Octave 3.8, jcobi/1

Percentage Accurate: 75.1% → 99.9%

Time: 8.2s

Alternatives: 12

Speedup: 0.7×

Specification

\[\alpha > -1 \land \beta > -1\]

Math

\[\begin{array}{l} \\ \frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (/ (+ (/ (- beta alpha) (+ (+ alpha beta) 2.0)) 1.0) 2.0))

C

double code(double alpha, double beta) {
	return (((beta - alpha) / ((alpha + beta) + 2.0)) + 1.0) / 2.0;
}

Fortran

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

Java

public static double code(double alpha, double beta) {
	return (((beta - alpha) / ((alpha + beta) + 2.0)) + 1.0) / 2.0;
}

Python

def code(alpha, beta):
	return (((beta - alpha) / ((alpha + beta) + 2.0)) + 1.0) / 2.0

Julia

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

MATLAB

function tmp = code(alpha, beta)
	tmp = (((beta - alpha) / ((alpha + beta) + 2.0)) + 1.0) / 2.0;
end

Wolfram

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

Initial Program: 75.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (/ (+ (/ (- beta alpha) (+ (+ alpha beta) 2.0)) 1.0) 2.0))

C

double code(double alpha, double beta) {
	return (((beta - alpha) / ((alpha + beta) + 2.0)) + 1.0) / 2.0;
}

Fortran

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

Java

public static double code(double alpha, double beta) {
	return (((beta - alpha) / ((alpha + beta) + 2.0)) + 1.0) / 2.0;
}

Python

def code(alpha, beta):
	return (((beta - alpha) / ((alpha + beta) + 2.0)) + 1.0) / 2.0

Julia

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

MATLAB

function tmp = code(alpha, beta)
	tmp = (((beta - alpha) / ((alpha + beta) + 2.0)) + 1.0) / 2.0;
end

Wolfram

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

Alternative 1: 99.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.9999:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(\beta - -2\right) \cdot \frac{\mathsf{fma}\left(-2, \beta, -2\right)}{\alpha}, 0.5, 1 + \beta\right)}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;\left(\left(1 - \frac{\alpha}{\left(\alpha + \beta\right) - -2}\right) - \frac{\beta}{-2 - \left(\alpha + \beta\right)}\right) \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (if (<= (/ (- beta alpha) (+ 2.0 (+ alpha beta))) -0.9999)
   (/
    (fma (* (- beta -2.0) (/ (fma -2.0 beta -2.0) alpha)) 0.5 (+ 1.0 beta))
    alpha)
   (*
    (-
     (- 1.0 (/ alpha (- (+ alpha beta) -2.0)))
     (/ beta (- -2.0 (+ alpha beta))))
    0.5)))

C

double code(double alpha, double beta) {
	double tmp;
	if (((beta - alpha) / (2.0 + (alpha + beta))) <= -0.9999) {
		tmp = fma(((beta - -2.0) * (fma(-2.0, beta, -2.0) / alpha)), 0.5, (1.0 + beta)) / alpha;
	} else {
		tmp = ((1.0 - (alpha / ((alpha + beta) - -2.0))) - (beta / (-2.0 - (alpha + beta)))) * 0.5;
	}
	return tmp;
}

Julia

function code(alpha, beta)
	tmp = 0.0
	if (Float64(Float64(beta - alpha) / Float64(2.0 + Float64(alpha + beta))) <= -0.9999)
		tmp = Float64(fma(Float64(Float64(beta - -2.0) * Float64(fma(-2.0, beta, -2.0) / alpha)), 0.5, Float64(1.0 + beta)) / alpha);
	else
		tmp = Float64(Float64(Float64(1.0 - Float64(alpha / Float64(Float64(alpha + beta) - -2.0))) - Float64(beta / Float64(-2.0 - Float64(alpha + beta)))) * 0.5);
	end
	return tmp
end

Wolfram

code[alpha_, beta_] := If[LessEqual[N[(N[(beta - alpha), $MachinePrecision] / N[(2.0 + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -0.9999], N[(N[(N[(N[(beta - -2.0), $MachinePrecision] * N[(N[(-2.0 * beta + -2.0), $MachinePrecision] / alpha), $MachinePrecision]), $MachinePrecision] * 0.5 + N[(1.0 + beta), $MachinePrecision]), $MachinePrecision] / alpha), $MachinePrecision], N[(N[(N[(1.0 - N[(alpha / N[(N[(alpha + beta), $MachinePrecision] - -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(beta / N[(-2.0 - N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < -0.99990000000000001

   1. Initial program 7.5%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in alpha around inf

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

   5. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{\mathsf{fma}\left(-2, \beta, -2\right)}{\alpha} \cdot \left(\beta - -2\right), 0.5, 1 + \beta\right)}{\alpha}} \]

   if -0.99990000000000001 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64)))

   1. Initial program 99.8%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift--.f64 N/A

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

      4. div-sub N/A

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

      5. associate-+l- N/A

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

      6. lower--.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-+.f64 N/A

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

      11. lower--.f64 N/A

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

      12. lower-/.f64 99.9

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 99.9

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

   4. Applied rewrites 99.9%

      \[\leadsto \frac{\color{blue}{\frac{\beta}{2 + \left(\alpha + \beta\right)} - \left(\frac{\alpha}{2 + \left(\alpha + \beta\right)} - 1\right)}}{2} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. div-sub N/A

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

      4. sub-neg N/A

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

      5. div-inv N/A

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

      6. metadata-eval N/A

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

      7. lower-fma.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 N/A

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

      11. lower-neg.f64 N/A

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

      12. div-inv N/A

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

      13. metadata-eval N/A

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

      14. lower-*.f64 99.9

          \[\leadsto \mathsf{fma}\left(\frac{\beta}{\left(\alpha + \beta\right) + 2}, 0.5, -\color{blue}{\left(\frac{\alpha}{2 + \left(\alpha + \beta\right)} - 1\right) \cdot 0.5}\right) \]

   6. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\beta}{\left(\alpha + \beta\right) + 2}, 0.5, -\left(\frac{\alpha}{\left(\alpha + \beta\right) + 2} - 1\right) \cdot 0.5\right)} \]
   7. Step-by-step derivation

      1. lift-fma.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. lift-+.f64 N/A

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

      5. lift-neg.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. distribute-lft-neg-in N/A

