Octave 3.8, jcobi/3

Percentage Accurate: 94.1% → 99.8%

Time: 5.1s

Alternatives: 17

Speedup: 3.0×

Specification

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

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\alpha + \beta\right) + 2 \cdot 1\\ \frac{\frac{\frac{\left(\left(\alpha + \beta\right) + \beta \cdot \alpha\right) + 1}{t\_0}}{t\_0}}{t\_0 + 1} \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (+ (+ alpha beta) (* 2.0 1.0))))
   (/ (/ (/ (+ (+ (+ alpha beta) (* beta alpha)) 1.0) t_0) t_0) (+ t_0 1.0))))

C

double code(double alpha, double beta) {
	double t_0 = (alpha + beta) + (2.0 * 1.0);
	return (((((alpha + beta) + (beta * alpha)) + 1.0) / t_0) / t_0) / (t_0 + 1.0);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

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

Java

public static double code(double alpha, double beta) {
	double t_0 = (alpha + beta) + (2.0 * 1.0);
	return (((((alpha + beta) + (beta * alpha)) + 1.0) / t_0) / t_0) / (t_0 + 1.0);
}

Python

def code(alpha, beta):
	t_0 = (alpha + beta) + (2.0 * 1.0)
	return (((((alpha + beta) + (beta * alpha)) + 1.0) / t_0) / t_0) / (t_0 + 1.0)

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 94.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\alpha + \beta\right) + 2 \cdot 1\\ \frac{\frac{\frac{\left(\left(\alpha + \beta\right) + \beta \cdot \alpha\right) + 1}{t\_0}}{t\_0}}{t\_0 + 1} \end{array} \end{array} \]

FPCore

(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (+ (+ alpha beta) (* 2.0 1.0))))
   (/ (/ (/ (+ (+ (+ alpha beta) (* beta alpha)) 1.0) t_0) t_0) (+ t_0 1.0))))

C

double code(double alpha, double beta) {
	double t_0 = (alpha + beta) + (2.0 * 1.0);
	return (((((alpha + beta) + (beta * alpha)) + 1.0) / t_0) / t_0) / (t_0 + 1.0);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

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

Java

public static double code(double alpha, double beta) {
	double t_0 = (alpha + beta) + (2.0 * 1.0);
	return (((((alpha + beta) + (beta * alpha)) + 1.0) / t_0) / t_0) / (t_0 + 1.0);
}

Python

def code(alpha, beta):
	t_0 = (alpha + beta) + (2.0 * 1.0)
	return (((((alpha + beta) + (beta * alpha)) + 1.0) / t_0) / t_0) / (t_0 + 1.0)

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} t_0 := \left(\alpha + \beta\right) + 2 \cdot 1\\ t_1 := \left(\alpha + \beta\right) + 2\\ \mathbf{if}\;\beta \leq 10^{+14}:\\ \;\;\;\;\frac{\frac{\frac{\left(\mathsf{fma}\left(\beta, \alpha, \beta + \alpha\right) - 1\right) - -2}{t\_0}}{t\_0}}{t\_0 + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\beta, \frac{\alpha}{\beta + \alpha}, 1\right), \frac{\beta + \alpha}{\left(\beta + \alpha\right) - -2}, \frac{1}{\beta}\right)}{t\_1}}{t\_1 + 1}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (+ (+ alpha beta) (* 2.0 1.0))) (t_1 (+ (+ alpha beta) 2.0)))
   (if (<= beta 1e+14)
     (/
      (/ (/ (- (- (fma beta alpha (+ beta alpha)) 1.0) -2.0) t_0) t_0)
      (+ t_0 1.0))
     (/
      (/
       (fma
        (fma beta (/ alpha (+ beta alpha)) 1.0)
        (/ (+ beta alpha) (- (+ beta alpha) -2.0))
        (/ 1.0 beta))
       t_1)
      (+ t_1 1.0)))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double t_0 = (alpha + beta) + (2.0 * 1.0);
	double t_1 = (alpha + beta) + 2.0;
	double tmp;
	if (beta <= 1e+14) {
		tmp = ((((fma(beta, alpha, (beta + alpha)) - 1.0) - -2.0) / t_0) / t_0) / (t_0 + 1.0);
	} else {
		tmp = (fma(fma(beta, (alpha / (beta + alpha)), 1.0), ((beta + alpha) / ((beta + alpha) - -2.0)), (1.0 / beta)) / t_1) / (t_1 + 1.0);
	}
	return tmp;
}

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	t_0 = Float64(Float64(alpha + beta) + Float64(2.0 * 1.0))
	t_1 = Float64(Float64(alpha + beta) + 2.0)
	tmp = 0.0
	if (beta <= 1e+14)
		tmp = Float64(Float64(Float64(Float64(Float64(fma(beta, alpha, Float64(beta + alpha)) - 1.0) - -2.0) / t_0) / t_0) / Float64(t_0 + 1.0));
	else
		tmp = Float64(Float64(fma(fma(beta, Float64(alpha / Float64(beta + alpha)), 1.0), Float64(Float64(beta + alpha) / Float64(Float64(beta + alpha) - -2.0)), Float64(1.0 / beta)) / t_1) / Float64(t_1 + 1.0));
	end
	return tmp
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := Block[{t$95$0 = N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(alpha + beta), $MachinePrecision] + 2.0), $MachinePrecision]}, If[LessEqual[beta, 1e+14], N[(N[(N[(N[(N[(N[(beta * alpha + N[(beta + alpha), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision] - -2.0), $MachinePrecision] / t$95$0), $MachinePrecision] / t$95$0), $MachinePrecision] / N[(t$95$0 + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(beta * N[(alpha / N[(beta + alpha), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision] * N[(N[(beta + alpha), $MachinePrecision] / N[(N[(beta + alpha), $MachinePrecision] - -2.0), $MachinePrecision]), $MachinePrecision] + N[(1.0 / beta), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision] / N[(t$95$1 + 1.0), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if beta < 1e14

