Hyperbolic arcsine

Average Accuracy: 17.3% → 99.9%

Time: 10.4s

Precision: binary64

Cost: 20488

Math

\[\log \left(x + \sqrt{x \cdot x + 1}\right) \]

↓

\[\begin{array}{l} \mathbf{if}\;x \leq -0.023:\\ \;\;\;\;-\log \left(\mathsf{hypot}\left(1, x\right) - x\right)\\ \mathbf{elif}\;x \leq 0.025:\\ \;\;\;\;-0.16666666666666666 \cdot {x}^{3} + \left(0.075 \cdot {x}^{5} + \left(x + -0.044642857142857144 \cdot {x}^{7}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-\log \left(\frac{1}{x + \mathsf{hypot}\left(1, x\right)}\right)\\ \end{array} \]

FPCore

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

↓

(FPCore (x)
 :precision binary64
 (if (<= x -0.023)
   (- (log (- (hypot 1.0 x) x)))
   (if (<= x 0.025)
     (+
      (* -0.16666666666666666 (pow x 3.0))
      (+ (* 0.075 (pow x 5.0)) (+ x (* -0.044642857142857144 (pow x 7.0)))))
     (- (log (/ 1.0 (+ x (hypot 1.0 x))))))))

C

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

↓

double code(double x) {
	double tmp;
	if (x <= -0.023) {
		tmp = -log((hypot(1.0, x) - x));
	} else if (x <= 0.025) {
		tmp = (-0.16666666666666666 * pow(x, 3.0)) + ((0.075 * pow(x, 5.0)) + (x + (-0.044642857142857144 * pow(x, 7.0))));
	} else {
		tmp = -log((1.0 / (x + hypot(1.0, x))));
	}
	return tmp;
}

Java

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

↓

public static double code(double x) {
	double tmp;
	if (x <= -0.023) {
		tmp = -Math.log((Math.hypot(1.0, x) - x));
	} else if (x <= 0.025) {
		tmp = (-0.16666666666666666 * Math.pow(x, 3.0)) + ((0.075 * Math.pow(x, 5.0)) + (x + (-0.044642857142857144 * Math.pow(x, 7.0))));
	} else {
		tmp = -Math.log((1.0 / (x + Math.hypot(1.0, x))));
	}
	return tmp;
}

Python

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

↓

def code(x):
	tmp = 0
	if x <= -0.023:
		tmp = -math.log((math.hypot(1.0, x) - x))
	elif x <= 0.025:
		tmp = (-0.16666666666666666 * math.pow(x, 3.0)) + ((0.075 * math.pow(x, 5.0)) + (x + (-0.044642857142857144 * math.pow(x, 7.0))))
	else:
		tmp = -math.log((1.0 / (x + math.hypot(1.0, x))))
	return tmp

Julia

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

↓

function code(x)
	tmp = 0.0
	if (x <= -0.023)
		tmp = Float64(-log(Float64(hypot(1.0, x) - x)));
	elseif (x <= 0.025)
		tmp = Float64(Float64(-0.16666666666666666 * (x ^ 3.0)) + Float64(Float64(0.075 * (x ^ 5.0)) + Float64(x + Float64(-0.044642857142857144 * (x ^ 7.0)))));
	else
		tmp = Float64(-log(Float64(1.0 / Float64(x + hypot(1.0, x)))));
	end
	return tmp
end

MATLAB

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

↓

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -0.023)
		tmp = -log((hypot(1.0, x) - x));
	elseif (x <= 0.025)
		tmp = (-0.16666666666666666 * (x ^ 3.0)) + ((0.075 * (x ^ 5.0)) + (x + (-0.044642857142857144 * (x ^ 7.0))));
	else
		tmp = -log((1.0 / (x + hypot(1.0, x))));
	end
	tmp_2 = tmp;
end

