expq2 (section 3.11)

Average Error: 41.9 → 0.5

Time: 2.6s

Precision: binary64

Math

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

↓

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

FPCore

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

↓

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

C

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

↓

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

Java

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

↓

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

Python

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

↓

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

Julia

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

↓

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

Wolfram

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

↓

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

Error

Bits error versus x

Target

Original	41.9
Target	41.5
Herbie	0.5

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

Derivation

1. Initial program 41.9

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

2. Simplified 0.5

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

3. Final simplification 0.5

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

Reproduce

herbie shell --seed 2022165 
(FPCore (x)
  :name "expq2 (section 3.11)"
  :precision binary64

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

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