Hyperbolic arcsine

Percentage Accurate: 17.9% → 99.5%

Time: 10.8s

Alternatives: 10

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: 17.9% 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, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6 \cdot 10^{-6}:\\ \;\;\;\;\log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right)\\ \mathbf{elif}\;x \leq 0.76:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5 + \frac{-0.125}{x \cdot x}}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -6e-6)
   (log (/ -1.0 (- x (hypot 1.0 x))))
   (if (<= x 0.76) x (log (+ (* x 2.0) (/ (+ 0.5 (/ -0.125 (* x x))) x))))))

C

double code(double x) {
	double tmp;
	if (x <= -6e-6) {
		tmp = log((-1.0 / (x - hypot(1.0, x))));
	} else if (x <= 0.76) {
		tmp = x;
	} else {
		tmp = log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	}
	return tmp;
}

Java

public static double code(double x) {
	double tmp;
	if (x <= -6e-6) {
		tmp = Math.log((-1.0 / (x - Math.hypot(1.0, x))));
	} else if (x <= 0.76) {
		tmp = x;
	} else {
		tmp = Math.log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -6e-6:
		tmp = math.log((-1.0 / (x - math.hypot(1.0, x))))
	elif x <= 0.76:
		tmp = x
	else:
		tmp = math.log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -6e-6)
		tmp = log(Float64(-1.0 / Float64(x - hypot(1.0, x))));
	elseif (x <= 0.76)
		tmp = x;
	else
		tmp = log(Float64(Float64(x * 2.0) + Float64(Float64(0.5 + Float64(-0.125 / Float64(x * x))) / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -6e-6)
		tmp = log((-1.0 / (x - hypot(1.0, x))));
	elseif (x <= 0.76)
		tmp = x;
	else
		tmp = log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -6e-6], N[Log[N[(-1.0 / N[(x - N[Sqrt[1.0 ^ 2 + x ^ 2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], If[LessEqual[x, 0.76], x, 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]]]

Derivation

1. Split input into 3 regimes

2. if x < -6.0000000000000002e-6

   1. Initial program 3.1%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 4.4%

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

   3. Simplified 4.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. flip-+ N/A

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

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

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

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

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

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

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

      5. rem-square-sqrt N/A

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

      6. metadata-eval N/A

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

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

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

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

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

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

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

      10. hypot-undefine N/A

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

      11. hypot-lowering-hypot.f64 2.8%

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

   6. Applied egg-rr 2.8%

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

      1. +-commutative N/A

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

      2. associate--r+ N/A

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

      3. +-inverses N/A

         \[\leadsto \mathsf{log.f64}\left(\left(\frac{0 - 1}{x - \sqrt{1 \cdot 1 + x \cdot x}}\right)\right) \]

      4. metadata-eval N/A

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

      5. metadata-eval N/A

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

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

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

      7. metadata-eval N/A

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

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

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

      9. hypot-undefine N/A

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

      10. hypot-lowering-hypot.f64 100.0%

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

   8. Applied egg-rr 100.0%

      \[\leadsto \log \color{blue}{\left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]

   if -6.0000000000000002e-6 < x < 0.76000000000000001

   1. Initial program 6.4%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 6.4%

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

   3. Simplified 6.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

   7. Simplified 100.0%

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

   if 0.76000000000000001 < x

   1. Initial program 57.3%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

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

   7. Simplified 100.0%

      \[\leadsto \log \color{blue}{\left(x \cdot 2 + \frac{0.5 + \frac{-0.125}{x \cdot x}}{x}\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 2: 99.2% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.78:\\ \;\;\;\;\log \left(\frac{-1}{x \cdot \left(\left(2 + \frac{0.5}{x \cdot x}\right) + \frac{-0.125}{x \cdot \left(x \cdot \left(x \cdot x\right)\right)}\right)}\right)\\ \mathbf{elif}\;x \leq 0.76:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5 + \frac{-0.125}{x \cdot x}}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -0.78)
   (log
    (/ -1.0 (* x (+ (+ 2.0 (/ 0.5 (* x x))) (/ -0.125 (* x (* x (* x x))))))))
   (if (<= x 0.76) x (log (+ (* x 2.0) (/ (+ 0.5 (/ -0.125 (* x x))) x))))))

