Average Error: 2.9 → 0.0
Time: 2.2s
Precision: binary64
\[x + \frac{y}{1.1283791670955126 \cdot e^{z} - x \cdot y}\]
↓
\[x + \frac{1}{e^{z} \cdot \frac{1.1283791670955126}{y} - x}\]
x + \frac{y}{1.1283791670955126 \cdot e^{z} - x \cdot y}
↓
x + \frac{1}{e^{z} \cdot \frac{1.1283791670955126}{y} - x}
(FPCore (x y z)
 :precision binary64
 (+ x (/ y (- (* 1.1283791670955126 (exp z)) (* x y)))))
↓
(FPCore (x y z)
 :precision binary64
 (+ x (/ 1.0 (- (* (exp z) (/ 1.1283791670955126 y)) x))))
double code(double x, double y, double z) {
	return x + (y / ((1.1283791670955126 * exp(z)) - (x * y)));
}
↓
double code(double x, double y, double z) {
	return x + (1.0 / ((exp(z) * (1.1283791670955126 / y)) - x));
}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Target

Original2.9
Target0.0
Herbie0.0
\[x + \frac{1}{\frac{1.1283791670955126}{y} \cdot e^{z} - x}\]

Derivation

  1. Initial program 2.9

    \[x + \frac{y}{1.1283791670955126 \cdot e^{z} - x \cdot y}\]
  2. Using strategy rm

  3. Applied clear-num_binary642.9

    \[\leadsto x + \color{blue}{\frac{1}{\frac{1.1283791670955126 \cdot e^{z} - x \cdot y}{y}}}\]

  4. Simplified0.0

    \[\leadsto x + \frac{1}{\color{blue}{\frac{e^{z}}{y \cdot 0.886226925452758} - x}}\]
  5. Using strategy rm

  6. Applied div-inv_binary640.0

    \[\leadsto x + \frac{1}{\color{blue}{e^{z} \cdot \frac{1}{y \cdot 0.886226925452758}} - x}\]

  7. Simplified0.0

    \[\leadsto x + \frac{1}{e^{z} \cdot \color{blue}{\frac{1.1283791670955126}{y}} - x}\]

  8. Final simplification0.0

    \[\leadsto x + \frac{1}{e^{z} \cdot \frac{1.1283791670955126}{y} - x}\]

Reproduce

herbie shell --seed 2020224 
(FPCore (x y z)
  :name "Numeric.SpecFunctions:invErfc from math-functions-0.1.5.2, A"
  :precision binary64

  :herbie-target
  (+ x (/ 1.0 (- (* (/ 1.1283791670955126 y) (exp z)) x)))

  (+ x (/ y (- (* 1.1283791670955126 (exp z)) (* x y)))))