Octave 3.8, jcobi/2

Average Error: 23.9 → 1.4

Time: 15.1s

Precision: binary64

\[\left(\alpha > -1 \land \beta > -1\right) \land i > 0\]

Math

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

↓

\[\begin{array}{l} t_0 := \left(\alpha + \beta\right) + 2 \cdot i\\ \mathbf{if}\;\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{t_0}}{2 + t_0} \leq -0.9995:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(i, 4, \mathsf{fma}\left(2, \beta, 2\right)\right)}{\alpha}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{\sqrt[3]{{\left(\mathsf{log1p}\left(\left(\alpha + \beta\right) \cdot \frac{\frac{\beta - \alpha}{\beta + \left(\alpha + \mathsf{fma}\left(2, i, 2\right)\right)}}{\mathsf{fma}\left(2, i, \alpha + \beta\right)}\right)\right)}^{3}}}}{2}\\ \end{array} \]

FPCore

(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))))
   (if (<= (/ (/ (* (+ alpha beta) (- beta alpha)) t_0) (+ 2.0 t_0)) -0.9995)
     (/ (/ (fma i 4.0 (fma 2.0 beta 2.0)) alpha) 2.0)
     (/
      (exp
       (cbrt
        (pow
         (log1p
          (*
           (+ alpha beta)
           (/
            (/ (- beta alpha) (+ beta (+ alpha (fma 2.0 i 2.0))))
            (fma 2.0 i (+ alpha beta)))))
         3.0)))
      2.0))))

C

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 tmp;
	if (((((alpha + beta) * (beta - alpha)) / t_0) / (2.0 + t_0)) <= -0.9995) {
		tmp = (fma(i, 4.0, fma(2.0, beta, 2.0)) / alpha) / 2.0;
	} else {
		tmp = exp(cbrt(pow(log1p(((alpha + beta) * (((beta - alpha) / (beta + (alpha + fma(2.0, i, 2.0)))) / fma(2.0, i, (alpha + beta))))), 3.0))) / 2.0;
	}
	return tmp;
}

Julia

function code(alpha, beta, i)
	return Float64(Float64(Float64(Float64(Float64(Float64(alpha + beta) * Float64(beta - alpha)) / Float64(Float64(alpha + beta) + Float64(2.0 * i))) / Float64(Float64(Float64(alpha + beta) + Float64(2.0 * i)) + 2.0)) + 1.0) / 2.0)
end

↓

function code(alpha, beta, i)
	t_0 = Float64(Float64(alpha + beta) + Float64(2.0 * i))
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(alpha + beta) * Float64(beta - alpha)) / t_0) / Float64(2.0 + t_0)) <= -0.9995)
		tmp = Float64(Float64(fma(i, 4.0, fma(2.0, beta, 2.0)) / alpha) / 2.0);
	else
		tmp = Float64(exp(cbrt((log1p(Float64(Float64(alpha + beta) * Float64(Float64(Float64(beta - alpha) / Float64(beta + Float64(alpha + fma(2.0, i, 2.0)))) / fma(2.0, i, Float64(alpha + beta))))) ^ 3.0))) / 2.0);
	end
	return tmp
end

Wolfram

code[alpha_, beta_, i_] := N[(N[(N[(N[(N[(N[(alpha + beta), $MachinePrecision] * N[(beta - alpha), $MachinePrecision]), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision] / 2.0), $MachinePrecision]

↓

code[alpha_, beta_, i_] := Block[{t$95$0 = N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(N[(N[(alpha + beta), $MachinePrecision] * N[(beta - alpha), $MachinePrecision]), $MachinePrecision] / t$95$0), $MachinePrecision] / N[(2.0 + t$95$0), $MachinePrecision]), $MachinePrecision], -0.9995], N[(N[(N[(i * 4.0 + N[(2.0 * beta + 2.0), $MachinePrecision]), $MachinePrecision] / alpha), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[Exp[N[Power[N[Power[N[Log[1 + N[(N[(alpha + beta), $MachinePrecision] * N[(N[(N[(beta - alpha), $MachinePrecision] / N[(beta + N[(alpha + N[(2.0 * i + 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(2.0 * i + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 3.0], $MachinePrecision], 1/3], $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]]]

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)) < -0.99950000000000006

   1. Initial program 62.0

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

   2. Simplified 55.2

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

   3. Taylor expanded in alpha around inf 6.0

      \[\leadsto \frac{\color{blue}{\frac{\left(\beta + -1 \cdot \beta\right) - -1 \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)}{\alpha}}}{2} \]

   4. Simplified 6.0

      \[\leadsto \frac{\color{blue}{\frac{\mathsf{fma}\left(i, 4, \mathsf{fma}\left(2, \beta, 2\right)\right)}{\alpha}}}{2} \]

   if -0.99950000000000006 < (/.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. Simplified 9.1

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

   3. Applied egg-rr 0.0

      \[\leadsto \frac{\color{blue}{e^{\mathsf{log1p}\left(\left(\beta + \alpha\right) \cdot \frac{\frac{\beta - \alpha}{\alpha + \mathsf{fma}\left(2, i, \beta\right)}}{\beta + \left(\alpha + \mathsf{fma}\left(2, i, 2\right)\right)}\right)}}}{2} \]

   4. Applied egg-rr 0.0

      \[\leadsto \frac{e^{\mathsf{log1p}\left(\color{blue}{\frac{\beta + \alpha}{\frac{\left(\beta + \alpha\right) + \mathsf{fma}\left(2, i, 2\right)}{\beta - \alpha} \cdot \left(\alpha + \mathsf{fma}\left(2, i, \beta\right)\right)}}\right)}}{2} \]

   5. Applied egg-rr 0.0

      \[\leadsto \frac{e^{\color{blue}{\sqrt[3]{{\left(\mathsf{log1p}\left(\left(\beta + \alpha\right) \cdot \frac{\frac{\beta - \alpha}{\beta + \left(\alpha + \mathsf{fma}\left(2, i, 2\right)\right)}}{\mathsf{fma}\left(2, i, \beta + \alpha\right)}\right)\right)}^{3}}}}}{2} \]
3. Recombined 2 regimes into one program.

4. Final simplification 1.4

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{\left(\alpha + \beta\right) + 2 \cdot i}}{2 + \left(\left(\alpha + \beta\right) + 2 \cdot i\right)} \leq -0.9995:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(i, 4, \mathsf{fma}\left(2, \beta, 2\right)\right)}{\alpha}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{\sqrt[3]{{\left(\mathsf{log1p}\left(\left(\alpha + \beta\right) \cdot \frac{\frac{\beta - \alpha}{\beta + \left(\alpha + \mathsf{fma}\left(2, i, 2\right)\right)}}{\mathsf{fma}\left(2, i, \alpha + \beta\right)}\right)\right)}^{3}}}}{2}\\ \end{array} \]

Reproduce

herbie shell --seed 2022210 
(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))