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

      8. distribute-rgt-out N/A

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

      9. lower-*.f64 N/A

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

      10. lower-+.f64 N/A

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

      11. lower-neg.f64 99.9

          \[\leadsto 0.5 \cdot \left(\frac{\beta}{2 + \left(\alpha + \beta\right)} + \color{blue}{\left(-\left(\frac{\alpha}{\left(\alpha + \beta\right) + 2} - 1\right)\right)}\right) \]

      12. lift-+.f64 N/A

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

      13. +-commutative N/A

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

      14. lift-+.f64 99.9

          \[\leadsto 0.5 \cdot \left(\frac{\beta}{2 + \left(\alpha + \beta\right)} + \left(-\left(\frac{\alpha}{\color{blue}{2 + \left(\alpha + \beta\right)}} - 1\right)\right)\right) \]

   8. Applied rewrites 99.9%

      \[\leadsto \color{blue}{0.5 \cdot \left(\frac{\beta}{2 + \left(\alpha + \beta\right)} + \left(-\left(\frac{\alpha}{2 + \left(\alpha + \beta\right)} - 1\right)\right)\right)} \]
   9. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-neg.f64 N/A

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

      3. unsub-neg N/A

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

      4. lower--.f64 99.9

         \[\leadsto 0.5 \cdot \color{blue}{\left(\frac{\beta}{2 + \left(\alpha + \beta\right)} - \left(\frac{\alpha}{2 + \left(\alpha + \beta\right)} - 1\right)\right)} \]

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-+.f64 N/A

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

      8. associate-+l+ N/A

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

      9. metadata-eval N/A

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

      10. sub-neg N/A

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

      11. associate-+r- N/A

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

      12. lift-+.f64 N/A

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

      13. lower--.f64 99.9

          \[\leadsto 0.5 \cdot \left(\frac{\beta}{\color{blue}{\left(\alpha + \beta\right) - -2}} - \left(\frac{\alpha}{2 + \left(\alpha + \beta\right)} - 1\right)\right) \]

      14. lift-+.f64 N/A

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

      15. +-commutative N/A

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

      16. lift-+.f64 N/A

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

      17. associate-+l+ N/A

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

      18. metadata-eval N/A

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

      19. sub-neg N/A

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

      20. associate-+r- N/A

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

      21. lift-+.f64 N/A

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

      22. lower--.f64 99.9

          \[\leadsto 0.5 \cdot \left(\frac{\beta}{\left(\alpha + \beta\right) - -2} - \left(\frac{\alpha}{\color{blue}{\left(\alpha + \beta\right) - -2}} - 1\right)\right) \]

   10. Applied rewrites 99.9%

       \[\leadsto 0.5 \cdot \color{blue}{\left(\frac{\beta}{\left(\alpha + \beta\right) - -2} - \left(\frac{\alpha}{\left(\alpha + \beta\right) - -2} - 1\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.9999:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(\beta - -2\right) \cdot \frac{\mathsf{fma}\left(-2, \beta, -2\right)}{\alpha}, 0.5, 1 + \beta\right)}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;\left(\left(1 - \frac{\alpha}{\left(\alpha + \beta\right) - -2}\right) - \frac{\beta}{-2 - \left(\alpha + \beta\right)}\right) \cdot 0.5\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 97.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)}\\ \mathbf{if}\;t\_0 \leq -0.999:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{elif}\;t\_0 \leq 0.01:\\ \;\;\;\;\mathsf{fma}\left(\frac{\alpha}{2 + \alpha}, -0.5, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\mathsf{fma}\left(\alpha, -2, -2\right)}{\beta}, 0.5, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (/ (- beta alpha) (+ 2.0 (+ alpha beta)))))
   (if (<= t_0 -0.999)
     (/ (+ 1.0 beta) alpha)
     (if (<= t_0 0.01)
       (fma (/ alpha (+ 2.0 alpha)) -0.5 0.5)
       (fma (/ (fma alpha -2.0 -2.0) beta) 0.5 1.0)))))

C

double code(double alpha, double beta) {
	double t_0 = (beta - alpha) / (2.0 + (alpha + beta));
	double tmp;
	if (t_0 <= -0.999) {
		tmp = (1.0 + beta) / alpha;
	} else if (t_0 <= 0.01) {
		tmp = fma((alpha / (2.0 + alpha)), -0.5, 0.5);
	} else {
		tmp = fma((fma(alpha, -2.0, -2.0) / beta), 0.5, 1.0);
	}
	return tmp;
}

Julia

function code(alpha, beta)
	t_0 = Float64(Float64(beta - alpha) / Float64(2.0 + Float64(alpha + beta)))
	tmp = 0.0
	if (t_0 <= -0.999)
		tmp = Float64(Float64(1.0 + beta) / alpha);
	elseif (t_0 <= 0.01)
		tmp = fma(Float64(alpha / Float64(2.0 + alpha)), -0.5, 0.5);
	else
		tmp = fma(Float64(fma(alpha, -2.0, -2.0) / beta), 0.5, 1.0);
	end
	return tmp
end

Wolfram

code[alpha_, beta_] := Block[{t$95$0 = N[(N[(beta - alpha), $MachinePrecision] / N[(2.0 + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -0.999], N[(N[(1.0 + beta), $MachinePrecision] / alpha), $MachinePrecision], If[LessEqual[t$95$0, 0.01], N[(N[(alpha / N[(2.0 + alpha), $MachinePrecision]), $MachinePrecision] * -0.5 + 0.5), $MachinePrecision], N[(N[(N[(alpha * -2.0 + -2.0), $MachinePrecision] / beta), $MachinePrecision] * 0.5 + 1.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < -0.998999999999999999

   1. Initial program 8.5%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in alpha around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{2 + 2 \cdot \beta}{\alpha}} \]
   4. Step-by-step derivation

      1. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. distribute-lft-in N/A

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

      4. metadata-eval N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{1 + \color{blue}{1} \cdot \beta}{\alpha} \]

      7. *-lft-identity N/A

         \[\leadsto \frac{1 + \color{blue}{\beta}}{\alpha} \]

      8. lower-+.f64 97.5

         \[\leadsto \frac{\color{blue}{1 + \beta}}{\alpha} \]

   5. Applied rewrites 97.5%

      \[\leadsto \color{blue}{\frac{1 + \beta}{\alpha}} \]

   if -0.998999999999999999 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < 0.0100000000000000002