C

double code(double x) {
	double tmp;
	if (x <= -0.78) {
		tmp = log((-1.0 / (x * ((2.0 + (0.5 / (x * x))) + (-0.125 / (x * (x * (x * x))))))));
	} else if (x <= 0.76) {
		tmp = x;
	} else {
		tmp = log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-0.78d0)) then
        tmp = log(((-1.0d0) / (x * ((2.0d0 + (0.5d0 / (x * x))) + ((-0.125d0) / (x * (x * (x * x))))))))
    else if (x <= 0.76d0) then
        tmp = x
    else
        tmp = log(((x * 2.0d0) + ((0.5d0 + ((-0.125d0) / (x * x))) / x)))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -0.78) {
		tmp = Math.log((-1.0 / (x * ((2.0 + (0.5 / (x * x))) + (-0.125 / (x * (x * (x * x))))))));
	} else if (x <= 0.76) {
		tmp = x;
	} else {
		tmp = Math.log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -0.78:
		tmp = math.log((-1.0 / (x * ((2.0 + (0.5 / (x * x))) + (-0.125 / (x * (x * (x * x))))))))
	elif x <= 0.76:
		tmp = x
	else:
		tmp = math.log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -0.78)
		tmp = log(Float64(-1.0 / Float64(x * Float64(Float64(2.0 + Float64(0.5 / Float64(x * x))) + Float64(-0.125 / Float64(x * Float64(x * Float64(x * x))))))));
	elseif (x <= 0.76)
		tmp = x;
	else
		tmp = log(Float64(Float64(x * 2.0) + Float64(Float64(0.5 + Float64(-0.125 / Float64(x * x))) / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -0.78)
		tmp = log((-1.0 / (x * ((2.0 + (0.5 / (x * x))) + (-0.125 / (x * (x * (x * x))))))));
	elseif (x <= 0.76)
		tmp = x;
	else
		tmp = log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -0.78], N[Log[N[(-1.0 / N[(x * N[(N[(2.0 + N[(0.5 / N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(-0.125 / N[(x * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], If[LessEqual[x, 0.76], x, 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]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.78000000000000003

   1. Initial program 3.1%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 4.4%

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

   3. Simplified 4.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. flip-+ N/A

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

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

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

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

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

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

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

      5. rem-square-sqrt N/A

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

      6. metadata-eval N/A

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

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

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

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

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

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

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

      10. hypot-undefine N/A

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

      11. hypot-lowering-hypot.f64 2.8%

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

   6. Applied egg-rr 2.8%

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

   7. Taylor expanded in x around -inf

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

      1. mul-1-neg N/A

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

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

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

      3. neg-sub0 N/A

         \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\mathsf{\_.f64}\left(\mathsf{*.f64}\left(x, x\right), \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, x\right)\right)\right), \left(x \cdot \left(0 - \left(\frac{\frac{1}{8}}{{x}^{4}} - \left(2 + \frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right)\right)\right) \]

      4. associate-+l- N/A

         \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\mathsf{\_.f64}\left(\mathsf{*.f64}\left(x, x\right), \mathsf{+.f64}\left(1, \mathsf{*.f64}\left(x, x\right)\right)\right), \left(x \cdot \left(\left(0 - \frac{\frac{1}{8}}{{x}^{4}}\right) + \left(2 + \frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right)\right) \]

      5. neg-sub0 N/A

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

      6. +-commutative N/A

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

      7. sub-neg N/A

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

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

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

      9. sub-neg N/A

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

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

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

   9. Simplified 2.2%

      \[\leadsto \log \left(\frac{x \cdot x - \left(1 + x \cdot x\right)}{\color{blue}{x \cdot \left(\left(2 + \frac{0.5}{x \cdot x}\right) + \frac{-0.125}{x \cdot \left(x \cdot \left(x \cdot x\right)\right)}\right)}}\right) \]

