7. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, \color{blue}{\frac{-1}{6}}\right)\right)\right)\right) \]

   5. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(1 + x \cdot \left(x \cdot -0.16666666666666666\right)\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot \left(x \cdot \left(x \cdot \frac{-1}{6}\right) + \color{blue}{1}\right) \]

      2. distribute-rgt-in N/A

         \[\leadsto \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x + \color{blue}{1 \cdot x} \]

      3. *-lft-identity N/A

         \[\leadsto \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x + x \]

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

         \[\leadsto \mathsf{+.f64}\left(\left(\left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x\right), \color{blue}{x}\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right)\right), x\right) \]

      6. associate-*r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(\left(x \cdot x\right) \cdot \frac{-1}{6}\right)\right), x\right) \]

      7. associate-*r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\left(x \cdot \left(x \cdot x\right)\right) \cdot \frac{-1}{6}\right), x\right) \]

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

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\left(x \cdot \left(x \cdot x\right)\right), \frac{-1}{6}\right), x\right) \]

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

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\mathsf{*.f64}\left(x, \left(x \cdot x\right)\right), \frac{-1}{6}\right), x\right) \]

      10. *-lowering-*.f64 100.0%

          \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, x\right)\right), \frac{-1}{6}\right), x\right) \]

   7. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666 + x} \]

   if 0.95999999999999996 < x

   1. Initial program 53.1%

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

   3. Taylor expanded in x around inf

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

      1. distribute-rgt-in N/A

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

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

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

      3. *-commutative N/A

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

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

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

      5. associate-*r/ N/A

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

      6. metadata-eval N/A

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

      7. associate-*l/ N/A

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

      8. unpow2 N/A

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

      9. associate-/r* N/A

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

      10. associate-/l* N/A

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

      11. *-inverses N/A

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

      12. metadata-eval N/A

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

      13. /-lowering-/.f64 100.0%

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

   5. Simplified 100.0%

      \[\leadsto \log \color{blue}{\left(x \cdot 2 + \frac{0.5}{x}\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.3:\\ \;\;\;\;0 - \log \left(\frac{x}{-0.5}\right)\\ \mathbf{elif}\;x \leq 0.96:\\ \;\;\;\;x + \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5}{x}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.2% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.3:\\ \;\;\;\;0 - \log \left(\frac{x}{-0.5}\right)\\ \mathbf{elif}\;x \leq 1.25:\\ \;\;\;\;x + \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.3)
   (- 0.0 (log (/ x -0.5)))
   (if (<= x 1.25)
     (+ x (* (* x (* x x)) -0.16666666666666666))
     (log (+ x x)))))

C

double code(double x) {
	double tmp;
	if (x <= -1.3) {
		tmp = 0.0 - log((x / -0.5));
	} else if (x <= 1.25) {
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	} else {
		tmp = log((x + x));
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-1.3d0)) then
        tmp = 0.0d0 - log((x / (-0.5d0)))
    else if (x <= 1.25d0) then
        tmp = x + ((x * (x * x)) * (-0.16666666666666666d0))
    else
        tmp = log((x + x))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -1.3) {
		tmp = 0.0 - Math.log((x / -0.5));
	} else if (x <= 1.25) {
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	} else {
		tmp = Math.log((x + x));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -1.3:
		tmp = 0.0 - math.log((x / -0.5))
	elif x <= 1.25:
		tmp = x + ((x * (x * x)) * -0.16666666666666666)
	else:
		tmp = math.log((x + x))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -1.3)
		tmp = Float64(0.0 - log(Float64(x / -0.5)));
	elseif (x <= 1.25)
		tmp = Float64(x + Float64(Float64(x * Float64(x * x)) * -0.16666666666666666));
	else
		tmp = log(Float64(x + x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -1.3)
		tmp = 0.0 - log((x / -0.5));
	elseif (x <= 1.25)
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	else
		tmp = log((x + x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -1.3], N[(0.0 - N[Log[N[(x / -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.25], N[(x + N[(N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] * -0.16666666666666666), $MachinePrecision]), $MachinePrecision], N[Log[N[(x + x), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.30000000000000004

