Average Error: 3.6 → 0.1
Time: 18.7s
Precision: binary64
\[\alpha > -1 \land \beta > -1\]
\[[alpha, beta]=\mathsf{sort}([alpha, beta])\]
\[\frac{\frac{\frac{\left(\left(\alpha + \beta\right) + \beta \cdot \alpha\right) + 1}{\left(\alpha + \beta\right) + 2 \cdot 1}}{\left(\alpha + \beta\right) + 2 \cdot 1}}{\left(\left(\alpha + \beta\right) + 2 \cdot 1\right) + 1} \]
↓
\[\begin{array}{l} t_0 := 2 + \left(\alpha + \beta\right)\\ \frac{\frac{1 + \alpha}{t_0}}{t_0 \cdot \frac{\alpha + \left(\beta + 3\right)}{1 + \beta}} \end{array} \]
\frac{\frac{\frac{\left(\left(\alpha + \beta\right) + \beta \cdot \alpha\right) + 1}{\left(\alpha + \beta\right) + 2 \cdot 1}}{\left(\alpha + \beta\right) + 2 \cdot 1}}{\left(\left(\alpha + \beta\right) + 2 \cdot 1\right) + 1}
↓
\begin{array}{l}
t_0 := 2 + \left(\alpha + \beta\right)\\
\frac{\frac{1 + \alpha}{t_0}}{t_0 \cdot \frac{\alpha + \left(\beta + 3\right)}{1 + \beta}}
\end{array}

(FPCore (alpha beta)
 :precision binary64
 (/
  (/
   (/ (+ (+ (+ alpha beta) (* beta alpha)) 1.0) (+ (+ alpha beta) (* 2.0 1.0)))
   (+ (+ alpha beta) (* 2.0 1.0)))
  (+ (+ (+ alpha beta) (* 2.0 1.0)) 1.0)))
↓
(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (+ 2.0 (+ alpha beta))))
   (/ (/ (+ 1.0 alpha) t_0) (* t_0 (/ (+ alpha (+ beta 3.0)) (+ 1.0 beta))))))
double code(double alpha, double beta) {
	return (((((alpha + beta) + (beta * alpha)) + 1.0) / ((alpha + beta) + (2.0 * 1.0))) / ((alpha + beta) + (2.0 * 1.0))) / (((alpha + beta) + (2.0 * 1.0)) + 1.0);
}
↓
double code(double alpha, double beta) {
	double t_0 = 2.0 + (alpha + beta);
	return ((1.0 + alpha) / t_0) / (t_0 * ((alpha + (beta + 3.0)) / (1.0 + beta)));
}

Error

Bits error versus alpha

Bits error versus beta

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 3.6

    \[\frac{\frac{\frac{\left(\left(\alpha + \beta\right) + \beta \cdot \alpha\right) + 1}{\left(\alpha + \beta\right) + 2 \cdot 1}}{\left(\alpha + \beta\right) + 2 \cdot 1}}{\left(\left(\alpha + \beta\right) + 2 \cdot 1\right) + 1} \]

  2. Simplified2.0

    \[\leadsto \color{blue}{\frac{\left(\alpha + 1\right) \cdot \frac{\beta + 1}{\left(\left(\alpha + \beta\right) + 2\right) \cdot \left(\left(\alpha + \beta\right) + 2\right)}}{\alpha + \left(\beta + 3\right)}} \]

  3. Applied *-un-lft-identity_binary642.0

    \[\leadsto \frac{\left(\alpha + 1\right) \cdot \frac{\color{blue}{1 \cdot \left(\beta + 1\right)}}{\left(\left(\alpha + \beta\right) + 2\right) \cdot \left(\left(\alpha + \beta\right) + 2\right)}}{\alpha + \left(\beta + 3\right)} \]

  4. Applied times-frac_binary640.1

    \[\leadsto \frac{\left(\alpha + 1\right) \cdot \color{blue}{\left(\frac{1}{\left(\alpha + \beta\right) + 2} \cdot \frac{\beta + 1}{\left(\alpha + \beta\right) + 2}\right)}}{\alpha + \left(\beta + 3\right)} \]