   10. Taylor expanded in x around 0

       \[\leadsto \mathsf{log.f64}\left(\mathsf{/.f64}\left(\color{blue}{-1}, \mathsf{*.f64}\left(x, \mathsf{+.f64}\left(\mathsf{+.f64}\left(2, \mathsf{/.f64}\left(\frac{1}{2}, \mathsf{*.f64}\left(x, x\right)\right)\right), \mathsf{/.f64}\left(\frac{-1}{8}, \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, x\right)\right)\right)\right)\right)\right)\right)\right) \]
   11. Step-by-step derivation

   12. Simplified 99.4%

       \[\leadsto \log \left(\frac{\color{blue}{-1}}{x \cdot \left(\left(2 + \frac{0.5}{x \cdot x}\right) + \frac{-0.125}{x \cdot \left(x \cdot \left(x \cdot x\right)\right)}\right)}\right) \]

   if -0.78000000000000003 < x < 0.76000000000000001

   1. Initial program 6.4%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 6.4%

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

   3. Simplified 6.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

   7. Simplified 100.0%

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

   if 0.76000000000000001 < x

   1. Initial program 57.3%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

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

   7. Simplified 100.0%

      \[\leadsto \log \color{blue}{\left(x \cdot 2 + \frac{0.5 + \frac{-0.125}{x \cdot x}}{x}\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 99.3% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.9:\\ \;\;\;\;\log \left(\frac{-0.5 + \left(\frac{0.125}{x \cdot x} + \frac{-0.0625}{x \cdot \left(x \cdot \left(x \cdot x\right)\right)}\right)}{x}\right)\\ \mathbf{elif}\;x \leq 0.76:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5 + \frac{-0.125}{x \cdot x}}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -0.9)
   (log (/ (+ -0.5 (+ (/ 0.125 (* x x)) (/ -0.0625 (* x (* x (* x x)))))) x))
   (if (<= x 0.76) x (log (+ (* x 2.0) (/ (+ 0.5 (/ -0.125 (* x x))) x))))))

C

double code(double x) {
	double tmp;
	if (x <= -0.9) {
		tmp = log(((-0.5 + ((0.125 / (x * x)) + (-0.0625 / (x * (x * (x * x)))))) / x));
	} else if (x <= 0.76) {
		tmp = x;
	} else {
		tmp = log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-0.9d0)) then
        tmp = log((((-0.5d0) + ((0.125d0 / (x * x)) + ((-0.0625d0) / (x * (x * (x * x)))))) / x))
    else if (x <= 0.76d0) then
        tmp = x
    else
        tmp = log(((x * 2.0d0) + ((0.5d0 + ((-0.125d0) / (x * x))) / x)))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -0.9) {
		tmp = Math.log(((-0.5 + ((0.125 / (x * x)) + (-0.0625 / (x * (x * (x * x)))))) / x));
	} else if (x <= 0.76) {
		tmp = x;
	} else {
		tmp = Math.log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -0.9:
		tmp = math.log(((-0.5 + ((0.125 / (x * x)) + (-0.0625 / (x * (x * (x * x)))))) / x))
	elif x <= 0.76:
		tmp = x
	else:
		tmp = math.log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -0.9)
		tmp = log(Float64(Float64(-0.5 + Float64(Float64(0.125 / Float64(x * x)) + Float64(-0.0625 / Float64(x * Float64(x * Float64(x * x)))))) / x));
	elseif (x <= 0.76)
		tmp = x;
	else
		tmp = log(Float64(Float64(x * 2.0) + Float64(Float64(0.5 + Float64(-0.125 / Float64(x * x))) / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -0.9)
		tmp = log(((-0.5 + ((0.125 / (x * x)) + (-0.0625 / (x * (x * (x * x)))))) / x));
	elseif (x <= 0.76)
		tmp = x;
	else
		tmp = log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -0.9], N[Log[N[(N[(-0.5 + N[(N[(0.125 / N[(x * x), $MachinePrecision]), $MachinePrecision] + N[(-0.0625 / N[(x * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]], $MachinePrecision], If[LessEqual[x, 0.76], x, 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]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.900000000000000022

