Hyperbolic arcsine

Percentage Accurate: 18.2% → 99.6%

Time: 8.6s

Alternatives: 6

Speedup: 20.3×

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: 18.2% 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: 99.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.1:\\ \;\;\;\;\log \left(\frac{\frac{0.125}{x \cdot x} - 0.5}{x}\right)\\ \mathbf{elif}\;x \leq 1.05:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.044642857142857144, x \cdot x, 0.075\right), x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\log \left(\left(x - \frac{-0.5}{x}\right) + x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.1)
   (log (/ (- (/ 0.125 (* x x)) 0.5) x))
   (if (<= x 1.05)
     (*
      (fma
       (fma
        (fma -0.044642857142857144 (* x x) 0.075)
        (* x x)
        -0.16666666666666666)
       (* x x)
       1.0)
      x)
     (log (+ (- x (/ -0.5 x)) x)))))

C

double code(double x) {
	double tmp;
	if (x <= -1.1) {
		tmp = log((((0.125 / (x * x)) - 0.5) / x));
	} else if (x <= 1.05) {
		tmp = fma(fma(fma(-0.044642857142857144, (x * x), 0.075), (x * x), -0.16666666666666666), (x * x), 1.0) * x;
	} else {
		tmp = log(((x - (-0.5 / x)) + x));
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (x <= -1.1)
		tmp = log(Float64(Float64(Float64(0.125 / Float64(x * x)) - 0.5) / x));
	elseif (x <= 1.05)
		tmp = Float64(fma(fma(fma(-0.044642857142857144, Float64(x * x), 0.075), Float64(x * x), -0.16666666666666666), Float64(x * x), 1.0) * x);
	else
		tmp = log(Float64(Float64(x - Float64(-0.5 / x)) + x));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[x, -1.1], N[Log[N[(N[(N[(0.125 / N[(x * x), $MachinePrecision]), $MachinePrecision] - 0.5), $MachinePrecision] / x), $MachinePrecision]], $MachinePrecision], If[LessEqual[x, 1.05], N[(N[(N[(N[(-0.044642857142857144 * N[(x * x), $MachinePrecision] + 0.075), $MachinePrecision] * N[(x * x), $MachinePrecision] + -0.16666666666666666), $MachinePrecision] * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * x), $MachinePrecision], N[Log[N[(N[(x - N[(-0.5 / x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.1000000000000001

   1. Initial program 3.6%

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

   3. Taylor expanded in x around -inf

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

      1. associate-*r/ N/A

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

      2. mul-1-neg N/A

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

      3. neg-sub0 N/A

         \[\leadsto \log \left(\frac{\color{blue}{0 - \left(\frac{1}{2} - \frac{1}{8} \cdot \frac{1}{{x}^{2}}\right)}}{x}\right) \]

      4. associate--r- N/A

         \[\leadsto \log \left(\frac{\color{blue}{\left(0 - \frac{1}{2}\right) + \frac{1}{8} \cdot \frac{1}{{x}^{2}}}}{x}\right) \]

      5. metadata-eval N/A

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

      6. +-commutative N/A

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

      7. metadata-eval N/A

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

      8. sub-neg N/A

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

      9. lower-/.f64 N/A

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

      10. lower--.f64 N/A

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

      11. associate-*r/ N/A

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

      12. metadata-eval N/A

          \[\leadsto \log \left(\frac{\frac{\color{blue}{\frac{1}{8}}}{{x}^{2}} - \frac{1}{2}}{x}\right) \]

      13. lower-/.f64 N/A

          \[\leadsto \log \left(\frac{\color{blue}{\frac{\frac{1}{8}}{{x}^{2}}} - \frac{1}{2}}{x}\right) \]

      14. unpow2 N/A

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

      15. lower-*.f64 99.7

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

   5. Applied rewrites 99.7%

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

   if -1.1000000000000001 < x < 1.05000000000000004

   1. Initial program 8.1%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right) \cdot x} \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right) + 1\right)} \cdot x \]

      4. *-commutative N/A

         \[\leadsto \left(\color{blue}{\left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right) \cdot {x}^{2}} + 1\right) \cdot x \]

      5. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}, {x}^{2}, 1\right)} \cdot x \]

      6. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)}, {x}^{2}, 1\right) \cdot x \]