   1. Initial program 99.9%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. clear-num N/A

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

      3. associate-/r/ N/A

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

      4. lift-+.f64 N/A

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

      5. distribute-rgt-in N/A

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

      6. metadata-eval N/A

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

      7. metadata-eval N/A

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

      8. metadata-eval N/A

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

      9. lower-fma.f64 N/A

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

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\alpha - \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right)} \]

   5. Taylor expanded in beta around 0

      \[\leadsto \color{blue}{\frac{1}{2} + \frac{-1}{2} \cdot \frac{\alpha}{2 + \alpha}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{\alpha}{2 + \alpha} + \frac{1}{2}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{\alpha}{2 + \alpha} \cdot \frac{-1}{2}} + \frac{1}{2} \]

      3. lower-fma.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-+.f64 98.5

         \[\leadsto \mathsf{fma}\left(\frac{\alpha}{\color{blue}{2 + \alpha}}, -0.5, 0.5\right) \]

   7. Applied rewrites 98.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\alpha}{2 + \alpha}, -0.5, 0.5\right)} \]

   if 0.0100000000000000002 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64)))

   1. Initial program 100.0%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in beta around -inf

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. +-commutative N/A

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

      6. associate--r+ N/A

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

      7. sub-neg N/A

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

      8. *-lft-identity N/A

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

      9. distribute-rgt-out-- N/A

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

      10. metadata-eval N/A

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

      11. metadata-eval N/A

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

      12. metadata-eval N/A

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

      13. metadata-eval N/A

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

      14. lower-fma.f64 N/A

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

      15. metadata-eval N/A

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

      16. metadata-eval 99.0

          \[\leadsto \mathsf{fma}\left(\frac{\mathsf{fma}\left(\alpha, -2, \color{blue}{-2}\right)}{\beta}, 0.5, 1\right) \]

   5. Applied rewrites 99.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\alpha, -2, -2\right)}{\beta}, 0.5, 1\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.999:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{elif}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq 0.01:\\ \;\;\;\;\mathsf{fma}\left(\frac{\alpha}{2 + \alpha}, -0.5, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\mathsf{fma}\left(\alpha, -2, -2\right)}{\beta}, 0.5, 1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 97.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)}\\ \mathbf{if}\;t\_0 \leq -0.999:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{elif}\;t\_0 \leq 0.01:\\ \;\;\;\;\mathsf{fma}\left(\frac{\alpha}{2 + \alpha}, -0.5, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{1}{\beta}\\ \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (/ (- beta alpha) (+ 2.0 (+ alpha beta)))))
   (if (<= t_0 -0.999)
     (/ (+ 1.0 beta) alpha)
     (if (<= t_0 0.01)
       (fma (/ alpha (+ 2.0 alpha)) -0.5 0.5)
       (- 1.0 (/ 1.0 beta))))))

C

double code(double alpha, double beta) {
	double t_0 = (beta - alpha) / (2.0 + (alpha + beta));
	double tmp;
	if (t_0 <= -0.999) {
		tmp = (1.0 + beta) / alpha;
	} else if (t_0 <= 0.01) {
		tmp = fma((alpha / (2.0 + alpha)), -0.5, 0.5);
	} else {
		tmp = 1.0 - (1.0 / beta);
	}
	return tmp;
}

Julia

function code(alpha, beta)
	t_0 = Float64(Float64(beta - alpha) / Float64(2.0 + Float64(alpha + beta)))
	tmp = 0.0
	if (t_0 <= -0.999)
		tmp = Float64(Float64(1.0 + beta) / alpha);
	elseif (t_0 <= 0.01)
		tmp = fma(Float64(alpha / Float64(2.0 + alpha)), -0.5, 0.5);
	else
		tmp = Float64(1.0 - Float64(1.0 / beta));
	end
	return tmp
end

Wolfram

code[alpha_, beta_] := Block[{t$95$0 = N[(N[(beta - alpha), $MachinePrecision] / N[(2.0 + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -0.999], N[(N[(1.0 + beta), $MachinePrecision] / alpha), $MachinePrecision], If[LessEqual[t$95$0, 0.01], N[(N[(alpha / N[(2.0 + alpha), $MachinePrecision]), $MachinePrecision] * -0.5 + 0.5), $MachinePrecision], N[(1.0 - N[(1.0 / beta), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < -0.998999999999999999

   1. Initial program 8.5%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in alpha around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{2 + 2 \cdot \beta}{\alpha}} \]
   4. Step-by-step derivation

      1. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. distribute-lft-in N/A

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

      4. metadata-eval N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{1 + \color{blue}{1} \cdot \beta}{\alpha} \]

      7. *-lft-identity N/A

         \[\leadsto \frac{1 + \color{blue}{\beta}}{\alpha} \]

      8. lower-+.f64 97.5

         \[\leadsto \frac{\color{blue}{1 + \beta}}{\alpha} \]

   5. Applied rewrites 97.5%

      \[\leadsto \color{blue}{\frac{1 + \beta}{\alpha}} \]

   if -0.998999999999999999 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < 0.0100000000000000002

   1. Initial program 99.9%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. clear-num N/A

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

      3. associate-/r/ N/A

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

      4. lift-+.f64 N/A

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

      5. distribute-rgt-in N/A

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

      6. metadata-eval N/A

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

      7. metadata-eval N/A

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

      8. metadata-eval N/A

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

      9. lower-fma.f64 N/A

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

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\alpha - \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right)} \]

   5. Taylor expanded in beta around 0

      \[\leadsto \color{blue}{\frac{1}{2} + \frac{-1}{2} \cdot \frac{\alpha}{2 + \alpha}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{\alpha}{2 + \alpha} + \frac{1}{2}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{\alpha}{2 + \alpha} \cdot \frac{-1}{2}} + \frac{1}{2} \]

      3. lower-fma.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-+.f64 98.5

         \[\leadsto \mathsf{fma}\left(\frac{\alpha}{\color{blue}{2 + \alpha}}, -0.5, 0.5\right) \]

   7. Applied rewrites 98.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\alpha}{2 + \alpha}, -0.5, 0.5\right)} \]

   if 0.0100000000000000002 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64)))