   1. Initial program 94.1%

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

      1. lift-+.f64 N/A

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

      2. fake-sub N/A

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

      3. metadata-eval N/A

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

      4. add-flip N/A

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

      5. lower--.f64 N/A

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

      6. lower--.f64 N/A

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

      7. lift-+.f64 N/A

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

      8. +-commutative N/A

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

      9. lift-*.f64 N/A

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

      10. lower-fma.f64 N/A

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

      11. lift-+.f64 N/A

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

      12. +-commutative N/A

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

      13. lower-+.f64 N/A

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

      14. metadata-eval 94.1

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

   3. Applied rewrites 94.1%

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

   if 1e14 < beta

   1. Initial program 94.1%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. div-add N/A

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

      4. lift-+.f64 N/A

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

      5. sum-to-mult N/A

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

      6. associate-/l* N/A

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

      7. lower-fma.f64 N/A

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

   3. Applied rewrites 99.8%

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

      1. lift-*.f64 N/A

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

      2. metadata-eval 99.8

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

   5. Applied rewrites 99.8%

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

      1. lift-*.f64 N/A

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

      2. metadata-eval 99.8

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

   7. Applied rewrites 99.8%

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

   8. Taylor expanded in beta around inf

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

      1. lower-/.f64 57.1

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

   10. Applied rewrites 57.1%

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

Alternative 2: 99.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} t_0 := \left(\alpha + \beta\right) + 2\\ \frac{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\beta, \frac{\alpha}{\beta + \alpha}, 1\right), \frac{\beta + \alpha}{\left(\beta + \alpha\right) - -2}, \frac{-1}{-2 - \left(\beta + \alpha\right)}\right)}{t\_0}}{t\_0 + 1} \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (+ (+ alpha beta) 2.0)))
   (/
    (/
     (fma
      (fma beta (/ alpha (+ beta alpha)) 1.0)
      (/ (+ beta alpha) (- (+ beta alpha) -2.0))
      (/ -1.0 (- -2.0 (+ beta alpha))))
     t_0)
    (+ t_0 1.0))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double t_0 = (alpha + beta) + 2.0;
	return (fma(fma(beta, (alpha / (beta + alpha)), 1.0), ((beta + alpha) / ((beta + alpha) - -2.0)), (-1.0 / (-2.0 - (beta + alpha)))) / t_0) / (t_0 + 1.0);
}

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	t_0 = Float64(Float64(alpha + beta) + 2.0)
	return Float64(Float64(fma(fma(beta, Float64(alpha / Float64(beta + alpha)), 1.0), Float64(Float64(beta + alpha) / Float64(Float64(beta + alpha) - -2.0)), Float64(-1.0 / Float64(-2.0 - Float64(beta + alpha)))) / t_0) / Float64(t_0 + 1.0))
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := Block[{t$95$0 = N[(N[(alpha + beta), $MachinePrecision] + 2.0), $MachinePrecision]}, N[(N[(N[(N[(beta * N[(alpha / N[(beta + alpha), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision] * N[(N[(beta + alpha), $MachinePrecision] / N[(N[(beta + alpha), $MachinePrecision] - -2.0), $MachinePrecision]), $MachinePrecision] + N[(-1.0 / N[(-2.0 - N[(beta + alpha), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t$95$0), $MachinePrecision] / N[(t$95$0 + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 94.1%