   1. Initial program 3.1%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 4.4%

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

   3. Simplified 4.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around -inf

      \[\leadsto \mathsf{log.f64}\left(\color{blue}{\left(-1 \cdot \frac{\left(\frac{1}{2} + \frac{\frac{1}{16}}{{x}^{4}}\right) - \frac{1}{8} \cdot \frac{1}{{x}^{2}}}{x}\right)}\right) \]
   6. 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

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

   7. Simplified 99.3%

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

   if -0.900000000000000022 < x < 0.76000000000000001

   1. Initial program 6.4%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 6.4%

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

   3. Simplified 6.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

   7. Simplified 100.0%

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

   if 0.76000000000000001 < x

   1. Initial program 57.3%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

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

   7. Simplified 100.0%

      \[\leadsto \log \color{blue}{\left(x \cdot 2 + \frac{0.5 + \frac{-0.125}{x \cdot x}}{x}\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 99.2% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.8:\\ \;\;\;\;\log \left(\frac{-1}{x \cdot \left(2 + \frac{0.5}{x \cdot x}\right)}\right)\\ \mathbf{elif}\;x \leq 0.76:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5 + \frac{-0.125}{x \cdot x}}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -0.8)
   (log (/ -1.0 (* x (+ 2.0 (/ 0.5 (* x x))))))
   (if (<= x 0.76) x (log (+ (* x 2.0) (/ (+ 0.5 (/ -0.125 (* x x))) x))))))

C

double code(double x) {
	double tmp;
	if (x <= -0.8) {
		tmp = log((-1.0 / (x * (2.0 + (0.5 / (x * x))))));
	} else if (x <= 0.76) {
		tmp = x;
	} else {
		tmp = log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-0.8d0)) then
        tmp = log(((-1.0d0) / (x * (2.0d0 + (0.5d0 / (x * x))))))
    else if (x <= 0.76d0) then
        tmp = x
    else
        tmp = log(((x * 2.0d0) + ((0.5d0 + ((-0.125d0) / (x * x))) / x)))
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -0.8) {
		tmp = Math.log((-1.0 / (x * (2.0 + (0.5 / (x * x))))));
	} else if (x <= 0.76) {
		tmp = x;
	} else {
		tmp = Math.log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -0.8:
		tmp = math.log((-1.0 / (x * (2.0 + (0.5 / (x * x))))))
	elif x <= 0.76:
		tmp = x
	else:
		tmp = math.log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -0.8)
		tmp = log(Float64(-1.0 / Float64(x * Float64(2.0 + Float64(0.5 / Float64(x * x))))));
	elseif (x <= 0.76)
		tmp = x;
	else
		tmp = log(Float64(Float64(x * 2.0) + Float64(Float64(0.5 + Float64(-0.125 / Float64(x * x))) / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -0.8)
		tmp = log((-1.0 / (x * (2.0 + (0.5 / (x * x))))));
	elseif (x <= 0.76)
		tmp = x;
	else
		tmp = log(((x * 2.0) + ((0.5 + (-0.125 / (x * x))) / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -0.8], N[Log[N[(-1.0 / N[(x * N[(2.0 + N[(0.5 / N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], If[LessEqual[x, 0.76], x, 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]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.80000000000000004

   1. Initial program 3.1%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 4.4%

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

   3. Simplified 4.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. flip-+ N/A

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

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

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

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

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

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

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

      5. rem-square-sqrt N/A

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

      6. metadata-eval N/A

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

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

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

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

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

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

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

      10. hypot-undefine N/A

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

      11. hypot-lowering-hypot.f64 2.8%

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

   6. Applied egg-rr 2.8%

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

      1. +-commutative N/A

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

      2. associate--r+ N/A

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

      3. +-inverses N/A

         \[\leadsto \mathsf{log.f64}\left(\left(\frac{0 - 1}{x - \sqrt{1 \cdot 1 + x \cdot x}}\right)\right) \]