   1. Initial program 100.0%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in beta around -inf

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. +-commutative N/A

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

      6. associate--r+ N/A

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

      7. sub-neg N/A

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

      8. *-lft-identity N/A

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

      9. distribute-rgt-out-- N/A

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

      10. metadata-eval N/A

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

      11. metadata-eval N/A

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

      12. metadata-eval N/A

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

      13. metadata-eval N/A

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

      14. lower-fma.f64 N/A

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

      15. metadata-eval N/A

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

      16. metadata-eval 99.0

          \[\leadsto \mathsf{fma}\left(\frac{\mathsf{fma}\left(\alpha, -2, \color{blue}{-2}\right)}{\beta}, 0.5, 1\right) \]

   5. Applied rewrites 99.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\alpha, -2, -2\right)}{\beta}, 0.5, 1\right)} \]

   6. Taylor expanded in alpha around 0

      \[\leadsto 1 - \color{blue}{\frac{1}{\beta}} \]
   7. Step-by-step derivation

   8. Applied rewrites 97.7%

      \[\leadsto 1 - \color{blue}{\frac{1}{\beta}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.999:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{elif}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq 0.01:\\ \;\;\;\;\mathsf{fma}\left(\frac{\alpha}{2 + \alpha}, -0.5, 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{1}{\beta}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.9999:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(\beta - -2\right) \cdot \frac{\mathsf{fma}\left(-2, \beta, -2\right)}{\alpha}, 0.5, 1 + \beta\right)}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\left(\frac{\alpha}{\beta} - 1\right) \cdot \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (if (<= (/ (- beta alpha) (+ 2.0 (+ alpha beta))) -0.9999)
   (/
    (fma (* (- beta -2.0) (/ (fma -2.0 beta -2.0) alpha)) 0.5 (+ 1.0 beta))
    alpha)
   (fma (/ (* (- (/ alpha beta) 1.0) beta) (- -2.0 (+ alpha beta))) 0.5 0.5)))

C

double code(double alpha, double beta) {
	double tmp;
	if (((beta - alpha) / (2.0 + (alpha + beta))) <= -0.9999) {
		tmp = fma(((beta - -2.0) * (fma(-2.0, beta, -2.0) / alpha)), 0.5, (1.0 + beta)) / alpha;
	} else {
		tmp = fma(((((alpha / beta) - 1.0) * beta) / (-2.0 - (alpha + beta))), 0.5, 0.5);
	}
	return tmp;
}

Julia

function code(alpha, beta)
	tmp = 0.0
	if (Float64(Float64(beta - alpha) / Float64(2.0 + Float64(alpha + beta))) <= -0.9999)
		tmp = Float64(fma(Float64(Float64(beta - -2.0) * Float64(fma(-2.0, beta, -2.0) / alpha)), 0.5, Float64(1.0 + beta)) / alpha);
	else
		tmp = fma(Float64(Float64(Float64(Float64(alpha / beta) - 1.0) * beta) / Float64(-2.0 - Float64(alpha + beta))), 0.5, 0.5);
	end
	return tmp
end

Wolfram

code[alpha_, beta_] := If[LessEqual[N[(N[(beta - alpha), $MachinePrecision] / N[(2.0 + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -0.9999], N[(N[(N[(N[(beta - -2.0), $MachinePrecision] * N[(N[(-2.0 * beta + -2.0), $MachinePrecision] / alpha), $MachinePrecision]), $MachinePrecision] * 0.5 + N[(1.0 + beta), $MachinePrecision]), $MachinePrecision] / alpha), $MachinePrecision], N[(N[(N[(N[(N[(alpha / beta), $MachinePrecision] - 1.0), $MachinePrecision] * beta), $MachinePrecision] / N[(-2.0 - N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 0.5 + 0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < -0.99990000000000001

   1. Initial program 7.5%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in alpha around inf

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

   5. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{\mathsf{fma}\left(-2, \beta, -2\right)}{\alpha} \cdot \left(\beta - -2\right), 0.5, 1 + \beta\right)}{\alpha}} \]

   if -0.99990000000000001 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64)))

   1. Initial program 99.8%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. clear-num N/A

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

      3. associate-/r/ N/A

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

      4. lift-+.f64 N/A

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

      5. distribute-rgt-in N/A

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

      6. metadata-eval N/A

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

      7. metadata-eval N/A

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

      8. metadata-eval N/A

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

      9. lower-fma.f64 N/A

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

   4. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\alpha - \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right)} \]

   5. Taylor expanded in beta around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-/.f64 99.9

         \[\leadsto \mathsf{fma}\left(\frac{\left(\color{blue}{\frac{\alpha}{\beta}} - 1\right) \cdot \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right) \]

   7. Applied rewrites 99.9%

      \[\leadsto \mathsf{fma}\left(\frac{\color{blue}{\left(\frac{\alpha}{\beta} - 1\right) \cdot \beta}}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.9999:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left(\beta - -2\right) \cdot \frac{\mathsf{fma}\left(-2, \beta, -2\right)}{\alpha}, 0.5, 1 + \beta\right)}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\left(\frac{\alpha}{\beta} - 1\right) \cdot \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 99.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 + \left(\alpha + \beta\right)\\ \mathbf{if}\;\frac{\beta - \alpha}{t\_0} \leq -0.999999:\\ \;\;\;\;\left(\frac{\beta}{t\_0} - \frac{-2 - \beta}{\alpha}\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\left(\frac{\alpha}{\beta} - 1\right) \cdot \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (+ 2.0 (+ alpha beta))))
   (if (<= (/ (- beta alpha) t_0) -0.999999)
     (* (- (/ beta t_0) (/ (- -2.0 beta) alpha)) 0.5)
     (fma
      (/ (* (- (/ alpha beta) 1.0) beta) (- -2.0 (+ alpha beta)))
      0.5
      0.5))))

C

double code(double alpha, double beta) {
	double t_0 = 2.0 + (alpha + beta);
	double tmp;
	if (((beta - alpha) / t_0) <= -0.999999) {
		tmp = ((beta / t_0) - ((-2.0 - beta) / alpha)) * 0.5;
	} else {
		tmp = fma(((((alpha / beta) - 1.0) * beta) / (-2.0 - (alpha + beta))), 0.5, 0.5);
	}
	return tmp;
}