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

   1. lift-/.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. div-add N/A

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

   4. lift-+.f64 N/A

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

   5. sum-to-mult N/A

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

   6. associate-/l* N/A

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

   7. lower-fma.f64 N/A

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

3. Applied rewrites 99.8%

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

   1. lift-*.f64 N/A

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

   2. metadata-eval 99.8

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

5. Applied rewrites 99.8%

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

   1. lift-*.f64 N/A

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

   2. metadata-eval 99.8

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

7. Applied rewrites 99.8%

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

Alternative 3: 99.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} t_0 := \left(\alpha + \beta\right) + 2 \cdot 1\\ \mathbf{if}\;\beta \leq 5 \cdot 10^{+136}:\\ \;\;\;\;\frac{\frac{\frac{\left(\mathsf{fma}\left(\beta, \alpha, \beta + \alpha\right) - 1\right) - -2}{t\_0}}{t\_0}}{t\_0 + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\beta}}{\left(\alpha + \beta\right) - -3}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (+ (+ alpha beta) (* 2.0 1.0))))
   (if (<= beta 5e+136)
     (/
      (/ (/ (- (- (fma beta alpha (+ beta alpha)) 1.0) -2.0) t_0) t_0)
      (+ t_0 1.0))
     (/ (/ (- alpha -1.0) beta) (- (+ alpha beta) -3.0)))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double t_0 = (alpha + beta) + (2.0 * 1.0);
	double tmp;
	if (beta <= 5e+136) {
		tmp = ((((fma(beta, alpha, (beta + alpha)) - 1.0) - -2.0) / t_0) / t_0) / (t_0 + 1.0);
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	t_0 = Float64(Float64(alpha + beta) + Float64(2.0 * 1.0))
	tmp = 0.0
	if (beta <= 5e+136)
		tmp = Float64(Float64(Float64(Float64(Float64(fma(beta, alpha, Float64(beta + alpha)) - 1.0) - -2.0) / t_0) / t_0) / Float64(t_0 + 1.0));
	else
		tmp = Float64(Float64(Float64(alpha - -1.0) / beta) / Float64(Float64(alpha + beta) - -3.0));
	end
	return tmp
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := Block[{t$95$0 = N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[beta, 5e+136], N[(N[(N[(N[(N[(N[(beta * alpha + N[(beta + alpha), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision] - -2.0), $MachinePrecision] / t$95$0), $MachinePrecision] / t$95$0), $MachinePrecision] / N[(t$95$0 + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(alpha - -1.0), $MachinePrecision] / beta), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -3.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if beta < 5.0000000000000002e136

   1. Initial program 94.1%

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

      1. lift-+.f64 N/A

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

      2. fake-sub N/A

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

      3. metadata-eval N/A

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

      4. add-flip N/A

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

      5. lower--.f64 N/A

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

      6. lower--.f64 N/A

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

      7. lift-+.f64 N/A

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

      8. +-commutative N/A

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

      9. lift-*.f64 N/A

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

      10. lower-fma.f64 N/A

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

      11. lift-+.f64 N/A

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

      12. +-commutative N/A

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

      13. lower-+.f64 N/A

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

      14. metadata-eval 94.1

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

   3. Applied rewrites 94.1%

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

   if 5.0000000000000002e136 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 56.1

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

   4. Applied rewrites 56.1%

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

      1. metadata-eval 56.1

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

      2. metadata-eval 56.1

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

   6. Applied rewrites 56.1%

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

Alternative 4: 99.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} t_0 := \left(\alpha + \beta\right) + 2 \cdot 1\\ \mathbf{if}\;\beta \leq 5 \cdot 10^{+136}:\\ \;\;\;\;\frac{\frac{\frac{\left(\left(\alpha + \beta\right) + \beta \cdot \alpha\right) + 1}{t\_0}}{t\_0}}{t\_0 + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\beta}}{\left(\alpha + \beta\right) - -3}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (+ (+ alpha beta) (* 2.0 1.0))))
   (if (<= beta 5e+136)
     (/ (/ (/ (+ (+ (+ alpha beta) (* beta alpha)) 1.0) t_0) t_0) (+ t_0 1.0))
     (/ (/ (- alpha -1.0) beta) (- (+ alpha beta) -3.0)))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double t_0 = (alpha + beta) + (2.0 * 1.0);
	double tmp;
	if (beta <= 5e+136) {
		tmp = (((((alpha + beta) + (beta * alpha)) + 1.0) / t_0) / t_0) / (t_0 + 1.0);
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Fortran

NOTE: alpha and beta should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(alpha, beta)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (alpha + beta) + (2.0d0 * 1.0d0)
    if (beta <= 5d+136) then
        tmp = (((((alpha + beta) + (beta * alpha)) + 1.0d0) / t_0) / t_0) / (t_0 + 1.0d0)
    else
        tmp = ((alpha - (-1.0d0)) / beta) / ((alpha + beta) - (-3.0d0))
    end if
    code = tmp
end function

Java

assert alpha < beta;
public static double code(double alpha, double beta) {
	double t_0 = (alpha + beta) + (2.0 * 1.0);
	double tmp;
	if (beta <= 5e+136) {
		tmp = (((((alpha + beta) + (beta * alpha)) + 1.0) / t_0) / t_0) / (t_0 + 1.0);
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Python

[alpha, beta] = sort([alpha, beta])
def code(alpha, beta):
	t_0 = (alpha + beta) + (2.0 * 1.0)
	tmp = 0
	if beta <= 5e+136:
		tmp = (((((alpha + beta) + (beta * alpha)) + 1.0) / t_0) / t_0) / (t_0 + 1.0)
	else:
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0)
	return tmp

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	t_0 = Float64(Float64(alpha + beta) + Float64(2.0 * 1.0))
	tmp = 0.0
	if (beta <= 5e+136)
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(alpha + beta) + Float64(beta * alpha)) + 1.0) / t_0) / t_0) / Float64(t_0 + 1.0));
	else
		tmp = Float64(Float64(Float64(alpha - -1.0) / beta) / Float64(Float64(alpha + beta) - -3.0));
	end
	return tmp
end

MATLAB

alpha, beta = num2cell(sort([alpha, beta])){:}
function tmp_2 = code(alpha, beta)
	t_0 = (alpha + beta) + (2.0 * 1.0);
	tmp = 0.0;
	if (beta <= 5e+136)
		tmp = (((((alpha + beta) + (beta * alpha)) + 1.0) / t_0) / t_0) / (t_0 + 1.0);
	else
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := Block[{t$95$0 = N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[beta, 5e+136], N[(N[(N[(N[(N[(N[(alpha + beta), $MachinePrecision] + N[(beta * alpha), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision] / t$95$0), $MachinePrecision] / t$95$0), $MachinePrecision] / N[(t$95$0 + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(alpha - -1.0), $MachinePrecision] / beta), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -3.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if beta < 5.0000000000000002e136

