expq2 (section 3.11)

Average Accuracy: 35.8% → 99.4%

Time: 5.9s

Precision: binary64

Cost: 13120

Math

\[\frac{e^{x}}{e^{x} - 1} \]

↓

\[\frac{1}{\mathsf{expm1}\left(x\right)} \cdot e^{x} \]

FPCore

(FPCore (x) :precision binary64 (/ (exp x) (- (exp x) 1.0)))

↓

(FPCore (x) :precision binary64 (* (/ 1.0 (expm1 x)) (exp x)))

C

double code(double x) {
	return exp(x) / (exp(x) - 1.0);
}

↓

double code(double x) {
	return (1.0 / expm1(x)) * exp(x);
}

Java

public static double code(double x) {
	return Math.exp(x) / (Math.exp(x) - 1.0);
}

↓

public static double code(double x) {
	return (1.0 / Math.expm1(x)) * Math.exp(x);
}

Python

def code(x):
	return math.exp(x) / (math.exp(x) - 1.0)

↓

def code(x):
	return (1.0 / math.expm1(x)) * math.exp(x)

Julia

function code(x)
	return Float64(exp(x) / Float64(exp(x) - 1.0))
end

↓

function code(x)
	return Float64(Float64(1.0 / expm1(x)) * exp(x))
end

Wolfram

code[x_] := N[(N[Exp[x], $MachinePrecision] / N[(N[Exp[x], $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]

↓

code[x_] := N[(N[(1.0 / N[(Exp[x] - 1), $MachinePrecision]), $MachinePrecision] * N[Exp[x], $MachinePrecision]), $MachinePrecision]

Target

Original	35.8%
Target	36.4%
Herbie	99.4%

\[\frac{1}{1 - e^{-x}} \]

Derivation

1. Initial program 42.6%

   \[\frac{e^{x}}{e^{x} - 1} \]

2. Simplified 100.0%

   \[\leadsto \color{blue}{\frac{e^{x}}{\mathsf{expm1}\left(x\right)}} \]

3. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\frac{1}{\mathsf{expm1}\left(x\right)} \cdot e^{x}} \]

4. Final simplification 100.0%

   \[\leadsto \frac{1}{\mathsf{expm1}\left(x\right)} \cdot e^{x} \]

Alternatives

Alternative 1
Accuracy	99.4%
Cost	12992

\[\frac{e^{x}}{\mathsf{expm1}\left(x\right)} \]

Alternative 2
Accuracy	98.6%
Cost	7104

\[e^{x} \cdot \left(\left(x \cdot 0.08333333333333333 + \frac{1}{x}\right) - 0.5\right) \]

Alternative 3
Accuracy	97.6%
Cost	6592

\[\frac{e^{x}}{x} \]

Alternative 4
Accuracy	98.7%
Cost	708

\[\begin{array}{l} \mathbf{if}\;x \leq -350:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;\left(x \cdot 0.08333333333333333 + \frac{1}{x}\right) + 0.5\\ \end{array} \]

Alternative 5
Accuracy	98.4%
Cost	580

\[\begin{array}{l} \mathbf{if}\;x \leq -2:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;1.5 + \left(\frac{1}{x} + -1\right)\\ \end{array} \]

Alternative 6
Accuracy	97.9%
Cost	452

\[\begin{array}{l} \mathbf{if}\;x \leq -1:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;1 + \frac{1}{x}\\ \end{array} \]

Alternative 7
Accuracy	98.4%
Cost	452

\[\begin{array}{l} \mathbf{if}\;x \leq -2:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x} + 0.5\\ \end{array} \]

Alternative 8
Accuracy	97.7%
Cost	324

\[\begin{array}{l} \mathbf{if}\;x \leq -350:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \]

Alternative 9
Accuracy	3.3%
Cost	64

\[0.5 \]

Alternative 10
Accuracy	3.9%
Cost	64

\[1 \]

Alternative 11
Accuracy	34.7%
Cost	64

\[0 \]

Reproduce

herbie shell --seed 2023157 -o generate:proofs
(FPCore (x)
  :name "expq2 (section 3.11)"
  :precision binary64

  :herbie-target
  (/ 1.0 (- 1.0 (exp (- x))))

  (/ (exp x) (- (exp x) 1.0)))