      7. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) \cdot {x}^{2}} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right), {x}^{2}, 1\right) \cdot x \]

      8. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) \cdot {x}^{2} + \color{blue}{\frac{-1}{6}}, {x}^{2}, 1\right) \cdot x \]

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}, {x}^{2}, \frac{-1}{6}\right)}, {x}^{2}, 1\right) \cdot x \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{-5}{112} \cdot {x}^{2} + \frac{3}{40}}, {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      11. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{-5}{112}, {x}^{2}, \frac{3}{40}\right)}, {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, \color{blue}{x \cdot x}, \frac{3}{40}\right), {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, \color{blue}{x \cdot x}, \frac{3}{40}\right), {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      14. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, x \cdot x, \frac{3}{40}\right), \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      15. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, x \cdot x, \frac{3}{40}\right), \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      16. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, x \cdot x, \frac{3}{40}\right), x \cdot x, \frac{-1}{6}\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

      17. lower-*.f64 100.0

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.044642857142857144, x \cdot x, 0.075\right), x \cdot x, -0.16666666666666666\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.044642857142857144, x \cdot x, 0.075\right), x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x} \]

   if 1.05000000000000004 < x

   1. Initial program 56.2%

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

   3. Taylor expanded in x around inf

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

      1. distribute-rgt-in N/A

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

      2. *-lft-identity N/A

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

      3. cancel-sign-sub N/A

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

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

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

      5. lower--.f64 N/A

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

      6. associate-*l* N/A

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

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

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

      8. metadata-eval N/A

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

      9. associate-*l/ N/A

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

      10. *-lft-identity N/A

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

      11. unpow2 N/A

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

      12. associate-/r* N/A

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

      13. *-inverses N/A

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

      14. associate-*r/ N/A

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

      15. metadata-eval N/A

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

      16. lower-/.f64 100.0

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

   5. Applied rewrites 100.0%

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

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.1:\\ \;\;\;\;\log \left(\frac{\frac{0.125}{x \cdot x} - 0.5}{x}\right)\\ \mathbf{elif}\;x \leq 1.05:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.044642857142857144, x \cdot x, 0.075\right), x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\log \left(\left(x - \frac{-0.5}{x}\right) + x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 99.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.3:\\ \;\;\;\;\log \left(\frac{-0.5}{x}\right)\\ \mathbf{elif}\;x \leq 1.05:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.044642857142857144, x \cdot x, 0.075\right), x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\log \left(\left(x - \frac{-0.5}{x}\right) + x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.3)
   (log (/ -0.5 x))
   (if (<= x 1.05)
     (*
      (fma
       (fma
        (fma -0.044642857142857144 (* x x) 0.075)
        (* x x)
        -0.16666666666666666)
       (* x x)
       1.0)
      x)
     (log (+ (- x (/ -0.5 x)) x)))))

C

double code(double x) {
	double tmp;
	if (x <= -1.3) {
		tmp = log((-0.5 / x));
	} else if (x <= 1.05) {
		tmp = fma(fma(fma(-0.044642857142857144, (x * x), 0.075), (x * x), -0.16666666666666666), (x * x), 1.0) * x;
	} else {
		tmp = log(((x - (-0.5 / x)) + x));
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (x <= -1.3)
		tmp = log(Float64(-0.5 / x));
	elseif (x <= 1.05)
		tmp = Float64(fma(fma(fma(-0.044642857142857144, Float64(x * x), 0.075), Float64(x * x), -0.16666666666666666), Float64(x * x), 1.0) * x);
	else
		tmp = log(Float64(Float64(x - Float64(-0.5 / x)) + x));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[x, -1.3], N[Log[N[(-0.5 / x), $MachinePrecision]], $MachinePrecision], If[LessEqual[x, 1.05], N[(N[(N[(N[(-0.044642857142857144 * N[(x * x), $MachinePrecision] + 0.075), $MachinePrecision] * N[(x * x), $MachinePrecision] + -0.16666666666666666), $MachinePrecision] * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * x), $MachinePrecision], N[Log[N[(N[(x - N[(-0.5 / x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.30000000000000004

   1. Initial program 3.6%

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

   3. Taylor expanded in x around -inf

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

      1. lower-/.f64 99.1

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

   5. Applied rewrites 99.1%

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

   if -1.30000000000000004 < x < 1.05000000000000004

   1. Initial program 8.1%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right) \cdot x} \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right) + 1\right)} \cdot x \]