Julia

function code(alpha, beta)
	t_0 = Float64(2.0 + Float64(alpha + beta))
	tmp = 0.0
	if (Float64(Float64(beta - alpha) / t_0) <= -0.999999)
		tmp = Float64(Float64(Float64(beta / t_0) - Float64(Float64(-2.0 - beta) / alpha)) * 0.5);
	else
		tmp = fma(Float64(Float64(Float64(Float64(alpha / beta) - 1.0) * beta) / Float64(-2.0 - Float64(alpha + beta))), 0.5, 0.5);
	end
	return tmp
end

Wolfram

code[alpha_, beta_] := Block[{t$95$0 = N[(2.0 + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(beta - alpha), $MachinePrecision] / t$95$0), $MachinePrecision], -0.999999], N[(N[(N[(beta / t$95$0), $MachinePrecision] - N[(N[(-2.0 - beta), $MachinePrecision] / alpha), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], N[(N[(N[(N[(N[(alpha / beta), $MachinePrecision] - 1.0), $MachinePrecision] * beta), $MachinePrecision] / N[(-2.0 - N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 0.5 + 0.5), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < -0.999998999999999971

   1. Initial program 6.6%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift--.f64 N/A

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

      4. div-sub N/A

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

      5. associate-+l- N/A

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

      6. lower--.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-+.f64 N/A

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

      11. lower--.f64 N/A

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

      12. lower-/.f64 9.8

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 9.8

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

   4. Applied rewrites 9.8%

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

   5. Taylor expanded in alpha around inf

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

      1. associate-*r/ N/A

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

      2. mul-1-neg N/A

         \[\leadsto \frac{\frac{\beta}{2 + \left(\alpha + \beta\right)} - \frac{\color{blue}{\mathsf{neg}\left(\left(2 + \beta\right)\right)}}{\alpha}}{2} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{\frac{\beta}{2 + \left(\alpha + \beta\right)} - \color{blue}{\frac{\mathsf{neg}\left(\left(2 + \beta\right)\right)}{\alpha}}}{2} \]

      4. mul-1-neg N/A

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

      5. distribute-lft-in N/A

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

      6. metadata-eval N/A

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

      7. mul-1-neg N/A

         \[\leadsto \frac{\frac{\beta}{2 + \left(\alpha + \beta\right)} - \frac{-2 + \color{blue}{\left(\mathsf{neg}\left(\beta\right)\right)}}{\alpha}}{2} \]

      8. unsub-neg N/A

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

      9. lower--.f64 98.9

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

   7. Applied rewrites 98.9%

      \[\leadsto \frac{\frac{\beta}{2 + \left(\alpha + \beta\right)} - \color{blue}{\frac{-2 - \beta}{\alpha}}}{2} \]
   8. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. div-inv N/A

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

      3. metadata-eval N/A

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

      4. lower-*.f64 98.9

         \[\leadsto \color{blue}{\left(\frac{\beta}{2 + \left(\alpha + \beta\right)} - \frac{-2 - \beta}{\alpha}\right) \cdot 0.5} \]

   9. Applied rewrites 98.9%

      \[\leadsto \color{blue}{\left(\frac{\beta}{2 + \left(\alpha + \beta\right)} - \frac{-2 - \beta}{\alpha}\right) \cdot 0.5} \]

   if -0.999998999999999971 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64)))

   1. Initial program 99.7%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. clear-num N/A

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

      3. associate-/r/ N/A

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

      4. lift-+.f64 N/A

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

      5. distribute-rgt-in N/A

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

      6. metadata-eval N/A

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

      7. metadata-eval N/A

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

      8. metadata-eval N/A

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

      9. lower-fma.f64 N/A

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

   4. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\alpha - \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right)} \]

   5. Taylor expanded in beta around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-/.f64 99.7

         \[\leadsto \mathsf{fma}\left(\frac{\left(\color{blue}{\frac{\alpha}{\beta}} - 1\right) \cdot \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right) \]

   7. Applied rewrites 99.7%

      \[\leadsto \mathsf{fma}\left(\frac{\color{blue}{\left(\frac{\alpha}{\beta} - 1\right) \cdot \beta}}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.999999:\\ \;\;\;\;\left(\frac{\beta}{2 + \left(\alpha + \beta\right)} - \frac{-2 - \beta}{\alpha}\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\left(\frac{\alpha}{\beta} - 1\right) \cdot \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 99.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 + \left(\alpha + \beta\right)\\ \mathbf{if}\;\frac{\beta - \alpha}{t\_0} \leq -0.999999:\\ \;\;\;\;\left(\frac{\beta}{t\_0} - \frac{-2 - \beta}{\alpha}\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\beta - \alpha}{\left(\alpha + \beta\right) - -2}, 0.5, 0.5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (+ 2.0 (+ alpha beta))))
   (if (<= (/ (- beta alpha) t_0) -0.999999)
     (* (- (/ beta t_0) (/ (- -2.0 beta) alpha)) 0.5)
     (fma (/ (- beta alpha) (- (+ alpha beta) -2.0)) 0.5 0.5))))

C

double code(double alpha, double beta) {
	double t_0 = 2.0 + (alpha + beta);
	double tmp;
	if (((beta - alpha) / t_0) <= -0.999999) {
		tmp = ((beta / t_0) - ((-2.0 - beta) / alpha)) * 0.5;
	} else {
		tmp = fma(((beta - alpha) / ((alpha + beta) - -2.0)), 0.5, 0.5);
	}
	return tmp;
}

Julia

function code(alpha, beta)
	t_0 = Float64(2.0 + Float64(alpha + beta))
	tmp = 0.0
	if (Float64(Float64(beta - alpha) / t_0) <= -0.999999)
		tmp = Float64(Float64(Float64(beta / t_0) - Float64(Float64(-2.0 - beta) / alpha)) * 0.5);
	else
		tmp = fma(Float64(Float64(beta - alpha) / Float64(Float64(alpha + beta) - -2.0)), 0.5, 0.5);
	end
	return tmp
end

Wolfram

code[alpha_, beta_] := Block[{t$95$0 = N[(2.0 + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(beta - alpha), $MachinePrecision] / t$95$0), $MachinePrecision], -0.999999], N[(N[(N[(beta / t$95$0), $MachinePrecision] - N[(N[(-2.0 - beta), $MachinePrecision] / alpha), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], N[(N[(N[(beta - alpha), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -2.0), $MachinePrecision]), $MachinePrecision] * 0.5 + 0.5), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < -0.999998999999999971

