Octave 3.8, jcobi/2

Percentage Accurate: 64.3% → 97.7%

Time: 23.8s

Precision: binary64

Cost: 55876

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

Math

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

↓

\[\begin{array}{l} t_0 := \frac{\mathsf{fma}\left(i, 4, \mathsf{fma}\left(\beta, 2, 2\right)\right)}{\alpha}\\ t_1 := \left(\alpha + \beta\right) + 2 \cdot i\\ t_2 := 2 + t_1\\ \mathbf{if}\;\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{t_1}}{t_2} \leq -0.5:\\ \;\;\;\;\frac{\frac{\beta}{\alpha} \cdot \frac{\beta}{\alpha} + \left(t_0 + \left(\frac{\beta + \mathsf{fma}\left(2, i, 2\right)}{\alpha} \cdot \frac{\mathsf{fma}\left(2, i, \beta\right)}{\alpha} - t_0 \cdot t_0\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\left(\beta - \alpha\right) \cdot \frac{\beta}{\beta + 2 \cdot i}}{t_2} + 1}{2}\\ \end{array} \]

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 (/ (fma i 4.0 (fma beta 2.0 2.0)) alpha))
        (t_1 (+ (+ alpha beta) (* 2.0 i)))
        (t_2 (+ 2.0 t_1)))
   (if (<= (/ (/ (* (+ alpha beta) (- beta alpha)) t_1) t_2) -0.5)
     (/
      (+
       (* (/ beta alpha) (/ beta alpha))
       (+
        t_0
        (-
         (* (/ (+ beta (fma 2.0 i 2.0)) alpha) (/ (fma 2.0 i beta) alpha))
         (* t_0 t_0))))
      2.0)
     (/ (+ (/ (* (- beta alpha) (/ beta (+ beta (* 2.0 i)))) t_2) 1.0) 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 = fma(i, 4.0, fma(beta, 2.0, 2.0)) / alpha;
	double t_1 = (alpha + beta) + (2.0 * i);
	double t_2 = 2.0 + t_1;
	double tmp;
	if (((((alpha + beta) * (beta - alpha)) / t_1) / t_2) <= -0.5) {
		tmp = (((beta / alpha) * (beta / alpha)) + (t_0 + ((((beta + fma(2.0, i, 2.0)) / alpha) * (fma(2.0, i, beta) / alpha)) - (t_0 * t_0)))) / 2.0;
	} else {
		tmp = ((((beta - alpha) * (beta / (beta + (2.0 * i)))) / t_2) + 1.0) / 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(fma(i, 4.0, fma(beta, 2.0, 2.0)) / alpha)
	t_1 = Float64(Float64(alpha + beta) + Float64(2.0 * i))
	t_2 = Float64(2.0 + t_1)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(alpha + beta) * Float64(beta - alpha)) / t_1) / t_2) <= -0.5)
		tmp = Float64(Float64(Float64(Float64(beta / alpha) * Float64(beta / alpha)) + Float64(t_0 + Float64(Float64(Float64(Float64(beta + fma(2.0, i, 2.0)) / alpha) * Float64(fma(2.0, i, beta) / alpha)) - Float64(t_0 * t_0)))) / 2.0);
	else
		tmp = Float64(Float64(Float64(Float64(Float64(beta - alpha) * Float64(beta / Float64(beta + Float64(2.0 * i)))) / t_2) + 1.0) / 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[(i * 4.0 + N[(beta * 2.0 + 2.0), $MachinePrecision]), $MachinePrecision] / alpha), $MachinePrecision]}, Block[{t$95$1 = N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 + t$95$1), $MachinePrecision]}, If[LessEqual[N[(N[(N[(N[(alpha + beta), $MachinePrecision] * N[(beta - alpha), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision] / t$95$2), $MachinePrecision], -0.5], N[(N[(N[(N[(beta / alpha), $MachinePrecision] * N[(beta / alpha), $MachinePrecision]), $MachinePrecision] + N[(t$95$0 + N[(N[(N[(N[(beta + N[(2.0 * i + 2.0), $MachinePrecision]), $MachinePrecision] / alpha), $MachinePrecision] * N[(N[(2.0 * i + beta), $MachinePrecision] / alpha), $MachinePrecision]), $MachinePrecision] - N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(N[(N[(N[(beta - alpha), $MachinePrecision] * N[(beta / N[(beta + N[(2.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t$95$2), $MachinePrecision] + 1.0), $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)) < -0.5