      4. metadata-eval N/A

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

      5. metadata-eval N/A

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

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

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

      7. metadata-eval N/A

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

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

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

      9. hypot-undefine N/A

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

      10. hypot-lowering-hypot.f64 100.0%

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

   8. Applied egg-rr 100.0%

      \[\leadsto \log \color{blue}{\left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]

   9. Taylor expanded in x around -inf

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

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

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

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

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

       3. associate-*r/ N/A

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

       4. metadata-eval N/A

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

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

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

       6. unpow2 N/A

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

       7. *-lowering-*.f64 99.2%

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

   11. Simplified 99.2%

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

   if -0.80000000000000004 < x < 0.76000000000000001

   1. Initial program 6.4%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 6.4%

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

   3. Simplified 6.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

   7. Simplified 100.0%

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

   if 0.76000000000000001 < x

   1. Initial program 57.3%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

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

   7. Simplified 100.0%

      \[\leadsto \log \color{blue}{\left(x \cdot 2 + \frac{0.5 + \frac{-0.125}{x \cdot x}}{x}\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 99.2% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.8:\\ \;\;\;\;\log \left(\frac{-1}{x \cdot \left(2 + \frac{0.5}{x \cdot x}\right)}\right)\\ \mathbf{elif}\;x \leq 0.8:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -0.8)
   (log (/ -1.0 (* x (+ 2.0 (/ 0.5 (* x x))))))
   (if (<= x 0.8) x (log (+ (* x 2.0) (/ 0.5 x))))))

C

double code(double x) {
	double tmp;
	if (x <= -0.8) {
		tmp = log((-1.0 / (x * (2.0 + (0.5 / (x * x))))));
	} else if (x <= 0.8) {
		tmp = x;
	} 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 <= (-0.8d0)) then
        tmp = log(((-1.0d0) / (x * (2.0d0 + (0.5d0 / (x * x))))))
    else if (x <= 0.8d0) then
        tmp = x
    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 <= -0.8) {
		tmp = Math.log((-1.0 / (x * (2.0 + (0.5 / (x * x))))));
	} else if (x <= 0.8) {
		tmp = x;
	} else {
		tmp = Math.log(((x * 2.0) + (0.5 / x)));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -0.8:
		tmp = math.log((-1.0 / (x * (2.0 + (0.5 / (x * x))))))
	elif x <= 0.8:
		tmp = x
	else:
		tmp = math.log(((x * 2.0) + (0.5 / x)))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -0.8)
		tmp = log(Float64(-1.0 / Float64(x * Float64(2.0 + Float64(0.5 / Float64(x * x))))));
	elseif (x <= 0.8)
		tmp = x;
	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 <= -0.8)
		tmp = log((-1.0 / (x * (2.0 + (0.5 / (x * x))))));
	elseif (x <= 0.8)
		tmp = x;
	else
		tmp = log(((x * 2.0) + (0.5 / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -0.8], N[Log[N[(-1.0 / N[(x * N[(2.0 + N[(0.5 / N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], If[LessEqual[x, 0.8], x, 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 < -0.80000000000000004

   1. Initial program 3.1%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 4.4%

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

   3. Simplified 4.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. flip-+ N/A

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

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

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

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

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

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

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

      5. rem-square-sqrt N/A

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

      6. metadata-eval N/A

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

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

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

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

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

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

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

      10. hypot-undefine N/A

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

      11. hypot-lowering-hypot.f64 2.8%

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

   6. Applied egg-rr 2.8%

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

      1. +-commutative N/A

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

      2. associate--r+ N/A

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

      3. +-inverses N/A

         \[\leadsto \mathsf{log.f64}\left(\left(\frac{0 - 1}{x - \sqrt{1 \cdot 1 + x \cdot x}}\right)\right) \]

      4. metadata-eval N/A

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

      5. metadata-eval N/A

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

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

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

      7. metadata-eval N/A

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

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

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

      9. hypot-undefine N/A

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

      10. hypot-lowering-hypot.f64 100.0%

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

   8. Applied egg-rr 100.0%

      \[\leadsto \log \color{blue}{\left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]