      4. *-commutative N/A

         \[\leadsto \left(\color{blue}{\left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right) \cdot {x}^{2}} + 1\right) \cdot x \]

      5. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}, {x}^{2}, 1\right)} \cdot x \]

      6. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)}, {x}^{2}, 1\right) \cdot x \]

      7. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) \cdot {x}^{2}} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right), {x}^{2}, 1\right) \cdot x \]

      8. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) \cdot {x}^{2} + \color{blue}{\frac{-1}{6}}, {x}^{2}, 1\right) \cdot x \]

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}, {x}^{2}, \frac{-1}{6}\right)}, {x}^{2}, 1\right) \cdot x \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{-5}{112} \cdot {x}^{2} + \frac{3}{40}}, {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      11. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{-5}{112}, {x}^{2}, \frac{3}{40}\right)}, {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, \color{blue}{x \cdot x}, \frac{3}{40}\right), {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, \color{blue}{x \cdot x}, \frac{3}{40}\right), {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      14. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, x \cdot x, \frac{3}{40}\right), \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      15. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, x \cdot x, \frac{3}{40}\right), \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      16. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, x \cdot x, \frac{3}{40}\right), x \cdot x, \frac{-1}{6}\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

      17. lower-*.f64 100.0

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.044642857142857144, x \cdot x, 0.075\right), x \cdot x, -0.16666666666666666\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.044642857142857144, x \cdot x, 0.075\right), x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x} \]

   if 1.05000000000000004 < x

   1. Initial program 56.2%

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

   3. Taylor expanded in x around inf

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

      1. distribute-rgt-in N/A

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

      2. *-lft-identity N/A

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

      3. cancel-sign-sub N/A

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

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

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

      5. lower--.f64 N/A

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

      6. associate-*l* N/A

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

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

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

      8. metadata-eval N/A

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

      9. associate-*l/ N/A

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

      10. *-lft-identity N/A

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

      11. unpow2 N/A

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

      12. associate-/r* N/A

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

      13. *-inverses N/A

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

      14. associate-*r/ N/A

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

      15. metadata-eval N/A

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

      16. lower-/.f64 100.0

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

   5. Applied rewrites 100.0%

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

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.3:\\ \;\;\;\;\log \left(\frac{-0.5}{x}\right)\\ \mathbf{elif}\;x \leq 1.05:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.044642857142857144, x \cdot x, 0.075\right), x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\log \left(\left(x - \frac{-0.5}{x}\right) + x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.3:\\ \;\;\;\;\log \left(\frac{-0.5}{x}\right)\\ \mathbf{elif}\;x \leq 1.25:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.044642857142857144, x \cdot x, 0.075\right), x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\log \left(2 \cdot x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x -1.3)
   (log (/ -0.5 x))
   (if (<= x 1.25)
     (*
      (fma
       (fma
        (fma -0.044642857142857144 (* x x) 0.075)
        (* x x)
        -0.16666666666666666)
       (* x x)
       1.0)
      x)
     (log (* 2.0 x)))))

C

double code(double x) {
	double tmp;
	if (x <= -1.3) {
		tmp = log((-0.5 / x));
	} else if (x <= 1.25) {
		tmp = fma(fma(fma(-0.044642857142857144, (x * x), 0.075), (x * x), -0.16666666666666666), (x * x), 1.0) * x;
	} else {
		tmp = log((2.0 * x));
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (x <= -1.3)
		tmp = log(Float64(-0.5 / x));
	elseif (x <= 1.25)
		tmp = Float64(fma(fma(fma(-0.044642857142857144, Float64(x * x), 0.075), Float64(x * x), -0.16666666666666666), Float64(x * x), 1.0) * x);
	else
		tmp = log(Float64(2.0 * x));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[x, -1.3], N[Log[N[(-0.5 / x), $MachinePrecision]], $MachinePrecision], If[LessEqual[x, 1.25], N[(N[(N[(N[(-0.044642857142857144 * N[(x * x), $MachinePrecision] + 0.075), $MachinePrecision] * N[(x * x), $MachinePrecision] + -0.16666666666666666), $MachinePrecision] * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * x), $MachinePrecision], N[Log[N[(2.0 * x), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.30000000000000004

