Average Error: 3.6 → 0.2
Time: 5.1s
Precision: binary64
\[\alpha > -1 \land \beta > -1\]
\[ \begin{array}{c}[alpha, beta] = \mathsf{sort}([alpha, beta])\\ \end{array} \]
\[\frac{\frac{\frac{\left(\left(\alpha + \beta\right) + \beta \cdot \alpha\right) + 1}{\left(\alpha + \beta\right) + 2 \cdot 1}}{\left(\alpha + \beta\right) + 2 \cdot 1}}{\left(\left(\alpha + \beta\right) + 2 \cdot 1\right) + 1} \]
↓
\[\begin{array}{l} t_0 := \alpha + \left(\beta + 2\right)\\ {\left(\frac{\beta + \left(\alpha + 3\right)}{\alpha + 1}\right)}^{-1} \cdot \left(\frac{1}{t_0} \cdot \frac{\beta + 1}{t_0}\right) \end{array} \]
(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 (+ alpha (+ beta 2.0))))
   (*
    (pow (/ (+ beta (+ alpha 3.0)) (+ alpha 1.0)) -1.0)
    (* (/ 1.0 t_0) (/ (+ beta 1.0) t_0)))))
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 = alpha + (beta + 2.0);
	return pow(((beta + (alpha + 3.0)) / (alpha + 1.0)), -1.0) * ((1.0 / t_0) * ((beta + 1.0) / t_0));
}

real(8) function code(alpha, beta)
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    code = (((((alpha + beta) + (beta * alpha)) + 1.0d0) / ((alpha + beta) + (2.0d0 * 1.0d0))) / ((alpha + beta) + (2.0d0 * 1.0d0))) / (((alpha + beta) + (2.0d0 * 1.0d0)) + 1.0d0)
end function
↓
real(8) function code(alpha, beta)
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8) :: t_0
    t_0 = alpha + (beta + 2.0d0)
    code = (((beta + (alpha + 3.0d0)) / (alpha + 1.0d0)) ** (-1.0d0)) * ((1.0d0 / t_0) * ((beta + 1.0d0) / t_0))
end function

public static 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);
}
↓
public static double code(double alpha, double beta) {
	double t_0 = alpha + (beta + 2.0);
	return Math.pow(((beta + (alpha + 3.0)) / (alpha + 1.0)), -1.0) * ((1.0 / t_0) * ((beta + 1.0) / t_0));
}

def code(alpha, 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)
↓
def code(alpha, beta):
	t_0 = alpha + (beta + 2.0)
	return math.pow(((beta + (alpha + 3.0)) / (alpha + 1.0)), -1.0) * ((1.0 / t_0) * ((beta + 1.0) / t_0))

function code(alpha, beta)
	return Float64(Float64(Float64(Float64(Float64(Float64(alpha + beta) + Float64(beta * alpha)) + 1.0) / Float64(Float64(alpha + beta) + Float64(2.0 * 1.0))) / Float64(Float64(alpha + beta) + Float64(2.0 * 1.0))) / Float64(Float64(Float64(alpha + beta) + Float64(2.0 * 1.0)) + 1.0))
end
↓
function code(alpha, beta)
	t_0 = Float64(alpha + Float64(beta + 2.0))
	return Float64((Float64(Float64(beta + Float64(alpha + 3.0)) / Float64(alpha + 1.0)) ^ -1.0) * Float64(Float64(1.0 / t_0) * Float64(Float64(beta + 1.0) / t_0)))
end

function tmp = code(alpha, beta)
	tmp = (((((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);
end
↓
function tmp = code(alpha, beta)
	t_0 = alpha + (beta + 2.0);
	tmp = (((beta + (alpha + 3.0)) / (alpha + 1.0)) ^ -1.0) * ((1.0 / t_0) * ((beta + 1.0) / t_0));
end

code[alpha_, beta_] := N[(N[(N[(N[(N[(N[(alpha + beta), $MachinePrecision] + N[(beta * alpha), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * 1.0), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]
↓
code[alpha_, beta_] := Block[{t$95$0 = N[(alpha + N[(beta + 2.0), $MachinePrecision]), $MachinePrecision]}, N[(N[Power[N[(N[(beta + N[(alpha + 3.0), $MachinePrecision]), $MachinePrecision] / N[(alpha + 1.0), $MachinePrecision]), $MachinePrecision], -1.0], $MachinePrecision] * N[(N[(1.0 / t$95$0), $MachinePrecision] * N[(N[(beta + 1.0), $MachinePrecision] / t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

\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 := \alpha + \left(\beta + 2\right)\\
{\left(\frac{\beta + \left(\alpha + 3\right)}{\alpha + 1}\right)}^{-1} \cdot \left(\frac{1}{t_0} \cdot \frac{\beta + 1}{t_0}\right)
\end{array}

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

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

  3. Applied egg-rr0.2

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

  4. Applied egg-rr0.2

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

  5. Final simplification0.2

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

Reproduce

herbie shell --seed 2022159 
(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)))