   1. Initial program 2.6%

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

   2. Taylor expanded in alpha around inf 77.3%

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

   3. Simplified 91.1%

      \[\leadsto \frac{\color{blue}{\left(0 + \frac{\beta}{\alpha} \cdot \frac{\beta}{\alpha}\right) - \left(\left(\frac{\mathsf{fma}\left(i, 4, \mathsf{fma}\left(\beta, 2, 2\right)\right)}{\alpha} \cdot \frac{\mathsf{fma}\left(i, 4, \mathsf{fma}\left(\beta, 2, 2\right)\right)}{\alpha} - \frac{\beta + \mathsf{fma}\left(2, i, 2\right)}{\alpha} \cdot \frac{\mathsf{fma}\left(2, i, \beta\right)}{\alpha}\right) - \frac{\mathsf{fma}\left(i, 4, \mathsf{fma}\left(\beta, 2, 2\right)\right)}{\alpha}\right)}}{2} \]
      Step-by-step derivation

      [Start] 77.3	\[ \frac{\left(\frac{\beta}{\alpha} + \left(-1 \cdot \frac{\beta}{\alpha} + \frac{{\beta}^{2}}{{\alpha}^{2}}\right)\right) - \left(-1 \cdot \frac{4 \cdot i + \left(2 + 2 \cdot \beta\right)}{\alpha} + \left(-1 \cdot \frac{\left(\beta + 2 \cdot i\right) \cdot \left(\beta + \left(2 + 2 \cdot i\right)\right)}{{\alpha}^{2}} + \frac{\left(\left(\beta + -1 \cdot \beta\right) - -1 \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)\right) \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)}{{\alpha}^{2}}\right)\right)}{2} \]
      associate-+r+ [=>] 77.3	\[ \frac{\color{blue}{\left(\left(\frac{\beta}{\alpha} + -1 \cdot \frac{\beta}{\alpha}\right) + \frac{{\beta}^{2}}{{\alpha}^{2}}\right)} - \left(-1 \cdot \frac{4 \cdot i + \left(2 + 2 \cdot \beta\right)}{\alpha} + \left(-1 \cdot \frac{\left(\beta + 2 \cdot i\right) \cdot \left(\beta + \left(2 + 2 \cdot i\right)\right)}{{\alpha}^{2}} + \frac{\left(\left(\beta + -1 \cdot \beta\right) - -1 \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)\right) \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)}{{\alpha}^{2}}\right)\right)}{2} \]
      +-commutative [<=] 77.3	\[ \frac{\left(\color{blue}{\left(-1 \cdot \frac{\beta}{\alpha} + \frac{\beta}{\alpha}\right)} + \frac{{\beta}^{2}}{{\alpha}^{2}}\right) - \left(-1 \cdot \frac{4 \cdot i + \left(2 + 2 \cdot \beta\right)}{\alpha} + \left(-1 \cdot \frac{\left(\beta + 2 \cdot i\right) \cdot \left(\beta + \left(2 + 2 \cdot i\right)\right)}{{\alpha}^{2}} + \frac{\left(\left(\beta + -1 \cdot \beta\right) - -1 \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)\right) \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)}{{\alpha}^{2}}\right)\right)}{2} \]
      distribute-lft1-in [=>] 77.3	\[ \frac{\left(\color{blue}{\left(-1 + 1\right) \cdot \frac{\beta}{\alpha}} + \frac{{\beta}^{2}}{{\alpha}^{2}}\right) - \left(-1 \cdot \frac{4 \cdot i + \left(2 + 2 \cdot \beta\right)}{\alpha} + \left(-1 \cdot \frac{\left(\beta + 2 \cdot i\right) \cdot \left(\beta + \left(2 + 2 \cdot i\right)\right)}{{\alpha}^{2}} + \frac{\left(\left(\beta + -1 \cdot \beta\right) - -1 \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)\right) \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)}{{\alpha}^{2}}\right)\right)}{2} \]