   1. Initial program 94.1%

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

   if 5.0000000000000002e136 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 56.1

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

   4. Applied rewrites 56.1%

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

      1. metadata-eval 56.1

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

      2. metadata-eval 56.1

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

   6. Applied rewrites 56.1%

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

Alternative 5: 99.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} \mathbf{if}\;\beta \leq 2 \cdot 10^{+130}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(\alpha - -1, \beta, \alpha\right) - -1}{\left(\alpha - \left(-2 - \beta\right)\right) \cdot \left(\left(-2 - \beta\right) - \alpha\right)}}{-3 - \left(\alpha + \beta\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\beta}}{\left(\alpha + \beta\right) - -3}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (if (<= beta 2e+130)
   (/
    (/
     (- (fma (- alpha -1.0) beta alpha) -1.0)
     (* (- alpha (- -2.0 beta)) (- (- -2.0 beta) alpha)))
    (- -3.0 (+ alpha beta)))
   (/ (/ (- alpha -1.0) beta) (- (+ alpha beta) -3.0))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double tmp;
	if (beta <= 2e+130) {
		tmp = ((fma((alpha - -1.0), beta, alpha) - -1.0) / ((alpha - (-2.0 - beta)) * ((-2.0 - beta) - alpha))) / (-3.0 - (alpha + beta));
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	tmp = 0.0
	if (beta <= 2e+130)
		tmp = Float64(Float64(Float64(fma(Float64(alpha - -1.0), beta, alpha) - -1.0) / Float64(Float64(alpha - Float64(-2.0 - beta)) * Float64(Float64(-2.0 - beta) - alpha))) / Float64(-3.0 - Float64(alpha + beta)));
	else
		tmp = Float64(Float64(Float64(alpha - -1.0) / beta) / Float64(Float64(alpha + beta) - -3.0));
	end
	return tmp
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := If[LessEqual[beta, 2e+130], N[(N[(N[(N[(N[(alpha - -1.0), $MachinePrecision] * beta + alpha), $MachinePrecision] - -1.0), $MachinePrecision] / N[(N[(alpha - N[(-2.0 - beta), $MachinePrecision]), $MachinePrecision] * N[(N[(-2.0 - beta), $MachinePrecision] - alpha), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(-3.0 - N[(alpha + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(alpha - -1.0), $MachinePrecision] / beta), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 2.0000000000000001e130

   1. Initial program 94.1%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. div-add N/A

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

      4. lift-+.f64 N/A

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

      5. sum-to-mult N/A

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

      6. associate-/l* N/A

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

      7. lower-fma.f64 N/A

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

   3. Applied rewrites 99.8%

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

   5. Applied rewrites 92.7%

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

   if 2.0000000000000001e130 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 56.1

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

   4. Applied rewrites 56.1%

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

      1. metadata-eval 56.1

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

      2. metadata-eval 56.1

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

   6. Applied rewrites 56.1%

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

Alternative 6: 99.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} t_0 := \left(\beta + \alpha\right) - -2\\ \mathbf{if}\;\beta \leq 1.02 \cdot 10^{+18}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\beta, \alpha, \beta + \alpha\right) - -1}{t\_0 \cdot \left(\left(\left(\beta + \alpha\right) - -3\right) \cdot t\_0\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\beta}}{\left(\alpha + \beta\right) - -3}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (let* ((t_0 (- (+ beta alpha) -2.0)))
   (if (<= beta 1.02e+18)
     (/
      (- (fma beta alpha (+ beta alpha)) -1.0)
      (* t_0 (* (- (+ beta alpha) -3.0) t_0)))
     (/ (/ (- alpha -1.0) beta) (- (+ alpha beta) -3.0)))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double t_0 = (beta + alpha) - -2.0;
	double tmp;
	if (beta <= 1.02e+18) {
		tmp = (fma(beta, alpha, (beta + alpha)) - -1.0) / (t_0 * (((beta + alpha) - -3.0) * t_0));
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	t_0 = Float64(Float64(beta + alpha) - -2.0)
	tmp = 0.0
	if (beta <= 1.02e+18)
		tmp = Float64(Float64(fma(beta, alpha, Float64(beta + alpha)) - -1.0) / Float64(t_0 * Float64(Float64(Float64(beta + alpha) - -3.0) * t_0)));
	else
		tmp = Float64(Float64(Float64(alpha - -1.0) / beta) / Float64(Float64(alpha + beta) - -3.0));
	end
	return tmp
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := Block[{t$95$0 = N[(N[(beta + alpha), $MachinePrecision] - -2.0), $MachinePrecision]}, If[LessEqual[beta, 1.02e+18], N[(N[(N[(beta * alpha + N[(beta + alpha), $MachinePrecision]), $MachinePrecision] - -1.0), $MachinePrecision] / N[(t$95$0 * N[(N[(N[(beta + alpha), $MachinePrecision] - -3.0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(alpha - -1.0), $MachinePrecision] / beta), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -3.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if beta < 1.02e18