   1. Initial program 6.6%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift--.f64 N/A

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

      4. div-sub N/A

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

      5. associate-+l- N/A

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

      6. lower--.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-+.f64 N/A

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

      11. lower--.f64 N/A

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

      12. lower-/.f64 9.8

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 9.8

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

   4. Applied rewrites 9.8%

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

   5. Taylor expanded in alpha around inf

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

      1. associate-*r/ N/A

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

      2. mul-1-neg N/A

         \[\leadsto \frac{\frac{\beta}{2 + \left(\alpha + \beta\right)} - \frac{\color{blue}{\mathsf{neg}\left(\left(2 + \beta\right)\right)}}{\alpha}}{2} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{\frac{\beta}{2 + \left(\alpha + \beta\right)} - \color{blue}{\frac{\mathsf{neg}\left(\left(2 + \beta\right)\right)}{\alpha}}}{2} \]

      4. mul-1-neg N/A

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

      5. distribute-lft-in N/A

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

      6. metadata-eval N/A

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

      7. mul-1-neg N/A

         \[\leadsto \frac{\frac{\beta}{2 + \left(\alpha + \beta\right)} - \frac{-2 + \color{blue}{\left(\mathsf{neg}\left(\beta\right)\right)}}{\alpha}}{2} \]

      8. unsub-neg N/A

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

      9. lower--.f64 98.9

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

   7. Applied rewrites 98.9%

      \[\leadsto \frac{\frac{\beta}{2 + \left(\alpha + \beta\right)} - \color{blue}{\frac{-2 - \beta}{\alpha}}}{2} \]
   8. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. div-inv N/A

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

      3. metadata-eval N/A

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

      4. lower-*.f64 98.9

         \[\leadsto \color{blue}{\left(\frac{\beta}{2 + \left(\alpha + \beta\right)} - \frac{-2 - \beta}{\alpha}\right) \cdot 0.5} \]

   9. Applied rewrites 98.9%

      \[\leadsto \color{blue}{\left(\frac{\beta}{2 + \left(\alpha + \beta\right)} - \frac{-2 - \beta}{\alpha}\right) \cdot 0.5} \]

   if -0.999998999999999971 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64)))

   1. Initial program 99.7%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. clear-num N/A

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

      3. associate-/r/ N/A

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

      4. lift-+.f64 N/A

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

      5. distribute-rgt-in N/A

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

      6. metadata-eval N/A

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

      7. metadata-eval N/A

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

      8. metadata-eval N/A

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

      9. lower-fma.f64 N/A

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

   4. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\alpha - \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.999999:\\ \;\;\;\;\left(\frac{\beta}{2 + \left(\alpha + \beta\right)} - \frac{-2 - \beta}{\alpha}\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\beta - \alpha}{\left(\alpha + \beta\right) - -2}, 0.5, 0.5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 99.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.999999:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\beta - \alpha}{\left(\alpha + \beta\right) - -2}, 0.5, 0.5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (if (<= (/ (- beta alpha) (+ 2.0 (+ alpha beta))) -0.999999)
   (/ (+ 1.0 beta) alpha)
   (fma (/ (- beta alpha) (- (+ alpha beta) -2.0)) 0.5 0.5)))

C

double code(double alpha, double beta) {
	double tmp;
	if (((beta - alpha) / (2.0 + (alpha + beta))) <= -0.999999) {
		tmp = (1.0 + beta) / alpha;
	} else {
		tmp = fma(((beta - alpha) / ((alpha + beta) - -2.0)), 0.5, 0.5);
	}
	return tmp;
}

Julia

function code(alpha, beta)
	tmp = 0.0
	if (Float64(Float64(beta - alpha) / Float64(2.0 + Float64(alpha + beta))) <= -0.999999)
		tmp = Float64(Float64(1.0 + beta) / alpha);
	else
		tmp = fma(Float64(Float64(beta - alpha) / Float64(Float64(alpha + beta) - -2.0)), 0.5, 0.5);
	end
	return tmp
end

Wolfram

code[alpha_, beta_] := If[LessEqual[N[(N[(beta - alpha), $MachinePrecision] / N[(2.0 + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -0.999999], N[(N[(1.0 + beta), $MachinePrecision] / alpha), $MachinePrecision], N[(N[(N[(beta - alpha), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -2.0), $MachinePrecision]), $MachinePrecision] * 0.5 + 0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < -0.999998999999999971

   1. Initial program 6.6%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in alpha around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{2 + 2 \cdot \beta}{\alpha}} \]
   4. Step-by-step derivation

      1. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. distribute-lft-in N/A

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

      4. metadata-eval N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{1 + \color{blue}{1} \cdot \beta}{\alpha} \]

      7. *-lft-identity N/A

         \[\leadsto \frac{1 + \color{blue}{\beta}}{\alpha} \]

      8. lower-+.f64 98.9

         \[\leadsto \frac{\color{blue}{1 + \beta}}{\alpha} \]

   5. Applied rewrites 98.9%

      \[\leadsto \color{blue}{\frac{1 + \beta}{\alpha}} \]

   if -0.999998999999999971 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64)))

   1. Initial program 99.7%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. clear-num N/A

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

      3. associate-/r/ N/A

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

      4. lift-+.f64 N/A

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

      5. distribute-rgt-in N/A

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

      6. metadata-eval N/A

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

      7. metadata-eval N/A

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

      8. metadata-eval N/A

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

      9. lower-fma.f64 N/A

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

   4. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\alpha - \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.999999:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\beta - \alpha}{\left(\alpha + \beta\right) - -2}, 0.5, 0.5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 99.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.999999:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\alpha - \beta, \frac{0.5}{-2 - \left(\alpha + \beta\right)}, 0.5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (if (<= (/ (- beta alpha) (+ 2.0 (+ alpha beta))) -0.999999)
   (/ (+ 1.0 beta) alpha)
   (fma (- alpha beta) (/ 0.5 (- -2.0 (+ alpha beta))) 0.5)))

C

double code(double alpha, double beta) {
	double tmp;
	if (((beta - alpha) / (2.0 + (alpha + beta))) <= -0.999999) {
		tmp = (1.0 + beta) / alpha;
	} else {
		tmp = fma((alpha - beta), (0.5 / (-2.0 - (alpha + beta))), 0.5);
	}
	return tmp;
}