   1. Initial program 3.9%

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

   3. Taylor expanded in x around -inf

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

      1. /-lowering-/.f64 99.0%

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

   5. Simplified 99.0%

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

      1. clear-num N/A

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

      2. log-rec N/A

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

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

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

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

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

      5. /-lowering-/.f64 99.0%

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

   7. Applied egg-rr 99.0%

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

   if -1.30000000000000004 < x < 1.25

   1. Initial program 8.1%

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

   3. Taylor expanded in x around 0

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

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

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(1 + \frac{-1}{6} \cdot {x}^{2}\right)}\right) \]

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

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \color{blue}{\left(\frac{-1}{6} \cdot {x}^{2}\right)}\right)\right) \]

      3. *-commutative N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left({x}^{2} \cdot \color{blue}{\frac{-1}{6}}\right)\right)\right) \]

      4. unpow2 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left(\left(x \cdot x\right) \cdot \frac{-1}{6}\right)\right)\right) \]

      5. associate-*l* N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left(x \cdot \color{blue}{\left(x \cdot \frac{-1}{6}\right)}\right)\right)\right) \]

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

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, \color{blue}{\left(x \cdot \frac{-1}{6}\right)}\right)\right)\right) \]

      7. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, \color{blue}{\frac{-1}{6}}\right)\right)\right)\right) \]

   5. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(1 + x \cdot \left(x \cdot -0.16666666666666666\right)\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot \left(x \cdot \left(x \cdot \frac{-1}{6}\right) + \color{blue}{1}\right) \]

      2. distribute-rgt-in N/A

         \[\leadsto \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x + \color{blue}{1 \cdot x} \]

      3. *-lft-identity N/A

         \[\leadsto \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x + x \]

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

         \[\leadsto \mathsf{+.f64}\left(\left(\left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x\right), \color{blue}{x}\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right)\right), x\right) \]

      6. associate-*r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(\left(x \cdot x\right) \cdot \frac{-1}{6}\right)\right), x\right) \]

      7. associate-*r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\left(x \cdot \left(x \cdot x\right)\right) \cdot \frac{-1}{6}\right), x\right) \]

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

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\left(x \cdot \left(x \cdot x\right)\right), \frac{-1}{6}\right), x\right) \]

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

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\mathsf{*.f64}\left(x, \left(x \cdot x\right)\right), \frac{-1}{6}\right), x\right) \]

      10. *-lowering-*.f64 100.0%

          \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, x\right)\right), \frac{-1}{6}\right), x\right) \]

   7. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666 + x} \]

   if 1.25 < x

   1. Initial program 53.1%

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

   3. Taylor expanded in x around inf

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

   5. Simplified 99.7%

      \[\leadsto \log \left(x + \color{blue}{x}\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.3:\\ \;\;\;\;0 - \log \left(\frac{x}{-0.5}\right)\\ \mathbf{elif}\;x \leq 1.25:\\ \;\;\;\;x + \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 99.3% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.3:\\ \;\;\;\;\log \left(\frac{-0.5}{x}\right)\\ \mathbf{elif}\;x \leq 1.25:\\ \;\;\;\;x + \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.3)
   (log (/ -0.5 x))
   (if (<= x 1.25)
     (+ x (* (* x (* x x)) -0.16666666666666666))
     (log (+ x x)))))