   1. Initial program 94.1%

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

      1. lift-/.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-/l/ N/A

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

      4. lift-/.f64 N/A

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

      5. associate-/l/ N/A

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

      6. lower-/.f64 N/A

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

   3. Applied rewrites 84.3%

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

   if 1.02e18 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 56.1

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

   4. Applied rewrites 56.1%

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

      1. metadata-eval 56.1

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

      2. metadata-eval 56.1

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

   6. Applied rewrites 56.1%

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

Alternative 7: 98.3% accurate, 1.8× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} \mathbf{if}\;\beta \leq 1.66 \cdot 10^{+16}:\\ \;\;\;\;\frac{\frac{\frac{1 + \beta}{2 + \beta}}{2 + \beta}}{\left(2 + \beta\right) + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\beta}}{\left(\alpha + \beta\right) - -3}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (if (<= beta 1.66e+16)
   (/ (/ (/ (+ 1.0 beta) (+ 2.0 beta)) (+ 2.0 beta)) (+ (+ 2.0 beta) 1.0))
   (/ (/ (- alpha -1.0) beta) (- (+ alpha beta) -3.0))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double tmp;
	if (beta <= 1.66e+16) {
		tmp = (((1.0 + beta) / (2.0 + beta)) / (2.0 + beta)) / ((2.0 + beta) + 1.0);
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Fortran

NOTE: alpha and beta should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(alpha, beta)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8) :: tmp
    if (beta <= 1.66d+16) then
        tmp = (((1.0d0 + beta) / (2.0d0 + beta)) / (2.0d0 + beta)) / ((2.0d0 + beta) + 1.0d0)
    else
        tmp = ((alpha - (-1.0d0)) / beta) / ((alpha + beta) - (-3.0d0))
    end if
    code = tmp
end function

Java

assert alpha < beta;
public static double code(double alpha, double beta) {
	double tmp;
	if (beta <= 1.66e+16) {
		tmp = (((1.0 + beta) / (2.0 + beta)) / (2.0 + beta)) / ((2.0 + beta) + 1.0);
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Python

[alpha, beta] = sort([alpha, beta])
def code(alpha, beta):
	tmp = 0
	if beta <= 1.66e+16:
		tmp = (((1.0 + beta) / (2.0 + beta)) / (2.0 + beta)) / ((2.0 + beta) + 1.0)
	else:
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0)
	return tmp

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	tmp = 0.0
	if (beta <= 1.66e+16)
		tmp = Float64(Float64(Float64(Float64(1.0 + beta) / Float64(2.0 + beta)) / Float64(2.0 + beta)) / Float64(Float64(2.0 + beta) + 1.0));
	else
		tmp = Float64(Float64(Float64(alpha - -1.0) / beta) / Float64(Float64(alpha + beta) - -3.0));
	end
	return tmp
end

MATLAB

alpha, beta = num2cell(sort([alpha, beta])){:}
function tmp_2 = code(alpha, beta)
	tmp = 0.0;
	if (beta <= 1.66e+16)
		tmp = (((1.0 + beta) / (2.0 + beta)) / (2.0 + beta)) / ((2.0 + beta) + 1.0);
	else
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := If[LessEqual[beta, 1.66e+16], N[(N[(N[(N[(1.0 + beta), $MachinePrecision] / N[(2.0 + beta), $MachinePrecision]), $MachinePrecision] / N[(2.0 + beta), $MachinePrecision]), $MachinePrecision] / N[(N[(2.0 + beta), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(alpha - -1.0), $MachinePrecision] / beta), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 1.66e16

   1. Initial program 94.1%

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

   2. Taylor expanded in alpha around 0

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

      1. lower-+.f64 92.8

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

   4. Applied rewrites 92.8%

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

   5. Taylor expanded in alpha around 0

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

      1. lower-+.f64 92.8

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

   7. Applied rewrites 92.8%

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

   8. Taylor expanded in alpha around 0

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

      1. lower-+.f64 92.7

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

   10. Applied rewrites 92.7%

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

   11. Taylor expanded in alpha around 0

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

       1. lower-+.f64 93.0

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

   13. Applied rewrites 93.0%

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

   if 1.66e16 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 56.1

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

   4. Applied rewrites 56.1%

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

      1. metadata-eval 56.1

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

      2. metadata-eval 56.1

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

   6. Applied rewrites 56.1%

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

Alternative 8: 97.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} \mathbf{if}\;\beta \leq 2.2:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\left(\alpha - -2\right) \cdot \left(\alpha - -2\right)}}{\alpha - -3}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{-1 \cdot \left(-1 \cdot \alpha - 1\right)}{2 + \beta}}{\left(2 + \beta\right) + 1}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (if (<= beta 2.2)
   (/ (/ (- alpha -1.0) (* (- alpha -2.0) (- alpha -2.0))) (- alpha -3.0))
   (/ (/ (* -1.0 (- (* -1.0 alpha) 1.0)) (+ 2.0 beta)) (+ (+ 2.0 beta) 1.0))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double tmp;
	if (beta <= 2.2) {
		tmp = ((alpha - -1.0) / ((alpha - -2.0) * (alpha - -2.0))) / (alpha - -3.0);
	} else {
		tmp = ((-1.0 * ((-1.0 * alpha) - 1.0)) / (2.0 + beta)) / ((2.0 + beta) + 1.0);
	}
	return tmp;
}

