?

Average Accuracy: 5.4% → 65.7%
Time: 1.3s
Precision: binary64
Cost: 6464

?

\[-0.00017 < x\]
\[e^{x} - 1 \]
\[\mathsf{expm1}\left(x\right) \]
(FPCore (x) :precision binary64 (- (exp x) 1.0))
(FPCore (x) :precision binary64 (expm1 x))
double code(double x) {
	return exp(x) - 1.0;
}
double code(double x) {
	return expm1(x);
}
public static double code(double x) {
	return Math.exp(x) - 1.0;
}
public static double code(double x) {
	return Math.expm1(x);
}
def code(x):
	return math.exp(x) - 1.0
def code(x):
	return math.expm1(x)
function code(x)
	return Float64(exp(x) - 1.0)
end
function code(x)
	return expm1(x)
end
code[x_] := N[(N[Exp[x], $MachinePrecision] - 1.0), $MachinePrecision]
code[x_] := N[(Exp[x] - 1), $MachinePrecision]
e^{x} - 1
\mathsf{expm1}\left(x\right)

Error?

Try it out?

Your Program's Arguments

Results

Enter valid numbers for all inputs

Target

Original5.4%
Target65.9%
Herbie65.7%
\[x \cdot \left(\left(1 + \frac{x}{2}\right) + \frac{x \cdot x}{6}\right) \]

Derivation?

  1. Initial program 4.6%

    \[e^{x} - 1 \]
  2. Simplified64.8%

    \[\leadsto \color{blue}{\mathsf{expm1}\left(x\right)} \]
    Step-by-step derivation

    [Start]4.6

    \[ e^{x} - 1 \]

    expm1-def [=>]64.8

    \[ \color{blue}{\mathsf{expm1}\left(x\right)} \]
  3. Final simplification64.8%

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

Reproduce?

herbie shell --seed 2023157 
(FPCore (x)
  :name "expm1 (example 3.7)"
  :precision binary64
  :pre (< -0.00017 x)

  :herbie-target
  (* x (+ (+ 1.0 (/ x 2.0)) (/ (* x x) 6.0)))

  (- (exp x) 1.0))