C

double code(double x) {
	double tmp;
	if (x <= -1.3) {
		tmp = log((-0.5 / x));
	} else if (x <= 1.25) {
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	} else {
		tmp = log((x + x));
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-1.3d0)) then
        tmp = log(((-0.5d0) / x))
    else if (x <= 1.25d0) then
        tmp = x + ((x * (x * x)) * (-0.16666666666666666d0))
    else
        tmp = log((x + x))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -1.3) {
		tmp = Math.log((-0.5 / x));
	} else if (x <= 1.25) {
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	} else {
		tmp = Math.log((x + x));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -1.3:
		tmp = math.log((-0.5 / x))
	elif x <= 1.25:
		tmp = x + ((x * (x * x)) * -0.16666666666666666)
	else:
		tmp = math.log((x + x))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -1.3)
		tmp = log(Float64(-0.5 / x));
	elseif (x <= 1.25)
		tmp = Float64(x + Float64(Float64(x * Float64(x * x)) * -0.16666666666666666));
	else
		tmp = log(Float64(x + x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -1.3)
		tmp = log((-0.5 / x));
	elseif (x <= 1.25)
		tmp = x + ((x * (x * x)) * -0.16666666666666666);
	else
		tmp = log((x + x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -1.3], N[Log[N[(-0.5 / x), $MachinePrecision]], $MachinePrecision], If[LessEqual[x, 1.25], N[(x + N[(N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] * -0.16666666666666666), $MachinePrecision]), $MachinePrecision], N[Log[N[(x + x), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.30000000000000004

   1. Initial program 3.9%

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

   3. Taylor expanded in x around -inf

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

      1. /-lowering-/.f64 99.0%

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

   5. Simplified 99.0%

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

   if -1.30000000000000004 < x < 1.25

   1. Initial program 8.1%

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

   3. Taylor expanded in x around 0

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

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

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(1 + \frac{-1}{6} \cdot {x}^{2}\right)}\right) \]

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

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \color{blue}{\left(\frac{-1}{6} \cdot {x}^{2}\right)}\right)\right) \]

      3. *-commutative N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left({x}^{2} \cdot \color{blue}{\frac{-1}{6}}\right)\right)\right) \]

      4. unpow2 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left(\left(x \cdot x\right) \cdot \frac{-1}{6}\right)\right)\right) \]

      5. associate-*l* N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \left(x \cdot \color{blue}{\left(x \cdot \frac{-1}{6}\right)}\right)\right)\right) \]

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

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, \color{blue}{\left(x \cdot \frac{-1}{6}\right)}\right)\right)\right) \]

      7. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, \color{blue}{\frac{-1}{6}}\right)\right)\right)\right) \]

   5. Simplified 100.0%

      \[\leadsto \color{blue}{x \cdot \left(1 + x \cdot \left(x \cdot -0.16666666666666666\right)\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot \left(x \cdot \left(x \cdot \frac{-1}{6}\right) + \color{blue}{1}\right) \]

      2. distribute-rgt-in N/A

         \[\leadsto \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x + \color{blue}{1 \cdot x} \]

      3. *-lft-identity N/A

         \[\leadsto \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x + x \]

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

         \[\leadsto \mathsf{+.f64}\left(\left(\left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right) \cdot x\right), \color{blue}{x}\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right)\right), x\right) \]

      6. associate-*r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \left(\left(x \cdot x\right) \cdot \frac{-1}{6}\right)\right), x\right) \]

      7. associate-*r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\left(x \cdot \left(x \cdot x\right)\right) \cdot \frac{-1}{6}\right), x\right) \]

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

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\left(x \cdot \left(x \cdot x\right)\right), \frac{-1}{6}\right), x\right) \]

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

         \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\mathsf{*.f64}\left(x, \left(x \cdot x\right)\right), \frac{-1}{6}\right), x\right) \]

      10. *-lowering-*.f64 100.0%

          \[\leadsto \mathsf{+.f64}\left(\mathsf{*.f64}\left(\mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, x\right)\right), \frac{-1}{6}\right), x\right) \]

   7. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666 + x} \]

   if 1.25 < x

   1. Initial program 53.1%

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

   3. Taylor expanded in x around inf

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

   5. Simplified 99.7%

      \[\leadsto \log \left(x + \color{blue}{x}\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.3:\\ \;\;\;\;\log \left(\frac{-0.5}{x}\right)\\ \mathbf{elif}\;x \leq 1.25:\\ \;\;\;\;x + \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 74.4% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1.25:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (if (<= x 1.25) x (log (+ x x))))