      metadata-eval [=>] 77.3	\[ \frac{\left(\color{blue}{0} \cdot \frac{\beta}{\alpha} + \frac{{\beta}^{2}}{{\alpha}^{2}}\right) - \left(-1 \cdot \frac{4 \cdot i + \left(2 + 2 \cdot \beta\right)}{\alpha} + \left(-1 \cdot \frac{\left(\beta + 2 \cdot i\right) \cdot \left(\beta + \left(2 + 2 \cdot i\right)\right)}{{\alpha}^{2}} + \frac{\left(\left(\beta + -1 \cdot \beta\right) - -1 \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)\right) \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)}{{\alpha}^{2}}\right)\right)}{2} \]
      mul0-lft [=>] 77.3	\[ \frac{\left(\color{blue}{0} + \frac{{\beta}^{2}}{{\alpha}^{2}}\right) - \left(-1 \cdot \frac{4 \cdot i + \left(2 + 2 \cdot \beta\right)}{\alpha} + \left(-1 \cdot \frac{\left(\beta + 2 \cdot i\right) \cdot \left(\beta + \left(2 + 2 \cdot i\right)\right)}{{\alpha}^{2}} + \frac{\left(\left(\beta + -1 \cdot \beta\right) - -1 \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)\right) \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)}{{\alpha}^{2}}\right)\right)}{2} \]
      unpow2 [=>] 77.3	\[ \frac{\left(0 + \frac{\color{blue}{\beta \cdot \beta}}{{\alpha}^{2}}\right) - \left(-1 \cdot \frac{4 \cdot i + \left(2 + 2 \cdot \beta\right)}{\alpha} + \left(-1 \cdot \frac{\left(\beta + 2 \cdot i\right) \cdot \left(\beta + \left(2 + 2 \cdot i\right)\right)}{{\alpha}^{2}} + \frac{\left(\left(\beta + -1 \cdot \beta\right) - -1 \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)\right) \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)}{{\alpha}^{2}}\right)\right)}{2} \]
      unpow2 [=>] 77.3	\[ \frac{\left(0 + \frac{\beta \cdot \beta}{\color{blue}{\alpha \cdot \alpha}}\right) - \left(-1 \cdot \frac{4 \cdot i + \left(2 + 2 \cdot \beta\right)}{\alpha} + \left(-1 \cdot \frac{\left(\beta + 2 \cdot i\right) \cdot \left(\beta + \left(2 + 2 \cdot i\right)\right)}{{\alpha}^{2}} + \frac{\left(\left(\beta + -1 \cdot \beta\right) - -1 \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)\right) \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)}{{\alpha}^{2}}\right)\right)}{2} \]
      times-frac [=>] 77.3	\[ \frac{\left(0 + \color{blue}{\frac{\beta}{\alpha} \cdot \frac{\beta}{\alpha}}\right) - \left(-1 \cdot \frac{4 \cdot i + \left(2 + 2 \cdot \beta\right)}{\alpha} + \left(-1 \cdot \frac{\left(\beta + 2 \cdot i\right) \cdot \left(\beta + \left(2 + 2 \cdot i\right)\right)}{{\alpha}^{2}} + \frac{\left(\left(\beta + -1 \cdot \beta\right) - -1 \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)\right) \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)}{{\alpha}^{2}}\right)\right)}{2} \]
      +-commutative [=>] 77.3	\[ \frac{\left(0 + \frac{\beta}{\alpha} \cdot \frac{\beta}{\alpha}\right) - \color{blue}{\left(\left(-1 \cdot \frac{\left(\beta + 2 \cdot i\right) \cdot \left(\beta + \left(2 + 2 \cdot i\right)\right)}{{\alpha}^{2}} + \frac{\left(\left(\beta + -1 \cdot \beta\right) - -1 \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)\right) \cdot \left(4 \cdot i + \left(2 + 2 \cdot \beta\right)\right)}{{\alpha}^{2}}\right) + -1 \cdot \frac{4 \cdot i + \left(2 + 2 \cdot \beta\right)}{\alpha}\right)}}{2} \]