  5. Applied associate-*r*_binary640.1

    \[\leadsto \frac{\color{blue}{\left(\left(\alpha + 1\right) \cdot \frac{1}{\left(\alpha + \beta\right) + 2}\right) \cdot \frac{\beta + 1}{\left(\alpha + \beta\right) + 2}}}{\alpha + \left(\beta + 3\right)} \]

  6. Simplified0.1

    \[\leadsto \frac{\color{blue}{\frac{1 + \alpha}{2 + \left(\alpha + \beta\right)}} \cdot \frac{\beta + 1}{\left(\alpha + \beta\right) + 2}}{\alpha + \left(\beta + 3\right)} \]

  7. Applied add-exp-log_binary641.4

    \[\leadsto \frac{\frac{1 + \alpha}{2 + \left(\alpha + \beta\right)} \cdot \frac{\beta + 1}{\color{blue}{e^{\log \left(\left(\alpha + \beta\right) + 2\right)}}}}{\alpha + \left(\beta + 3\right)} \]

  8. Applied add-exp-log_binary640.2

    \[\leadsto \frac{\frac{1 + \alpha}{2 + \left(\alpha + \beta\right)} \cdot \frac{\color{blue}{e^{\log \left(\beta + 1\right)}}}{e^{\log \left(\left(\alpha + \beta\right) + 2\right)}}}{\alpha + \left(\beta + 3\right)} \]

  9. Applied div-exp_binary640.2

    \[\leadsto \frac{\frac{1 + \alpha}{2 + \left(\alpha + \beta\right)} \cdot \color{blue}{e^{\log \left(\beta + 1\right) - \log \left(\left(\alpha + \beta\right) + 2\right)}}}{\alpha + \left(\beta + 3\right)} \]

  10. Simplified0.2

    \[\leadsto \frac{\frac{1 + \alpha}{2 + \left(\alpha + \beta\right)} \cdot e^{\color{blue}{\mathsf{log1p}\left(\beta\right) - \log \left(2 + \left(\beta + \alpha\right)\right)}}}{\alpha + \left(\beta + 3\right)} \]

  11. Applied associate-/l*_binary640.2

    \[\leadsto \color{blue}{\frac{\frac{1 + \alpha}{2 + \left(\alpha + \beta\right)}}{\frac{\alpha + \left(\beta + 3\right)}{e^{\mathsf{log1p}\left(\beta\right) - \log \left(2 + \left(\beta + \alpha\right)\right)}}}} \]

  12. Simplified0.1

    \[\leadsto \frac{\frac{1 + \alpha}{2 + \left(\alpha + \beta\right)}}{\color{blue}{\frac{\alpha + \left(\beta + 3\right)}{\frac{\beta + 1}{2 + \left(\alpha + \beta\right)}}}} \]

  13. Applied associate-/r/_binary640.1

    \[\leadsto \frac{\frac{1 + \alpha}{2 + \left(\alpha + \beta\right)}}{\color{blue}{\frac{\alpha + \left(\beta + 3\right)}{\beta + 1} \cdot \left(2 + \left(\alpha + \beta\right)\right)}} \]

  14. Final simplification0.1

    \[\leadsto \frac{\frac{1 + \alpha}{2 + \left(\alpha + \beta\right)}}{\left(2 + \left(\alpha + \beta\right)\right) \cdot \frac{\alpha + \left(\beta + 3\right)}{1 + \beta}} \]

Reproduce

herbie shell --seed 2022160 
(FPCore (alpha beta)
  :name "Octave 3.8, jcobi/3"
  :precision binary64
  :pre (and (> alpha -1.0) (> beta -1.0))
  (/ (/ (/ (+ (+ (+ alpha beta) (* beta alpha)) 1.0) (+ (+ alpha beta) (* 2.0 1.0))) (+ (+ alpha beta) (* 2.0 1.0))) (+ (+ (+ alpha beta) (* 2.0 1.0)) 1.0)))