Fortran

NOTE: alpha and beta should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(alpha, beta)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8) :: tmp
    if (beta <= 2.2d0) then
        tmp = ((alpha - (-1.0d0)) / ((alpha - (-2.0d0)) * (alpha - (-2.0d0)))) / (alpha - (-3.0d0))
    else
        tmp = (((-1.0d0) * (((-1.0d0) * alpha) - 1.0d0)) / (2.0d0 + beta)) / ((2.0d0 + beta) + 1.0d0)
    end if
    code = tmp
end function

Java

assert alpha < beta;
public static double code(double alpha, double beta) {
	double tmp;
	if (beta <= 2.2) {
		tmp = ((alpha - -1.0) / ((alpha - -2.0) * (alpha - -2.0))) / (alpha - -3.0);
	} else {
		tmp = ((-1.0 * ((-1.0 * alpha) - 1.0)) / (2.0 + beta)) / ((2.0 + beta) + 1.0);
	}
	return tmp;
}

Python

[alpha, beta] = sort([alpha, beta])
def code(alpha, beta):
	tmp = 0
	if beta <= 2.2:
		tmp = ((alpha - -1.0) / ((alpha - -2.0) * (alpha - -2.0))) / (alpha - -3.0)
	else:
		tmp = ((-1.0 * ((-1.0 * alpha) - 1.0)) / (2.0 + beta)) / ((2.0 + beta) + 1.0)
	return tmp

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	tmp = 0.0
	if (beta <= 2.2)
		tmp = Float64(Float64(Float64(alpha - -1.0) / Float64(Float64(alpha - -2.0) * Float64(alpha - -2.0))) / Float64(alpha - -3.0));
	else
		tmp = Float64(Float64(Float64(-1.0 * Float64(Float64(-1.0 * alpha) - 1.0)) / Float64(2.0 + beta)) / Float64(Float64(2.0 + beta) + 1.0));
	end
	return tmp
end

MATLAB

alpha, beta = num2cell(sort([alpha, beta])){:}
function tmp_2 = code(alpha, beta)
	tmp = 0.0;
	if (beta <= 2.2)
		tmp = ((alpha - -1.0) / ((alpha - -2.0) * (alpha - -2.0))) / (alpha - -3.0);
	else
		tmp = ((-1.0 * ((-1.0 * alpha) - 1.0)) / (2.0 + beta)) / ((2.0 + beta) + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := If[LessEqual[beta, 2.2], N[(N[(N[(alpha - -1.0), $MachinePrecision] / N[(N[(alpha - -2.0), $MachinePrecision] * N[(alpha - -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(alpha - -3.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(-1.0 * N[(N[(-1.0 * alpha), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision] / N[(2.0 + beta), $MachinePrecision]), $MachinePrecision] / N[(N[(2.0 + beta), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 2.2000000000000002

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around 0

      \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 48.2

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

   4. Applied rewrites 48.2%

      \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/r* N/A

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

      4. lower-/.f64 N/A

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

      5. lower-/.f64 47.1

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

      6. lift-+.f64 N/A

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

      7. +-commutative N/A

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

      8. add-flip N/A

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

      9. metadata-eval N/A

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

      10. lower--.f64 47.1

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

      11. lift-pow.f64 N/A

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

      12. unpow2 N/A

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

      13. lower-*.f64 47.1

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

      14. lift-+.f64 N/A

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

      15. +-commutative N/A

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

      16. add-flip-rev N/A

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

      17. metadata-eval N/A

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

      18. lower--.f64 47.1

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

      19. lift-+.f64 N/A

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

      20. +-commutative N/A

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

      21. add-flip-rev N/A

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

      22. metadata-eval N/A

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

      23. lower--.f64 47.1

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

      24. lift-+.f64 N/A

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

      25. +-commutative N/A

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

      26. add-flip N/A

          \[\leadsto \frac{\frac{\alpha - -1}{\left(\alpha - -2\right) \cdot \left(\alpha - -2\right)}}{\alpha - \color{blue}{\left(\mathsf{neg}\left(3\right)\right)}} \]

      27. metadata-eval N/A

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

      28. lower--.f64 47.1

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

   6. Applied rewrites 47.1%

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

   if 2.2000000000000002 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in alpha around 0

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

      1. lower-+.f64 92.8

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

   4. Applied rewrites 92.8%

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

   5. Taylor expanded in alpha around 0

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

      1. lower-+.f64 92.8

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

   7. Applied rewrites 92.8%

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

   8. Taylor expanded in alpha around 0

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

      1. lower-+.f64 92.7

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

   10. Applied rewrites 92.7%

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

   11. Taylor expanded in beta around -inf

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

       1. lower-*.f64 N/A

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

       2. lower--.f64 N/A

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

       3. lower-*.f64 62.4

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

   13. Applied rewrites 62.4%

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

Alternative 9: 97.4% accurate, 2.0× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} \mathbf{if}\;\beta \leq 2.95:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\left(\alpha - -2\right) \cdot \left(\alpha - -2\right)}}{\alpha - -3}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\beta}}{\left(\alpha + \beta\right) - -3}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (if (<= beta 2.95)
   (/ (/ (- alpha -1.0) (* (- alpha -2.0) (- alpha -2.0))) (- alpha -3.0))
   (/ (/ (- alpha -1.0) beta) (- (+ alpha beta) -3.0))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double tmp;
	if (beta <= 2.95) {
		tmp = ((alpha - -1.0) / ((alpha - -2.0) * (alpha - -2.0))) / (alpha - -3.0);
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Fortran