Julia

function code(alpha, beta)
	tmp = 0.0
	if (Float64(Float64(beta - alpha) / Float64(2.0 + Float64(alpha + beta))) <= -0.999999)
		tmp = Float64(Float64(1.0 + beta) / alpha);
	else
		tmp = fma(Float64(alpha - beta), Float64(0.5 / Float64(-2.0 - Float64(alpha + beta))), 0.5);
	end
	return tmp
end

Wolfram

code[alpha_, beta_] := If[LessEqual[N[(N[(beta - alpha), $MachinePrecision] / N[(2.0 + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -0.999999], N[(N[(1.0 + beta), $MachinePrecision] / alpha), $MachinePrecision], N[(N[(alpha - beta), $MachinePrecision] * N[(0.5 / N[(-2.0 - N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < -0.999998999999999971

   1. Initial program 6.6%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in alpha around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{2 + 2 \cdot \beta}{\alpha}} \]
   4. Step-by-step derivation

      1. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. distribute-lft-in N/A

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

      4. metadata-eval N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{1 + \color{blue}{1} \cdot \beta}{\alpha} \]

      7. *-lft-identity N/A

         \[\leadsto \frac{1 + \color{blue}{\beta}}{\alpha} \]

      8. lower-+.f64 98.9

         \[\leadsto \frac{\color{blue}{1 + \beta}}{\alpha} \]

   5. Applied rewrites 98.9%

      \[\leadsto \color{blue}{\frac{1 + \beta}{\alpha}} \]

   if -0.999998999999999971 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64)))

   1. Initial program 99.7%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. clear-num N/A

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

      3. associate-/r/ N/A

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

      4. lift-+.f64 N/A

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

      5. distribute-rgt-in N/A

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

      6. metadata-eval N/A

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

      7. metadata-eval N/A

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

      8. metadata-eval N/A

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

      9. lower-fma.f64 N/A

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

   4. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\alpha - \beta}{-2 - \left(\alpha + \beta\right)}, 0.5, 0.5\right)} \]
   5. Step-by-step derivation

      1. lift-fma.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-*l/ N/A

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

      4. associate-/l* N/A

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

      5. lower-fma.f64 N/A

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

      6. lower-/.f64 99.6

         \[\leadsto \mathsf{fma}\left(\alpha - \beta, \color{blue}{\frac{0.5}{-2 - \left(\alpha + \beta\right)}}, 0.5\right) \]

   6. Applied rewrites 99.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\alpha - \beta, \frac{0.5}{-2 - \left(\alpha + \beta\right)}, 0.5\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.999999:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\alpha - \beta, \frac{0.5}{-2 - \left(\alpha + \beta\right)}, 0.5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 97.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.05:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\beta}{\beta - -2}, 0.5, 0.5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (if (<= (/ (- beta alpha) (+ 2.0 (+ alpha beta))) -0.05)
   (/ (+ 1.0 beta) alpha)
   (fma (/ beta (- beta -2.0)) 0.5 0.5)))

C

double code(double alpha, double beta) {
	double tmp;
	if (((beta - alpha) / (2.0 + (alpha + beta))) <= -0.05) {
		tmp = (1.0 + beta) / alpha;
	} else {
		tmp = fma((beta / (beta - -2.0)), 0.5, 0.5);
	}
	return tmp;
}

Julia

function code(alpha, beta)
	tmp = 0.0
	if (Float64(Float64(beta - alpha) / Float64(2.0 + Float64(alpha + beta))) <= -0.05)
		tmp = Float64(Float64(1.0 + beta) / alpha);
	else
		tmp = fma(Float64(beta / Float64(beta - -2.0)), 0.5, 0.5);
	end
	return tmp
end

Wolfram

code[alpha_, beta_] := If[LessEqual[N[(N[(beta - alpha), $MachinePrecision] / N[(2.0 + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -0.05], N[(N[(1.0 + beta), $MachinePrecision] / alpha), $MachinePrecision], N[(N[(beta / N[(beta - -2.0), $MachinePrecision]), $MachinePrecision] * 0.5 + 0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < -0.050000000000000003

   1. Initial program 10.6%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in alpha around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{2 + 2 \cdot \beta}{\alpha}} \]
   4. Step-by-step derivation

      1. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. distribute-lft-in N/A

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

      4. metadata-eval N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{1 + \color{blue}{1} \cdot \beta}{\alpha} \]

      7. *-lft-identity N/A

         \[\leadsto \frac{1 + \color{blue}{\beta}}{\alpha} \]

      8. lower-+.f64 95.7

         \[\leadsto \frac{\color{blue}{1 + \beta}}{\alpha} \]

   5. Applied rewrites 95.7%

      \[\leadsto \color{blue}{\frac{1 + \beta}{\alpha}} \]

   if -0.050000000000000003 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64)))

   1. Initial program 100.0%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in alpha around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(1 + \frac{\beta}{2 + \beta}\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. distribute-rgt-in N/A

         \[\leadsto \color{blue}{\frac{\beta}{2 + \beta} \cdot \frac{1}{2} + 1 \cdot \frac{1}{2}} \]

      3. metadata-eval N/A

         \[\leadsto \frac{\beta}{2 + \beta} \cdot \frac{1}{2} + \color{blue}{\frac{1}{2}} \]

      4. lower-fma.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. +-commutative N/A

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

      7. metadata-eval N/A

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

      8. metadata-eval N/A

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

      9. sub-neg N/A

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

      10. lower--.f64 N/A

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

      11. metadata-eval 97.0

          \[\leadsto \mathsf{fma}\left(\frac{\beta}{\beta - \color{blue}{-2}}, 0.5, 0.5\right) \]

   5. Applied rewrites 97.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\beta}{\beta - -2}, 0.5, 0.5\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 96.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.05:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\beta}{\beta - -2}, 0.5, 0.5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 62.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.05:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (if (<= (/ (- beta alpha) (+ 2.0 (+ alpha beta))) -0.05)
   (/ (+ 1.0 beta) alpha)
   1.0))

C

double code(double alpha, double beta) {
	double tmp;
	if (((beta - alpha) / (2.0 + (alpha + beta))) <= -0.05) {
		tmp = (1.0 + beta) / alpha;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(alpha, beta)
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8) :: tmp
    if (((beta - alpha) / (2.0d0 + (alpha + beta))) <= (-0.05d0)) then
        tmp = (1.0d0 + beta) / alpha
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double alpha, double beta) {
	double tmp;
	if (((beta - alpha) / (2.0 + (alpha + beta))) <= -0.05) {
		tmp = (1.0 + beta) / alpha;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(alpha, beta):
	tmp = 0
	if ((beta - alpha) / (2.0 + (alpha + beta))) <= -0.05:
		tmp = (1.0 + beta) / alpha
	else:
		tmp = 1.0
	return tmp