   1. Initial program 3.6%

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

   3. Taylor expanded in x around -inf

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

      1. lower-/.f64 99.1

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

   5. Applied rewrites 99.1%

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

   if -1.30000000000000004 < x < 1.25

   1. Initial program 8.1%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right) \cdot x} \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right) + 1\right)} \cdot x \]

      4. *-commutative N/A

         \[\leadsto \left(\color{blue}{\left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}\right) \cdot {x}^{2}} + 1\right) \cdot x \]

      5. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) - \frac{1}{6}, {x}^{2}, 1\right)} \cdot x \]

      6. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)}, {x}^{2}, 1\right) \cdot x \]

      7. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) \cdot {x}^{2}} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right), {x}^{2}, 1\right) \cdot x \]

      8. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}\right) \cdot {x}^{2} + \color{blue}{\frac{-1}{6}}, {x}^{2}, 1\right) \cdot x \]

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{3}{40} + \frac{-5}{112} \cdot {x}^{2}, {x}^{2}, \frac{-1}{6}\right)}, {x}^{2}, 1\right) \cdot x \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{-5}{112} \cdot {x}^{2} + \frac{3}{40}}, {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      11. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{-5}{112}, {x}^{2}, \frac{3}{40}\right)}, {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, \color{blue}{x \cdot x}, \frac{3}{40}\right), {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, \color{blue}{x \cdot x}, \frac{3}{40}\right), {x}^{2}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      14. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, x \cdot x, \frac{3}{40}\right), \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      15. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, x \cdot x, \frac{3}{40}\right), \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      16. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-5}{112}, x \cdot x, \frac{3}{40}\right), x \cdot x, \frac{-1}{6}\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

      17. lower-*.f64 100.0

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.044642857142857144, x \cdot x, 0.075\right), x \cdot x, -0.16666666666666666\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.044642857142857144, x \cdot x, 0.075\right), x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x} \]

   if 1.25 < x

   1. Initial program 56.2%

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

   3. Taylor expanded in x around inf

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

      1. lower-*.f64 99.6

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

   5. Applied rewrites 99.6%

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

Alternative 4: 75.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1.3:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(0.075, x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\log \left(2 \cdot x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 1.3)
   (* (fma (fma 0.075 (* x x) -0.16666666666666666) (* x x) 1.0) x)
   (log (* 2.0 x))))

C

double code(double x) {
	double tmp;
	if (x <= 1.3) {
		tmp = fma(fma(0.075, (x * x), -0.16666666666666666), (x * x), 1.0) * x;
	} else {
		tmp = log((2.0 * x));
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (x <= 1.3)
		tmp = Float64(fma(fma(0.075, Float64(x * x), -0.16666666666666666), Float64(x * x), 1.0) * x);
	else
		tmp = log(Float64(2.0 * x));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[x, 1.3], N[(N[(N[(0.075 * N[(x * x), $MachinePrecision] + -0.16666666666666666), $MachinePrecision] * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * x), $MachinePrecision], N[Log[N[(2.0 * x), $MachinePrecision]], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.30000000000000004

   1. Initial program 6.7%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right)\right) \cdot x} \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right) + 1\right)} \cdot x \]

      4. *-commutative N/A

         \[\leadsto \left(\color{blue}{\left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right) \cdot {x}^{2}} + 1\right) \cdot x \]

      5. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}, {x}^{2}, 1\right)} \cdot x \]

      6. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{3}{40} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)}, {x}^{2}, 1\right) \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\frac{3}{40} \cdot {x}^{2} + \color{blue}{\frac{-1}{6}}, {x}^{2}, 1\right) \cdot x \]

      8. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{3}{40}, {x}^{2}, \frac{-1}{6}\right)}, {x}^{2}, 1\right) \cdot x \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3}{40}, \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      10. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3}{40}, \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      11. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3}{40}, x \cdot x, \frac{-1}{6}\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

      12. lower-*.f64 68.8

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.075, x \cdot x, -0.16666666666666666\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

   5. Applied rewrites 68.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.075, x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x} \]

   if 1.30000000000000004 < x

   1. Initial program 56.2%

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

   3. Taylor expanded in x around inf

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

      1. lower-*.f64 99.6

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

   5. Applied rewrites 99.6%

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

Alternative 5: 58.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1.5:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(0.075, x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\log \left(1 + x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 1.5)
   (* (fma (fma 0.075 (* x x) -0.16666666666666666) (* x x) 1.0) x)
   (log (+ 1.0 x))))