NOTE: alpha and beta should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(alpha, beta)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8) :: tmp
    if (beta <= 2.95d0) then
        tmp = ((alpha - (-1.0d0)) / ((alpha - (-2.0d0)) * (alpha - (-2.0d0)))) / (alpha - (-3.0d0))
    else
        tmp = ((alpha - (-1.0d0)) / beta) / ((alpha + beta) - (-3.0d0))
    end if
    code = tmp
end function

Java

assert alpha < beta;
public static double code(double alpha, double beta) {
	double tmp;
	if (beta <= 2.95) {
		tmp = ((alpha - -1.0) / ((alpha - -2.0) * (alpha - -2.0))) / (alpha - -3.0);
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Python

[alpha, beta] = sort([alpha, beta])
def code(alpha, beta):
	tmp = 0
	if beta <= 2.95:
		tmp = ((alpha - -1.0) / ((alpha - -2.0) * (alpha - -2.0))) / (alpha - -3.0)
	else:
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0)
	return tmp

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	tmp = 0.0
	if (beta <= 2.95)
		tmp = Float64(Float64(Float64(alpha - -1.0) / Float64(Float64(alpha - -2.0) * Float64(alpha - -2.0))) / Float64(alpha - -3.0));
	else
		tmp = Float64(Float64(Float64(alpha - -1.0) / beta) / Float64(Float64(alpha + beta) - -3.0));
	end
	return tmp
end

MATLAB

alpha, beta = num2cell(sort([alpha, beta])){:}
function tmp_2 = code(alpha, beta)
	tmp = 0.0;
	if (beta <= 2.95)
		tmp = ((alpha - -1.0) / ((alpha - -2.0) * (alpha - -2.0))) / (alpha - -3.0);
	else
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := If[LessEqual[beta, 2.95], N[(N[(N[(alpha - -1.0), $MachinePrecision] / N[(N[(alpha - -2.0), $MachinePrecision] * N[(alpha - -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(alpha - -3.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(alpha - -1.0), $MachinePrecision] / beta), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 2.9500000000000002

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around 0

      \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 48.2

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

   4. Applied rewrites 48.2%

      \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/r* N/A

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

      4. lower-/.f64 N/A

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

      5. lower-/.f64 47.1

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

      6. lift-+.f64 N/A

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

      7. +-commutative N/A

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

      8. add-flip N/A

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

      9. metadata-eval N/A

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

      10. lower--.f64 47.1

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

      11. lift-pow.f64 N/A

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

      12. unpow2 N/A

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

      13. lower-*.f64 47.1

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

      14. lift-+.f64 N/A

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

      15. +-commutative N/A

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

      16. add-flip-rev N/A

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

      17. metadata-eval N/A

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

      18. lower--.f64 47.1

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

      19. lift-+.f64 N/A

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

      20. +-commutative N/A

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

      21. add-flip-rev N/A

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

      22. metadata-eval N/A

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

      23. lower--.f64 47.1

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

      24. lift-+.f64 N/A

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

      25. +-commutative N/A

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

      26. add-flip N/A

          \[\leadsto \frac{\frac{\alpha - -1}{\left(\alpha - -2\right) \cdot \left(\alpha - -2\right)}}{\alpha - \color{blue}{\left(\mathsf{neg}\left(3\right)\right)}} \]

      27. metadata-eval N/A

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

      28. lower--.f64 47.1

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

   6. Applied rewrites 47.1%

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

   if 2.9500000000000002 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 56.1

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

   4. Applied rewrites 56.1%

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

      1. metadata-eval 56.1

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

      2. metadata-eval 56.1

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

   6. Applied rewrites 56.1%

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

Alternative 10: 97.4% accurate, 2.1× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} \mathbf{if}\;\beta \leq 2.9:\\ \;\;\;\;\frac{\alpha - -1}{\left(3 + \alpha\right) \cdot \mathsf{fma}\left(4 + \alpha, \alpha, 4\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\beta}}{\left(\alpha + \beta\right) - -3}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (if (<= beta 2.9)
   (/ (- alpha -1.0) (* (+ 3.0 alpha) (fma (+ 4.0 alpha) alpha 4.0)))
   (/ (/ (- alpha -1.0) beta) (- (+ alpha beta) -3.0))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double tmp;
	if (beta <= 2.9) {
		tmp = (alpha - -1.0) / ((3.0 + alpha) * fma((4.0 + alpha), alpha, 4.0));
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	tmp = 0.0
	if (beta <= 2.9)
		tmp = Float64(Float64(alpha - -1.0) / Float64(Float64(3.0 + alpha) * fma(Float64(4.0 + alpha), alpha, 4.0)));
	else
		tmp = Float64(Float64(Float64(alpha - -1.0) / beta) / Float64(Float64(alpha + beta) - -3.0));
	end
	return tmp
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := If[LessEqual[beta, 2.9], N[(N[(alpha - -1.0), $MachinePrecision] / N[(N[(3.0 + alpha), $MachinePrecision] * N[(N[(4.0 + alpha), $MachinePrecision] * alpha + 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(alpha - -1.0), $MachinePrecision] / beta), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 2.89999999999999991