Julia

function code(alpha, beta)
	tmp = 0.0
	if (Float64(Float64(beta - alpha) / Float64(2.0 + Float64(alpha + beta))) <= -0.05)
		tmp = Float64(Float64(1.0 + beta) / alpha);
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(alpha, beta)
	tmp = 0.0;
	if (((beta - alpha) / (2.0 + (alpha + beta))) <= -0.05)
		tmp = (1.0 + beta) / alpha;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[alpha_, beta_] := If[LessEqual[N[(N[(beta - alpha), $MachinePrecision] / N[(2.0 + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -0.05], N[(N[(1.0 + beta), $MachinePrecision] / alpha), $MachinePrecision], 1.0]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < -0.050000000000000003

   1. Initial program 10.6%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in alpha around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{2 + 2 \cdot \beta}{\alpha}} \]
   4. Step-by-step derivation

      1. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. distribute-lft-in N/A

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

      4. metadata-eval N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{1 + \color{blue}{1} \cdot \beta}{\alpha} \]

      7. *-lft-identity N/A

         \[\leadsto \frac{1 + \color{blue}{\beta}}{\alpha} \]

      8. lower-+.f64 95.7

         \[\leadsto \frac{\color{blue}{1 + \beta}}{\alpha} \]

   5. Applied rewrites 95.7%

      \[\leadsto \color{blue}{\frac{1 + \beta}{\alpha}} \]

   if -0.050000000000000003 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64)))

   1. Initial program 100.0%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in beta around inf

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Applied rewrites 53.7%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 66.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.05:\\ \;\;\;\;\frac{1 + \beta}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 57.6% accurate, 0.9× speedup

Math

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

FPCore

(FPCore (alpha beta)
 :precision binary64
 (if (<= (/ (- beta alpha) (+ 2.0 (+ alpha beta))) -0.5) (/ 1.0 alpha) 1.0))

C

double code(double alpha, double beta) {
	double tmp;
	if (((beta - alpha) / (2.0 + (alpha + beta))) <= -0.5) {
		tmp = 1.0 / alpha;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(alpha, beta)
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8) :: tmp
    if (((beta - alpha) / (2.0d0 + (alpha + beta))) <= (-0.5d0)) then
        tmp = 1.0d0 / alpha
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double alpha, double beta) {
	double tmp;
	if (((beta - alpha) / (2.0 + (alpha + beta))) <= -0.5) {
		tmp = 1.0 / alpha;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(alpha, beta):
	tmp = 0
	if ((beta - alpha) / (2.0 + (alpha + beta))) <= -0.5:
		tmp = 1.0 / alpha
	else:
		tmp = 1.0
	return tmp

Julia

function code(alpha, beta)
	tmp = 0.0
	if (Float64(Float64(beta - alpha) / Float64(2.0 + Float64(alpha + beta))) <= -0.5)
		tmp = Float64(1.0 / alpha);
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(alpha, beta)
	tmp = 0.0;
	if (((beta - alpha) / (2.0 + (alpha + beta))) <= -0.5)
		tmp = 1.0 / alpha;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[alpha_, beta_] := If[LessEqual[N[(N[(beta - alpha), $MachinePrecision] / N[(2.0 + N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -0.5], N[(1.0 / alpha), $MachinePrecision], 1.0]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64))) < -0.5

   1. Initial program 9.5%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in alpha around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{2 + 2 \cdot \beta}{\alpha}} \]
   4. Step-by-step derivation

      1. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. distribute-lft-in N/A

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

      4. metadata-eval N/A

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

      5. associate-*r* N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{1 + \color{blue}{1} \cdot \beta}{\alpha} \]

      7. *-lft-identity N/A

         \[\leadsto \frac{1 + \color{blue}{\beta}}{\alpha} \]

      8. lower-+.f64 96.7

         \[\leadsto \frac{\color{blue}{1 + \beta}}{\alpha} \]

   5. Applied rewrites 96.7%

      \[\leadsto \color{blue}{\frac{1 + \beta}{\alpha}} \]

   6. Taylor expanded in beta around 0

      \[\leadsto \frac{1}{\color{blue}{\alpha}} \]
   7. Step-by-step derivation

   8. Applied rewrites 74.8%

      \[\leadsto \frac{1}{\color{blue}{\alpha}} \]

   if -0.5 < (/.f64 (-.f64 beta alpha) (+.f64 (+.f64 alpha beta) #s(literal 2 binary64)))

   1. Initial program 100.0%

      \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
   2. Add Preprocessing

   3. Taylor expanded in beta around inf

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Applied rewrites 53.5%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 60.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\beta - \alpha}{2 + \left(\alpha + \beta\right)} \leq -0.5:\\ \;\;\;\;\frac{1}{\alpha}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 37.9% accurate, 35.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (alpha beta) :precision binary64 1.0)

C

double code(double alpha, double beta) {
	return 1.0;
}

Fortran

real(8) function code(alpha, beta)
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    code = 1.0d0
end function

Java

public static double code(double alpha, double beta) {
	return 1.0;
}

Python

def code(alpha, beta):
	return 1.0

Julia

function code(alpha, beta)
	return 1.0
end

MATLAB

function tmp = code(alpha, beta)
	tmp = 1.0;
end

Wolfram

code[alpha_, beta_] := 1.0

Derivation

1. Initial program 72.4%

   \[\frac{\frac{\beta - \alpha}{\left(\alpha + \beta\right) + 2} + 1}{2} \]
2. Add Preprocessing

3. Taylor expanded in beta around inf

   \[\leadsto \color{blue}{1} \]
4. Step-by-step derivation

5. Applied rewrites 39.0%

   \[\leadsto \color{blue}{1} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024304 
(FPCore (alpha beta)
  :name "Octave 3.8, jcobi/1"
  :precision binary64
  :pre (and (> alpha -1.0) (> beta -1.0))
  (/ (+ (/ (- beta alpha) (+ (+ alpha beta) 2.0)) 1.0) 2.0))