   if -0.5 < (/.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 81.9%

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

   2. Applied egg-rr 100.0%

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

      [Start] 81.9	\[ \frac{\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{\left(\alpha + \beta\right) + 2 \cdot i}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) + 2} + 1}{2} \]
      associate-/l* [=>] 100.0	\[ \frac{\frac{\color{blue}{\frac{\alpha + \beta}{\frac{\left(\alpha + \beta\right) + 2 \cdot i}{\beta - \alpha}}}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) + 2} + 1}{2} \]
      associate-/r/ [=>] 100.0	\[ \frac{\frac{\color{blue}{\frac{\alpha + \beta}{\left(\alpha + \beta\right) + 2 \cdot i} \cdot \left(\beta - \alpha\right)}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) + 2} + 1}{2} \]
      +-commutative [=>] 100.0	\[ \frac{\frac{\frac{\alpha + \beta}{\color{blue}{2 \cdot i + \left(\alpha + \beta\right)}} \cdot \left(\beta - \alpha\right)}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) + 2} + 1}{2} \]
      fma-def [=>] 100.0	\[ \frac{\frac{\frac{\alpha + \beta}{\color{blue}{\mathsf{fma}\left(2, i, \alpha + \beta\right)}} \cdot \left(\beta - \alpha\right)}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) + 2} + 1}{2} \]

   3. Taylor expanded in alpha around 0 100.0%

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

4. Final simplification 98.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{\left(\alpha + \beta\right) + 2 \cdot i}}{2 + \left(\left(\alpha + \beta\right) + 2 \cdot i\right)} \leq -0.5:\\ \;\;\;\;\frac{\frac{\beta}{\alpha} \cdot \frac{\beta}{\alpha} + \left(\frac{\mathsf{fma}\left(i, 4, \mathsf{fma}\left(\beta, 2, 2\right)\right)}{\alpha} + \left(\frac{\beta + \mathsf{fma}\left(2, i, 2\right)}{\alpha} \cdot \frac{\mathsf{fma}\left(2, i, \beta\right)}{\alpha} - \frac{\mathsf{fma}\left(i, 4, \mathsf{fma}\left(\beta, 2, 2\right)\right)}{\alpha} \cdot \frac{\mathsf{fma}\left(i, 4, \mathsf{fma}\left(\beta, 2, 2\right)\right)}{\alpha}\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\left(\beta - \alpha\right) \cdot \frac{\beta}{\beta + 2 \cdot i}}{2 + \left(\left(\alpha + \beta\right) + 2 \cdot i\right)} + 1}{2}\\ \end{array} \]

Alternatives

Alternative 1
Accuracy	97.6%
Cost	50372

\[\begin{array}{l} t_0 := \beta + \mathsf{fma}\left(2, i, 2\right)\\ t_1 := \frac{t_0}{\alpha}\\ t_2 := \beta + t_0\\ t_3 := \left(\alpha + \beta\right) + 2 \cdot i\\ t_4 := 2 + t_3\\ t_5 := \frac{\alpha \cdot \alpha}{i}\\ \mathbf{if}\;\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{t_3}}{t_4} \leq -0.5:\\ \;\;\;\;\frac{t_1 + \left(\mathsf{fma}\left(-2, \frac{t_2}{t_5}, \frac{\beta}{\alpha}\right) + \left(\mathsf{fma}\left(-2, \frac{\mathsf{fma}\left(2, i, \beta\right)}{t_5}, 2 \cdot \frac{i}{\alpha}\right) - t_1 \cdot \frac{t_2}{\alpha}\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\left(\beta - \alpha\right) \cdot \frac{\beta}{\beta + 2 \cdot i}}{t_4} + 1}{2}\\ \end{array} \]

