expq2 (section 3.11)

Average Error: 41.6 → 0.0

Time: 2.9s

Precision: binary64

Math

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

↓

\[{\left(-\mathsf{expm1}\left(-x\right)\right)}^{-1} \]

FPCore

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

↓

(FPCore (x) :precision binary64 (pow (- (expm1 (- x))) -1.0))

C

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

↓

double code(double x) {
	return pow(-expm1(-x), -1.0);
}

Java

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

↓

public static double code(double x) {
	return Math.pow(-Math.expm1(-x), -1.0);
}

Python

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

↓

def code(x):
	return math.pow(-math.expm1(-x), -1.0)

Julia

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

↓

function code(x)
	return Float64(-expm1(Float64(-x))) ^ -1.0
end

Wolfram

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

↓

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

Target

Original	41.6
Target	41.2
Herbie	0.0

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

Derivation

1. Initial program 41.6

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

2. Simplified 0.4

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

3. Applied egg-rr 0.4

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

4. Taylor expanded in x around inf 41.6

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

5. Simplified 0.0

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

6. Final simplification 0.0

   \[\leadsto {\left(-\mathsf{expm1}\left(-x\right)\right)}^{-1} \]

Reproduce

herbie shell --seed 2022206 
(FPCore (x)
  :name "expq2 (section 3.11)"
  :precision binary64

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

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