Average Error: 23.4 → 1.6
Time: 15.5s
Precision: binary64
\[\left(\alpha > -1 \land \beta > -1\right) \land i > 0\]
\[\frac{\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{\left(\alpha + \beta\right) + 2 \cdot i}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) + 2} + 1}{2} \]
\[\begin{array}{l} t_0 := \left(\alpha + \beta\right) + 2 \cdot i\\ t_1 := \mathsf{fma}\left(2, i, \alpha + \beta\right)\\ \mathbf{if}\;\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{t_0}}{2 + t_0} \leq -1:\\ \;\;\;\;\frac{\frac{\beta \cdot 2 + \left(2 + i \cdot 4\right)}{\alpha}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\alpha + \beta, \frac{\beta - \alpha}{-t_1} \cdot \frac{-1}{2 + t_1}, 1\right)}{2}\\ \end{array} \]
\frac{\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{\left(\alpha + \beta\right) + 2 \cdot i}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) + 2} + 1}{2}
\begin{array}{l}
t_0 := \left(\alpha + \beta\right) + 2 \cdot i\\
t_1 := \mathsf{fma}\left(2, i, \alpha + \beta\right)\\
\mathbf{if}\;\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{t_0}}{2 + t_0} \leq -1:\\
\;\;\;\;\frac{\frac{\beta \cdot 2 + \left(2 + i \cdot 4\right)}{\alpha}}{2}\\

\mathbf{else}:\\
\;\;\;\;\frac{\mathsf{fma}\left(\alpha + \beta, \frac{\beta - \alpha}{-t_1} \cdot \frac{-1}{2 + t_1}, 1\right)}{2}\\


\end{array}
(FPCore (alpha beta i)
 :precision binary64
 (/
  (+
   (/
    (/ (* (+ alpha beta) (- beta alpha)) (+ (+ alpha beta) (* 2.0 i)))
    (+ (+ (+ alpha beta) (* 2.0 i)) 2.0))
   1.0)
  2.0))
(FPCore (alpha beta i)
 :precision binary64
 (let* ((t_0 (+ (+ alpha beta) (* 2.0 i))) (t_1 (fma 2.0 i (+ alpha beta))))
   (if (<= (/ (/ (* (+ alpha beta) (- beta alpha)) t_0) (+ 2.0 t_0)) -1.0)
     (/ (/ (+ (* beta 2.0) (+ 2.0 (* i 4.0))) alpha) 2.0)
     (/
      (fma
       (+ alpha beta)
       (* (/ (- beta alpha) (- t_1)) (/ -1.0 (+ 2.0 t_1)))
       1.0)
      2.0))))
double code(double alpha, double beta, double i) {
	return (((((alpha + beta) * (beta - alpha)) / ((alpha + beta) + (2.0 * i))) / (((alpha + beta) + (2.0 * i)) + 2.0)) + 1.0) / 2.0;
}
double code(double alpha, double beta, double i) {
	double t_0 = (alpha + beta) + (2.0 * i);
	double t_1 = fma(2.0, i, (alpha + beta));
	double tmp;
	if (((((alpha + beta) * (beta - alpha)) / t_0) / (2.0 + t_0)) <= -1.0) {
		tmp = (((beta * 2.0) + (2.0 + (i * 4.0))) / alpha) / 2.0;
	} else {
		tmp = fma((alpha + beta), (((beta - alpha) / -t_1) * (-1.0 / (2.0 + t_1))), 1.0) / 2.0;
	}
	return tmp;
}

Error

Bits error versus alpha

Bits error versus beta

Bits error versus i

Derivation

  1. Split input into 2 regimes
  2. if (/.f64 (/.f64 (*.f64 (+.f64 alpha beta) (-.f64 beta alpha)) (+.f64 (+.f64 alpha beta) (*.f64 2 i))) (+.f64 (+.f64 (+.f64 alpha beta) (*.f64 2 i)) 2)) < -1

    1. Initial program 63.3

      \[\frac{\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{\left(\alpha + \beta\right) + 2 \cdot i}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) + 2} + 1}{2} \]
    2. Simplified56.3

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\alpha + \beta, \frac{\beta - \alpha}{\mathsf{fma}\left(2, i, \alpha + \beta\right) \cdot \left(2 + \mathsf{fma}\left(2, i, \alpha + \beta\right)\right)}, 1\right)}{2}} \]
    3. Taylor expanded in alpha around inf 5.4

      \[\leadsto \frac{\color{blue}{\frac{2 \cdot \beta + \left(2 + 4 \cdot i\right)}{\alpha}}}{2} \]

    if -1 < (/.f64 (/.f64 (*.f64 (+.f64 alpha beta) (-.f64 beta alpha)) (+.f64 (+.f64 alpha beta) (*.f64 2 i))) (+.f64 (+.f64 (+.f64 alpha beta) (*.f64 2 i)) 2))

    1. Initial program 12.6

      \[\frac{\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{\left(\alpha + \beta\right) + 2 \cdot i}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) + 2} + 1}{2} \]
    2. Simplified9.2

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\alpha + \beta, \frac{\beta - \alpha}{\mathsf{fma}\left(2, i, \alpha + \beta\right) \cdot \left(2 + \mathsf{fma}\left(2, i, \alpha + \beta\right)\right)}, 1\right)}{2}} \]
    3. Applied egg-rr0.5

      \[\leadsto \frac{\mathsf{fma}\left(\alpha + \beta, \color{blue}{\frac{\beta - \alpha}{\mathsf{fma}\left(2, i, \beta + \alpha\right)} \cdot \frac{1}{2 + \mathsf{fma}\left(2, i, \beta + \alpha\right)}}, 1\right)}{2} \]
    4. Applied egg-rr0.5

      \[\leadsto \frac{\mathsf{fma}\left(\alpha + \beta, \color{blue}{\left(-\frac{\beta - \alpha}{-\mathsf{fma}\left(2, i, \alpha + \beta\right)}\right)} \cdot \frac{1}{2 + \mathsf{fma}\left(2, i, \beta + \alpha\right)}, 1\right)}{2} \]
  3. Recombined 2 regimes into one program.
  4. Final simplification1.6

    \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{\left(\alpha + \beta\right) + 2 \cdot i}}{2 + \left(\left(\alpha + \beta\right) + 2 \cdot i\right)} \leq -1:\\ \;\;\;\;\frac{\frac{\beta \cdot 2 + \left(2 + i \cdot 4\right)}{\alpha}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\alpha + \beta, \frac{\beta - \alpha}{-\mathsf{fma}\left(2, i, \alpha + \beta\right)} \cdot \frac{-1}{2 + \mathsf{fma}\left(2, i, \alpha + \beta\right)}, 1\right)}{2}\\ \end{array} \]

Reproduce

herbie shell --seed 2022130 
(FPCore (alpha beta i)
  :name "Octave 3.8, jcobi/2"
  :precision binary64
  :pre (and (and (> alpha -1.0) (> beta -1.0)) (> i 0.0))
  (/ (+ (/ (/ (* (+ alpha beta) (- beta alpha)) (+ (+ alpha beta) (* 2.0 i))) (+ (+ (+ alpha beta) (* 2.0 i)) 2.0)) 1.0) 2.0))