Wolfram

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

↓

code[x_] := If[LessEqual[x, -0.023], (-N[Log[N[(N[Sqrt[1.0 ^ 2 + x ^ 2], $MachinePrecision] - x), $MachinePrecision]], $MachinePrecision]), If[LessEqual[x, 0.025], N[(N[(-0.16666666666666666 * N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision] + N[(N[(0.075 * N[Power[x, 5.0], $MachinePrecision]), $MachinePrecision] + N[(x + N[(-0.044642857142857144 * N[Power[x, 7.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], (-N[Log[N[(1.0 / N[(x + N[Sqrt[1.0 ^ 2 + x ^ 2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision])]]

Target

Original	17.3%
Target	28.3%
Herbie	99.9%

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

Derivation

1. Split input into 3 regimes

2. if x < -0.023

   1. Initial program 2.3%

      \[\log \left(x + \sqrt{x \cdot x + 1}\right) \]

   2. Simplified 2.3%

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

      [Start] 2.3	\[ \log \left(x + \sqrt{x \cdot x + 1}\right) \]
      +-commutative [=>] 2.3	\[ \log \left(x + \sqrt{\color{blue}{1 + x \cdot x}}\right) \]
      hypot-1-def [=>] 2.3	\[ \log \left(x + \color{blue}{\mathsf{hypot}\left(1, x\right)}\right) \]

   3. Applied egg-rr 2.4%

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

      [Start] 2.3	\[ \log \left(x + \mathsf{hypot}\left(1, x\right)\right) \]
      flip-+ [=>] 2.8	\[ \log \color{blue}{\left(\frac{x \cdot x - \mathsf{hypot}\left(1, x\right) \cdot \mathsf{hypot}\left(1, x\right)}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]
      div-sub [=>] 2.3	\[ \log \color{blue}{\left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{\mathsf{hypot}\left(1, x\right) \cdot \mathsf{hypot}\left(1, x\right)}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]
      hypot-udef [=>] 2.4	\[ \log \left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{\color{blue}{\sqrt{1 \cdot 1 + x \cdot x}} \cdot \mathsf{hypot}\left(1, x\right)}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      hypot-udef [=>] 2.3	\[ \log \left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{\sqrt{1 \cdot 1 + x \cdot x} \cdot \color{blue}{\sqrt{1 \cdot 1 + x \cdot x}}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      add-sqr-sqrt [<=] 2.4	\[ \log \left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{\color{blue}{1 \cdot 1 + x \cdot x}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      metadata-eval [=>] 2.4	\[ \log \left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{\color{blue}{1} + x \cdot x}{x - \mathsf{hypot}\left(1, x\right)}\right) \]

   4. Simplified 99.9%

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

      [Start] 2.4	\[ \log \left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{1 + x \cdot x}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      div-sub [<=] 3.3	\[ \log \color{blue}{\left(\frac{x \cdot x - \left(1 + x \cdot x\right)}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]
      +-commutative [=>] 3.3	\[ \log \left(\frac{x \cdot x - \color{blue}{\left(x \cdot x + 1\right)}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      associate--r+ [=>] 46.7	\[ \log \left(\frac{\color{blue}{\left(x \cdot x - x \cdot x\right) - 1}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      +-inverses [=>] 99.9	\[ \log \left(\frac{\color{blue}{0} - 1}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      metadata-eval [=>] 99.9	\[ \log \left(\frac{\color{blue}{-1}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]

   5. Applied egg-rr 99.9%

      \[\leadsto \color{blue}{\log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right) + 0} \]
      Proof

      [Start] 99.9	\[ \log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      *-un-lft-identity [=>] 99.9	\[ \log \color{blue}{\left(1 \cdot \frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]
      *-commutative [=>] 99.9	\[ \log \color{blue}{\left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)} \cdot 1\right)} \]
      log-prod [=>] 99.9	\[ \color{blue}{\log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right) + \log 1} \]
      metadata-eval [=>] 99.9	\[ \log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right) + \color{blue}{0} \]