   9. Taylor expanded in x around -inf

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

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

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

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

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

       3. associate-*r/ N/A

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

       4. metadata-eval N/A

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

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

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

       6. unpow2 N/A

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

       7. *-lowering-*.f64 99.2%

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

   11. Simplified 99.2%

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

   if -0.80000000000000004 < x < 0.80000000000000004

   1. Initial program 6.4%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 6.4%

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

   3. Simplified 6.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

   7. Simplified 100.0%

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

   if 0.80000000000000004 < x

   1. Initial program 57.3%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

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

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

      11. times-frac N/A

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

      12. metadata-eval N/A

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

      13. *-inverses N/A

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

      14. metadata-eval N/A

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

      15. /-lowering-/.f64 99.8%

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

   7. Simplified 99.8%

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

Alternative 6: 99.2% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.96:\\ \;\;\;\;\log \left(\frac{-0.5 + \frac{0.125}{x \cdot x}}{x}\right)\\ \mathbf{elif}\;x \leq 0.8:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -0.96)
   (log (/ (+ -0.5 (/ 0.125 (* x x))) x))
   (if (<= x 0.8) x (log (+ (* x 2.0) (/ 0.5 x))))))

C

double code(double x) {
	double tmp;
	if (x <= -0.96) {
		tmp = log(((-0.5 + (0.125 / (x * x))) / x));
	} else if (x <= 0.8) {
		tmp = x;
	} 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 <= (-0.96d0)) then
        tmp = log((((-0.5d0) + (0.125d0 / (x * x))) / x))
    else if (x <= 0.8d0) then
        tmp = x
    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 <= -0.96) {
		tmp = Math.log(((-0.5 + (0.125 / (x * x))) / x));
	} else if (x <= 0.8) {
		tmp = x;
	} else {
		tmp = Math.log(((x * 2.0) + (0.5 / x)));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -0.96:
		tmp = math.log(((-0.5 + (0.125 / (x * x))) / x))
	elif x <= 0.8:
		tmp = x
	else:
		tmp = math.log(((x * 2.0) + (0.5 / x)))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -0.96)
		tmp = log(Float64(Float64(-0.5 + Float64(0.125 / Float64(x * x))) / x));
	elseif (x <= 0.8)
		tmp = x;
	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 <= -0.96)
		tmp = log(((-0.5 + (0.125 / (x * x))) / x));
	elseif (x <= 0.8)
		tmp = x;
	else
		tmp = log(((x * 2.0) + (0.5 / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -0.96], N[Log[N[(N[(-0.5 + N[(0.125 / N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]], $MachinePrecision], If[LessEqual[x, 0.8], x, 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 < -0.95999999999999996

   1. Initial program 3.1%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 4.4%

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

   3. Simplified 4.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around -inf

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

      1. associate-*r/ N/A

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

      2. mul-1-neg N/A

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

      3. sub-neg N/A

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

      4. +-commutative N/A

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

      5. distribute-neg-in N/A

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

      6. remove-double-neg N/A

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

      7. sub-neg N/A

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

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

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

      9. sub-neg N/A

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

      10. metadata-eval N/A

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

      11. +-commutative N/A

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

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

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

      13. associate-*r/ N/A

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

      14. metadata-eval N/A

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

      15. /-lowering-/.f64 N/A

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

      16. unpow2 N/A

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

      17. *-lowering-*.f64 99.2%

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

   7. Simplified 99.2%

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

   if -0.95999999999999996 < x < 0.80000000000000004

   1. Initial program 6.4%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 6.4%

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

   3. Simplified 6.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

   7. Simplified 100.0%

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

   if 0.80000000000000004 < x

   1. Initial program 57.3%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

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

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

      11. times-frac N/A

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

      12. metadata-eval N/A

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

      13. *-inverses N/A

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

      14. metadata-eval N/A

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

      15. /-lowering-/.f64 99.8%

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

   7. Simplified 99.8%

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

Alternative 7: 99.1% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.25:\\ \;\;\;\;\log \left(\frac{-0.5}{x}\right)\\ \mathbf{elif}\;x \leq 0.8:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + \frac{0.5}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.25)
   (log (/ -0.5 x))
   (if (<= x 0.8) x (log (+ (* x 2.0) (/ 0.5 x))))))