Alternative 2
Accuracy	97.4%
Cost	3268

\[\begin{array}{l} t_0 := \beta + 2 \cdot i\\ t_1 := \left(\alpha + \beta\right) + 2 \cdot i\\ t_2 := 2 + t_1\\ \mathbf{if}\;\frac{\frac{\left(\alpha + \beta\right) \cdot \left(\beta - \alpha\right)}{t_1}}{t_2} \leq -0.5:\\ \;\;\;\;\frac{\frac{t_0 + \left(\beta + \left(2 + 2 \cdot i\right)\right)}{\alpha}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\left(\beta - \alpha\right) \cdot \frac{\beta}{t_0}}{t_2} + 1}{2}\\ \end{array} \]

Alternative 3
Accuracy	89.2%
Cost	1220

\[\begin{array}{l} \mathbf{if}\;\alpha \leq 3.4 \cdot 10^{+94}:\\ \;\;\;\;\frac{1 + \frac{\beta}{2 + \left(\left(\alpha + \beta\right) + 2 \cdot i\right)}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\left(\beta + 2 \cdot i\right) + \left(\beta + \left(2 + 2 \cdot i\right)\right)}{\alpha}}{2}\\ \end{array} \]

Alternative 4
Accuracy	83.3%
Cost	1092

\[\begin{array}{l} \mathbf{if}\;\alpha \leq 4 \cdot 10^{+94}:\\ \;\;\;\;\frac{1 + \frac{\beta}{2 + \left(\left(\alpha + \beta\right) + 2 \cdot i\right)}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{2 + \beta \cdot 2}{\alpha}}{2}\\ \end{array} \]

Alternative 5
Accuracy	83.3%
Cost	964

\[\begin{array}{l} \mathbf{if}\;\alpha \leq 3.5 \cdot 10^{+92}:\\ \;\;\;\;\frac{1 + \frac{\beta}{\beta + \left(2 + 2 \cdot i\right)}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{2 + \beta \cdot 2}{\alpha}}{2}\\ \end{array} \]

Alternative 6
Accuracy	78.3%
Cost	836

\[\begin{array}{l} \mathbf{if}\;\alpha \leq 7.8 \cdot 10^{+93}:\\ \;\;\;\;\frac{1 + \frac{\beta}{\beta + \left(\alpha + 2\right)}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{2 + \beta \cdot 2}{\alpha}}{2}\\ \end{array} \]

Alternative 7
Accuracy	75.3%
Cost	708

\[\begin{array}{l} \mathbf{if}\;i \leq 3.3 \cdot 10^{+227}:\\ \;\;\;\;\frac{1 + \frac{\beta}{\beta + 2}}{2}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \]

Alternative 8
Accuracy	78.1%
Cost	708

\[\begin{array}{l} \mathbf{if}\;\alpha \leq 1.55 \cdot 10^{+94}:\\ \;\;\;\;\frac{1 + \frac{\beta}{\beta + 2}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{2 + \beta \cdot 2}{\alpha}}{2}\\ \end{array} \]

Alternative 9
Accuracy	72.4%
Cost	196

\[\begin{array}{l} \mathbf{if}\;\beta \leq 8 \cdot 10^{+55}:\\ \;\;\;\;0.5\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 10
Accuracy	61.9%
Cost	64

\[0.5 \]

Reproduce

herbie shell --seed 2023162 
(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))