   6. Simplified 99.9%

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

      [Start] 99.9	\[ \log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right) + 0 \]
      +-rgt-identity [=>] 99.9	\[ \color{blue}{\log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]
      metadata-eval [<=] 99.9	\[ \log \left(\frac{\color{blue}{\frac{1}{-1}}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      associate-/r* [<=] 99.9	\[ \log \color{blue}{\left(\frac{1}{-1 \cdot \left(x - \mathsf{hypot}\left(1, x\right)\right)}\right)} \]
      neg-mul-1 [<=] 99.9	\[ \log \left(\frac{1}{\color{blue}{-\left(x - \mathsf{hypot}\left(1, x\right)\right)}}\right) \]
      log-rec [=>] 99.9	\[ \color{blue}{-\log \left(-\left(x - \mathsf{hypot}\left(1, x\right)\right)\right)} \]
      neg-sub0 [=>] 99.9	\[ -\log \color{blue}{\left(0 - \left(x - \mathsf{hypot}\left(1, x\right)\right)\right)} \]
      sub-neg [=>] 99.9	\[ -\log \left(0 - \color{blue}{\left(x + \left(-\mathsf{hypot}\left(1, x\right)\right)\right)}\right) \]
      +-commutative [<=] 99.9	\[ -\log \left(0 - \color{blue}{\left(\left(-\mathsf{hypot}\left(1, x\right)\right) + x\right)}\right) \]
      associate--r+ [=>] 99.9	\[ -\log \color{blue}{\left(\left(0 - \left(-\mathsf{hypot}\left(1, x\right)\right)\right) - x\right)} \]
      neg-sub0 [<=] 99.9	\[ -\log \left(\color{blue}{\left(-\left(-\mathsf{hypot}\left(1, x\right)\right)\right)} - x\right) \]
      remove-double-neg [=>] 99.9	\[ -\log \left(\color{blue}{\mathsf{hypot}\left(1, x\right)} - x\right) \]

   if -0.023 < x < 0.025000000000000001

   1. Initial program 8.4%

      \[\log \left(x + \sqrt{x \cdot x + 1}\right) \]

   2. Simplified 8.4%

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

      [Start] 8.4	\[ \log \left(x + \sqrt{x \cdot x + 1}\right) \]
      +-commutative [=>] 8.4	\[ \log \left(x + \sqrt{\color{blue}{1 + x \cdot x}}\right) \]
      hypot-1-def [=>] 8.4	\[ \log \left(x + \color{blue}{\mathsf{hypot}\left(1, x\right)}\right) \]

   3. Taylor expanded in x around 0 100.0%

      \[\leadsto \color{blue}{-0.16666666666666666 \cdot {x}^{3} + \left(0.075 \cdot {x}^{5} + \left(-0.044642857142857144 \cdot {x}^{7} + x\right)\right)} \]

   if 0.025000000000000001 < x

   1. Initial program 50.5%

      \[\log \left(x + \sqrt{x \cdot x + 1}\right) \]

   2. Simplified 99.9%

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

      [Start] 50.5	\[ \log \left(x + \sqrt{x \cdot x + 1}\right) \]
      +-commutative [=>] 50.5	\[ \log \left(x + \sqrt{\color{blue}{1 + x \cdot x}}\right) \]
      hypot-1-def [=>] 99.9	\[ \log \left(x + \color{blue}{\mathsf{hypot}\left(1, x\right)}\right) \]

   3. Applied egg-rr 2.3%

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

      [Start] 99.9	\[ \log \left(x + \mathsf{hypot}\left(1, x\right)\right) \]
      flip-+ [=>] 2.4	\[ \log \color{blue}{\left(\frac{x \cdot x - \mathsf{hypot}\left(1, x\right) \cdot \mathsf{hypot}\left(1, x\right)}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]
      div-sub [=>] 2.3	\[ \log \color{blue}{\left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{\mathsf{hypot}\left(1, x\right) \cdot \mathsf{hypot}\left(1, x\right)}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]
      hypot-udef [=>] 2.3	\[ \log \left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{\color{blue}{\sqrt{1 \cdot 1 + x \cdot x}} \cdot \mathsf{hypot}\left(1, x\right)}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      hypot-udef [=>] 2.3	\[ \log \left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{\sqrt{1 \cdot 1 + x \cdot x} \cdot \color{blue}{\sqrt{1 \cdot 1 + x \cdot x}}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      add-sqr-sqrt [<=] 2.3	\[ \log \left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{\color{blue}{1 \cdot 1 + x \cdot x}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      metadata-eval [=>] 2.3	\[ \log \left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{\color{blue}{1} + x \cdot x}{x - \mathsf{hypot}\left(1, x\right)}\right) \]