C

double code(double x) {
	double tmp;
	if (x <= 1.5) {
		tmp = fma(fma(0.075, (x * x), -0.16666666666666666), (x * x), 1.0) * x;
	} else {
		tmp = log((1.0 + x));
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (x <= 1.5)
		tmp = Float64(fma(fma(0.075, Float64(x * x), -0.16666666666666666), Float64(x * x), 1.0) * x);
	else
		tmp = log(Float64(1.0 + x));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[x, 1.5], N[(N[(N[(0.075 * N[(x * x), $MachinePrecision] + -0.16666666666666666), $MachinePrecision] * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * x), $MachinePrecision], N[Log[N[(1.0 + x), $MachinePrecision]], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.5

   1. Initial program 6.7%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right)\right) \cdot x} \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right) + 1\right)} \cdot x \]

      4. *-commutative N/A

         \[\leadsto \left(\color{blue}{\left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right) \cdot {x}^{2}} + 1\right) \cdot x \]

      5. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}, {x}^{2}, 1\right)} \cdot x \]

      6. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{3}{40} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)}, {x}^{2}, 1\right) \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\frac{3}{40} \cdot {x}^{2} + \color{blue}{\frac{-1}{6}}, {x}^{2}, 1\right) \cdot x \]

      8. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{3}{40}, {x}^{2}, \frac{-1}{6}\right)}, {x}^{2}, 1\right) \cdot x \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3}{40}, \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      10. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3}{40}, \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

      11. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3}{40}, x \cdot x, \frac{-1}{6}\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

      12. lower-*.f64 68.8

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.075, x \cdot x, -0.16666666666666666\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

   5. Applied rewrites 68.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.075, x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x} \]

   if 1.5 < x

   1. Initial program 56.2%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 31.3%

      \[\leadsto \log \left(x + \color{blue}{1}\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 62.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 1.5:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(0.075, x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\log \left(1 + x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 52.4% accurate, 20.3× speedup

Math

\[\begin{array}{l} \\ 1 \cdot x \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* 1.0 x))

C

double code(double x) {
	return 1.0 * x;
}

Fortran

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

Java

public static double code(double x) {
	return 1.0 * x;
}

Python

def code(x):
	return 1.0 * x

Julia

function code(x)
	return Float64(1.0 * x)
end

MATLAB

function tmp = code(x)
	tmp = 1.0 * x;
end

Wolfram

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

Derivation

1. Initial program 14.8%

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

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right)\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right)\right) \cdot x} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right)\right) \cdot x} \]

   3. +-commutative N/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right) + 1\right)} \cdot x \]

   4. *-commutative N/A

      \[\leadsto \left(\color{blue}{\left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}\right) \cdot {x}^{2}} + 1\right) \cdot x \]

   5. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{3}{40} \cdot {x}^{2} - \frac{1}{6}, {x}^{2}, 1\right)} \cdot x \]

   6. sub-neg N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{3}{40} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)}, {x}^{2}, 1\right) \cdot x \]

   7. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left(\frac{3}{40} \cdot {x}^{2} + \color{blue}{\frac{-1}{6}}, {x}^{2}, 1\right) \cdot x \]

   8. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{3}{40}, {x}^{2}, \frac{-1}{6}\right)}, {x}^{2}, 1\right) \cdot x \]

   9. unpow2 N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3}{40}, \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

   10. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3}{40}, \color{blue}{x \cdot x}, \frac{-1}{6}\right), {x}^{2}, 1\right) \cdot x \]

   11. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3}{40}, x \cdot x, \frac{-1}{6}\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

   12. lower-*.f64 58.1

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.075, x \cdot x, -0.16666666666666666\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

5. Applied rewrites 58.1%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.075, x \cdot x, -0.16666666666666666\right), x \cdot x, 1\right) \cdot x} \]

6. Taylor expanded in x around 0

   \[\leadsto 1 \cdot x \]
7. Step-by-step derivation

8. Applied rewrites 58.2%

   \[\leadsto 1 \cdot x \]
9. Add Preprocessing

Developer Target 1: 29.6% accurate, 0.9× 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 2024235 
(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)))))