C

double code(double x) {
	double tmp;
	if (x <= -1.25) {
		tmp = log((-0.5 / x));
	} else if (x <= 0.8) {
		tmp = x;
	} 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 <= (-1.25d0)) then
        tmp = log(((-0.5d0) / x))
    else if (x <= 0.8d0) then
        tmp = x
    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 <= -1.25) {
		tmp = Math.log((-0.5 / x));
	} else if (x <= 0.8) {
		tmp = x;
	} else {
		tmp = Math.log(((x * 2.0) + (0.5 / x)));
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -1.25:
		tmp = math.log((-0.5 / x))
	elif x <= 0.8:
		tmp = x
	else:
		tmp = math.log(((x * 2.0) + (0.5 / x)))
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -1.25)
		tmp = log(Float64(-0.5 / x));
	elseif (x <= 0.8)
		tmp = x;
	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 <= -1.25)
		tmp = log((-0.5 / x));
	elseif (x <= 0.8)
		tmp = x;
	else
		tmp = log(((x * 2.0) + (0.5 / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -1.25], N[Log[N[(-0.5 / x), $MachinePrecision]], $MachinePrecision], If[LessEqual[x, 0.8], x, 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 < -1.25

   1. Initial program 3.1%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 4.4%

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

   3. Simplified 4.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around -inf

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

      1. /-lowering-/.f64 99.1%

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

   7. Simplified 99.1%

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

   if -1.25 < x < 0.80000000000000004

   1. Initial program 6.4%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 6.4%

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

   3. Simplified 6.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

   7. Simplified 100.0%

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

   if 0.80000000000000004 < x

   1. Initial program 57.3%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

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

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

      11. times-frac N/A

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

      12. metadata-eval N/A

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

      13. *-inverses N/A

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

      14. metadata-eval N/A

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

      15. /-lowering-/.f64 99.8%

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

   7. Simplified 99.8%

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

Alternative 8: 99.0% accurate, 1.8× speedup

Math

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

FPCore

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

C

double code(double x) {
	double tmp;
	if (x <= -1.25) {
		tmp = log((-0.5 / x));
	} else 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 = log(((-0.5d0) / x))
    else 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 = Math.log((-0.5 / x));
	} else 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 = math.log((-0.5 / x))
	elif 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 = log(Float64(-0.5 / x));
	elseif (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 = log((-0.5 / x));
	elseif (x <= 1.25)
		tmp = x;
	else
		tmp = log((x + x));
	end
	tmp_2 = tmp;
end

Wolfram

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

Derivation

1. Split input into 3 regimes

2. if x < -1.25

   1. Initial program 3.1%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 4.4%

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

   3. Simplified 4.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around -inf

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

      1. /-lowering-/.f64 99.1%

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

   7. Simplified 99.1%

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

   if -1.25 < x < 1.25

   1. Initial program 6.4%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 6.4%

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

   3. Simplified 6.4%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

   7. Simplified 100.0%

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

   if 1.25 < x

   1. Initial program 57.3%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

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

   7. Simplified 99.1%

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

Alternative 9: 75.1% 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 5.1%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 5.6%

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

   3. Simplified 5.6%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

   7. Simplified 64.5%

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

   if 1.25 < x

   1. Initial program 57.3%

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

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

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

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

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

      3. +-commutative N/A

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

      4. hypot-1-def N/A

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

      5. hypot-lowering-hypot.f64 100.0%

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

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

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

   7. Simplified 99.1%

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

Alternative 10: 52.3% 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 17.2%

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

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

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

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

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

   3. +-commutative N/A

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

   4. hypot-1-def N/A

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

   5. hypot-lowering-hypot.f64 27.4%

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

3. Simplified 27.4%

   \[\leadsto \color{blue}{\log \left(x + \mathsf{hypot}\left(1, x\right)\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around 0

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

7. Simplified 50.9%

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

Developer Target 1: 30.3% 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 2024138 
(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)))))