   4. Simplified 2.3%

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

      [Start] 2.3	\[ \log \left(\frac{x \cdot x}{x - \mathsf{hypot}\left(1, x\right)} - \frac{1 + x \cdot x}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      div-sub [<=] 2.3	\[ \log \color{blue}{\left(\frac{x \cdot x - \left(1 + x \cdot x\right)}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]
      +-commutative [=>] 2.3	\[ \log \left(\frac{x \cdot x - \color{blue}{\left(x \cdot x + 1\right)}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      associate--r+ [=>] 2.3	\[ \log \left(\frac{\color{blue}{\left(x \cdot x - x \cdot x\right) - 1}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      +-inverses [=>] 2.3	\[ \log \left(\frac{\color{blue}{0} - 1}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      metadata-eval [=>] 2.3	\[ \log \left(\frac{\color{blue}{-1}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]

   5. Applied egg-rr 2.3%

      \[\leadsto \color{blue}{\log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right) + 0} \]
      Proof

      [Start] 2.3	\[ \log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      *-un-lft-identity [=>] 2.3	\[ \log \color{blue}{\left(1 \cdot \frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]
      *-commutative [=>] 2.3	\[ \log \color{blue}{\left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)} \cdot 1\right)} \]
      log-prod [=>] 2.3	\[ \color{blue}{\log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right) + \log 1} \]
      metadata-eval [=>] 2.3	\[ \log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right) + \color{blue}{0} \]

   6. Simplified 2.3%

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

      [Start] 2.3	\[ \log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right) + 0 \]
      +-rgt-identity [=>] 2.3	\[ \color{blue}{\log \left(\frac{-1}{x - \mathsf{hypot}\left(1, x\right)}\right)} \]
      metadata-eval [<=] 2.3	\[ \log \left(\frac{\color{blue}{\frac{1}{-1}}}{x - \mathsf{hypot}\left(1, x\right)}\right) \]
      associate-/r* [<=] 2.3	\[ \log \color{blue}{\left(\frac{1}{-1 \cdot \left(x - \mathsf{hypot}\left(1, x\right)\right)}\right)} \]
      neg-mul-1 [<=] 2.3	\[ \log \left(\frac{1}{\color{blue}{-\left(x - \mathsf{hypot}\left(1, x\right)\right)}}\right) \]
      log-rec [=>] 2.3	\[ \color{blue}{-\log \left(-\left(x - \mathsf{hypot}\left(1, x\right)\right)\right)} \]
      neg-sub0 [=>] 2.3	\[ -\log \color{blue}{\left(0 - \left(x - \mathsf{hypot}\left(1, x\right)\right)\right)} \]
      sub-neg [=>] 2.3	\[ -\log \left(0 - \color{blue}{\left(x + \left(-\mathsf{hypot}\left(1, x\right)\right)\right)}\right) \]
      +-commutative [<=] 2.3	\[ -\log \left(0 - \color{blue}{\left(\left(-\mathsf{hypot}\left(1, x\right)\right) + x\right)}\right) \]
      associate--r+ [=>] 2.3	\[ -\log \color{blue}{\left(\left(0 - \left(-\mathsf{hypot}\left(1, x\right)\right)\right) - x\right)} \]
      neg-sub0 [<=] 2.3	\[ -\log \left(\color{blue}{\left(-\left(-\mathsf{hypot}\left(1, x\right)\right)\right)} - x\right) \]
      remove-double-neg [=>] 2.3	\[ -\log \left(\color{blue}{\mathsf{hypot}\left(1, x\right)} - x\right) \]