C

double code(double x) {
	double tmp;
	if (x <= 1.25) {
		tmp = x;
	} else {
		tmp = log((x + x));
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= 1.25d0) then
        tmp = x
    else
        tmp = log((x + x))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= 1.25) {
		tmp = x;
	} else {
		tmp = Math.log((x + x));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 1.25:
		tmp = x
	else:
		tmp = math.log((x + x))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 1.25)
		tmp = x;
	else
		tmp = log(Float64(x + x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= 1.25)
		tmp = x;
	else
		tmp = log((x + x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 1.25], x, N[Log[N[(x + x), $MachinePrecision]], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.25

   1. Initial program 6.6%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x} \]
   4. Step-by-step derivation

   5. Simplified 65.1%

      \[\leadsto \color{blue}{x} \]

   if 1.25 < x

   1. Initial program 53.1%

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

   3. Taylor expanded in x around inf

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

   5. Simplified 99.7%

      \[\leadsto \log \left(x + \color{blue}{x}\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 51.9% accurate, 207.0× speedup

Math

\[\begin{array}{l} \\ x \end{array} \]

FPCore

(FPCore (x) :precision binary64 x)

C

double code(double x) {
	return x;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x
end function

Java

public static double code(double x) {
	return x;
}

Python

def code(x):
	return x

Julia

function code(x)
	return x
end

MATLAB

function tmp = code(x)
	tmp = x;
end

Wolfram

code[x_] := x

Derivation

1. Initial program 18.2%

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

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{x} \]
4. Step-by-step derivation

5. Simplified 50.2%

   \[\leadsto \color{blue}{x} \]
6. Add Preprocessing

Developer Target 1: 30.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \sqrt{x \cdot x + 1}\\ \mathbf{if}\;x < 0:\\ \;\;\;\;\log \left(\frac{-1}{x - t\_0}\right)\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + t\_0\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (sqrt (+ (* x x) 1.0))))
   (if (< x 0.0) (log (/ -1.0 (- x t_0))) (log (+ x t_0)))))

C

double code(double x) {
	double t_0 = sqrt(((x * x) + 1.0));
	double tmp;
	if (x < 0.0) {
		tmp = log((-1.0 / (x - t_0)));
	} else {
		tmp = log((x + t_0));
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = sqrt(((x * x) + 1.0d0))
    if (x < 0.0d0) then
        tmp = log(((-1.0d0) / (x - t_0)))
    else
        tmp = log((x + t_0))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = Math.sqrt(((x * x) + 1.0));
	double tmp;
	if (x < 0.0) {
		tmp = Math.log((-1.0 / (x - t_0)));
	} else {
		tmp = Math.log((x + t_0));
	}
	return tmp;
}

Python

def code(x):
	t_0 = math.sqrt(((x * x) + 1.0))
	tmp = 0
	if x < 0.0:
		tmp = math.log((-1.0 / (x - t_0)))
	else:
		tmp = math.log((x + t_0))
	return tmp

Julia

function code(x)
	t_0 = sqrt(Float64(Float64(x * x) + 1.0))
	tmp = 0.0
	if (x < 0.0)
		tmp = log(Float64(-1.0 / Float64(x - t_0)));
	else
		tmp = log(Float64(x + t_0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = sqrt(((x * x) + 1.0));
	tmp = 0.0;
	if (x < 0.0)
		tmp = log((-1.0 / (x - t_0)));
	else
		tmp = log((x + t_0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[Sqrt[N[(N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision]], $MachinePrecision]}, If[Less[x, 0.0], N[Log[N[(-1.0 / N[(x - t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], N[Log[N[(x + t$95$0), $MachinePrecision]], $MachinePrecision]]]

Reproduce

herbie shell --seed 2024192 
(FPCore (x)
  :name "Hyperbolic arcsine"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< x 0) (log (/ -1 (- x (sqrt (+ (* x x) 1))))) (log (+ x (sqrt (+ (* x x) 1))))))

  (log (+ x (sqrt (+ (* x x) 1.0)))))