   7. Applied egg-rr 3.2%

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

      [Start] 2.3	\[ -\log \left(\mathsf{hypot}\left(1, x\right) - x\right) \]
      flip-- [=>] 2.8	\[ -\log \color{blue}{\left(\frac{\mathsf{hypot}\left(1, x\right) \cdot \mathsf{hypot}\left(1, x\right) - x \cdot x}{\mathsf{hypot}\left(1, x\right) + x}\right)} \]
      div-inv [=>] 2.8	\[ -\log \color{blue}{\left(\left(\mathsf{hypot}\left(1, x\right) \cdot \mathsf{hypot}\left(1, x\right) - x \cdot x\right) \cdot \frac{1}{\mathsf{hypot}\left(1, x\right) + x}\right)} \]
      hypot-udef [=>] 2.9	\[ -\log \left(\left(\color{blue}{\sqrt{1 \cdot 1 + x \cdot x}} \cdot \mathsf{hypot}\left(1, x\right) - x \cdot x\right) \cdot \frac{1}{\mathsf{hypot}\left(1, x\right) + x}\right) \]
      hypot-udef [=>] 2.8	\[ -\log \left(\left(\sqrt{1 \cdot 1 + x \cdot x} \cdot \color{blue}{\sqrt{1 \cdot 1 + x \cdot x}} - x \cdot x\right) \cdot \frac{1}{\mathsf{hypot}\left(1, x\right) + x}\right) \]
      add-sqr-sqrt [<=] 3.2	\[ -\log \left(\left(\color{blue}{\left(1 \cdot 1 + x \cdot x\right)} - x \cdot x\right) \cdot \frac{1}{\mathsf{hypot}\left(1, x\right) + x}\right) \]
      metadata-eval [=>] 3.2	\[ -\log \left(\left(\left(\color{blue}{1} + x \cdot x\right) - x \cdot x\right) \cdot \frac{1}{\mathsf{hypot}\left(1, x\right) + x}\right) \]
      +-commutative [=>] 3.2	\[ -\log \left(\left(\left(1 + x \cdot x\right) - x \cdot x\right) \cdot \frac{1}{\color{blue}{x + \mathsf{hypot}\left(1, x\right)}}\right) \]

   8. Simplified 99.9%

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

      [Start] 3.2	\[ -\log \left(\left(\left(1 + x \cdot x\right) - x \cdot x\right) \cdot \frac{1}{x + \mathsf{hypot}\left(1, x\right)}\right) \]
      unpow2 [<=] 3.2	\[ -\log \left(\left(\left(1 + \color{blue}{{x}^{2}}\right) - x \cdot x\right) \cdot \frac{1}{x + \mathsf{hypot}\left(1, x\right)}\right) \]
      unpow2 [<=] 3.2	\[ -\log \left(\left(\left(1 + {x}^{2}\right) - \color{blue}{{x}^{2}}\right) \cdot \frac{1}{x + \mathsf{hypot}\left(1, x\right)}\right) \]
      associate--l+ [=>] 50.5	\[ -\log \left(\color{blue}{\left(1 + \left({x}^{2} - {x}^{2}\right)\right)} \cdot \frac{1}{x + \mathsf{hypot}\left(1, x\right)}\right) \]
      +-inverses [=>] 99.9	\[ -\log \left(\left(1 + \color{blue}{0}\right) \cdot \frac{1}{x + \mathsf{hypot}\left(1, x\right)}\right) \]
      metadata-eval [=>] 99.9	\[ -\log \left(\color{blue}{1} \cdot \frac{1}{x + \mathsf{hypot}\left(1, x\right)}\right) \]
      *-lft-identity [=>] 99.9	\[ -\log \color{blue}{\left(\frac{1}{x + \mathsf{hypot}\left(1, x\right)}\right)} \]

3. Recombined 3 regimes into one program.

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.023:\\ \;\;\;\;-\log \left(\mathsf{hypot}\left(1, x\right) - x\right)\\ \mathbf{elif}\;x \leq 0.025:\\ \;\;\;\;-0.16666666666666666 \cdot {x}^{3} + \left(0.075 \cdot {x}^{5} + \left(x + -0.044642857142857144 \cdot {x}^{7}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-\log \left(\frac{1}{x + \mathsf{hypot}\left(1, x\right)}\right)\\ \end{array} \]

Alternatives

Alternative 1
Accuracy	99.8%
Cost	13512

\[\begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{-6}:\\ \;\;\;\;-\log \left(\mathsf{hypot}\left(1, x\right) - x\right)\\ \mathbf{elif}\;x \leq 5.7 \cdot 10^{-6}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;-\log \left(\frac{1}{x + \mathsf{hypot}\left(1, x\right)}\right)\\ \end{array} \]

Alternative 2
Accuracy	99.5%
Cost	13320

\[\begin{array}{l} \mathbf{if}\;x \leq -0.8:\\ \;\;\;\;-\log \left(x \cdot -2 - \frac{0.5}{x}\right)\\ \mathbf{elif}\;x \leq 5.7 \cdot 10^{-6}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + \mathsf{hypot}\left(1, x\right)\right)\\ \end{array} \]

Alternative 3
Accuracy	99.8%
Cost	13320

\[\begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{-6}:\\ \;\;\;\;-\log \left(\mathsf{hypot}\left(1, x\right) - x\right)\\ \mathbf{elif}\;x \leq 5.7 \cdot 10^{-6}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + \mathsf{hypot}\left(1, x\right)\right)\\ \end{array} \]

Alternative 4
Accuracy	99.5%
Cost	7240

\[\begin{array}{l} \mathbf{if}\;x \leq -0.95:\\ \;\;\;\;-\log \left(x \cdot -2 - \frac{0.5}{x}\right)\\ \mathbf{elif}\;x \leq 0.96:\\ \;\;\;\;x + -0.16666666666666666 \cdot {x}^{3}\\ \mathbf{else}:\\ \;\;\;\;\log \left(x \cdot 2 + 0.5 \cdot \frac{1}{x}\right)\\ \end{array} \]

Alternative 5
Accuracy	99.3%
Cost	7048

\[\begin{array}{l} \mathbf{if}\;x \leq -1.25:\\ \;\;\;\;\log \left(\frac{-0.5}{x}\right)\\ \mathbf{elif}\;x \leq 1.26:\\ \;\;\;\;x + -0.16666666666666666 \cdot {x}^{3}\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + x\right)\\ \end{array} \]

Alternative 6
Accuracy	99.4%
Cost	7048

\[\begin{array}{l} \mathbf{if}\;x \leq -0.95:\\ \;\;\;\;-\log \left(x \cdot -2 - \frac{0.5}{x}\right)\\ \mathbf{elif}\;x \leq 1.26:\\ \;\;\;\;x + -0.16666666666666666 \cdot {x}^{3}\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + x\right)\\ \end{array} \]

Alternative 7
Accuracy	98.9%
Cost	6856

\[\begin{array}{l} \mathbf{if}\;x \leq -1.25:\\ \;\;\;\;\log \left(\frac{-0.5}{x}\right)\\ \mathbf{elif}\;x \leq 1.26:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + x\right)\\ \end{array} \]

Alternative 8
Accuracy	75.7%
Cost	6724

\[\begin{array}{l} \mathbf{if}\;x \leq 1.26:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\log \left(x + x\right)\\ \end{array} \]

Alternative 9
Accuracy	52.8%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023143 
(FPCore (x)
  :name "Hyperbolic arcsine"
  :precision binary64

  :herbie-target
  (if (< x 0.0) (log (/ -1.0 (- x (sqrt (+ (* x x) 1.0))))) (log (+ x (sqrt (+ (* x x) 1.0)))))

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