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around 0

      \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 48.2

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

   4. Applied rewrites 48.2%

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

   5. Taylor expanded in alpha around 0

      \[\leadsto \frac{1 + \alpha}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right) \cdot \left(\color{blue}{3} + \alpha\right)} \]
   6. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto \frac{1 + \alpha}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right) \cdot \left(3 + \alpha\right)} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1 + \alpha}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right) \cdot \left(3 + \alpha\right)} \]

      3. lower-+.f64 48.2

         \[\leadsto \frac{1 + \alpha}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right) \cdot \left(3 + \alpha\right)} \]

   7. Applied rewrites 48.2%

      \[\leadsto \frac{1 + \alpha}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right) \cdot \left(\color{blue}{3} + \alpha\right)} \]
   8. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{1 + \alpha}{\color{blue}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right)} \cdot \left(3 + \alpha\right)} \]

      2. +-commutative N/A

         \[\leadsto \frac{\alpha + 1}{\color{blue}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right)} \cdot \left(3 + \alpha\right)} \]

      3. metadata-eval N/A

         \[\leadsto \frac{\alpha + \left(\mathsf{neg}\left(-1\right)\right)}{\left(4 + \color{blue}{\alpha \cdot \left(4 + \alpha\right)}\right) \cdot \left(3 + \alpha\right)} \]

      4. sub-flip N/A

         \[\leadsto \frac{\alpha - -1}{\color{blue}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right)} \cdot \left(3 + \alpha\right)} \]

      5. lift--.f64 48.2

         \[\leadsto \frac{\alpha - -1}{\color{blue}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right)} \cdot \left(3 + \alpha\right)} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\alpha - -1}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right) \cdot \color{blue}{\left(3 + \alpha\right)}} \]

      7. *-commutative N/A

         \[\leadsto \frac{\alpha - -1}{\left(3 + \alpha\right) \cdot \color{blue}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right)}} \]

      8. lower-*.f64 48.2

         \[\leadsto \frac{\alpha - -1}{\left(3 + \alpha\right) \cdot \color{blue}{\left(4 + \alpha \cdot \left(4 + \alpha\right)\right)}} \]

      9. lift-+.f64 N/A

         \[\leadsto \frac{\alpha - -1}{\left(3 + \alpha\right) \cdot \left(4 + \alpha \cdot \color{blue}{\left(4 + \alpha\right)}\right)} \]

      10. +-commutative N/A

          \[\leadsto \frac{\alpha - -1}{\left(3 + \alpha\right) \cdot \left(\alpha \cdot \left(4 + \alpha\right) + 4\right)} \]

      11. lift-*.f64 N/A

          \[\leadsto \frac{\alpha - -1}{\left(3 + \alpha\right) \cdot \left(\alpha \cdot \left(4 + \alpha\right) + 4\right)} \]

      12. *-commutative N/A

          \[\leadsto \frac{\alpha - -1}{\left(3 + \alpha\right) \cdot \left(\left(4 + \alpha\right) \cdot \alpha + 4\right)} \]

      13. lower-fma.f64 48.2

          \[\leadsto \frac{\alpha - -1}{\left(3 + \alpha\right) \cdot \mathsf{fma}\left(4 + \alpha, \alpha, 4\right)} \]

   9. Applied rewrites 48.2%

      \[\leadsto \color{blue}{\frac{\alpha - -1}{\left(3 + \alpha\right) \cdot \mathsf{fma}\left(4 + \alpha, \alpha, 4\right)}} \]

   if 2.89999999999999991 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 56.1

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

   4. Applied rewrites 56.1%

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

      1. metadata-eval 56.1

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

      2. metadata-eval 56.1

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

   6. Applied rewrites 56.1%

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

Alternative 11: 97.2% accurate, 2.3× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} \mathbf{if}\;\beta \leq 2.1:\\ \;\;\;\;0.08333333333333333 + \alpha \cdot \left(\alpha \cdot \left(0.024691358024691357 \cdot \alpha - 0.011574074074074073\right) - 0.027777777777777776\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\beta}}{\left(\alpha + \beta\right) - -3}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (if (<= beta 2.1)
   (+
    0.08333333333333333
    (*
     alpha
     (-
      (* alpha (- (* 0.024691358024691357 alpha) 0.011574074074074073))
      0.027777777777777776)))
   (/ (/ (- alpha -1.0) beta) (- (+ alpha beta) -3.0))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double tmp;
	if (beta <= 2.1) {
		tmp = 0.08333333333333333 + (alpha * ((alpha * ((0.024691358024691357 * alpha) - 0.011574074074074073)) - 0.027777777777777776));
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Fortran

NOTE: alpha and beta should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(alpha, beta)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8) :: tmp
    if (beta <= 2.1d0) then
        tmp = 0.08333333333333333d0 + (alpha * ((alpha * ((0.024691358024691357d0 * alpha) - 0.011574074074074073d0)) - 0.027777777777777776d0))
    else
        tmp = ((alpha - (-1.0d0)) / beta) / ((alpha + beta) - (-3.0d0))
    end if
    code = tmp
end function

Java

assert alpha < beta;
public static double code(double alpha, double beta) {
	double tmp;
	if (beta <= 2.1) {
		tmp = 0.08333333333333333 + (alpha * ((alpha * ((0.024691358024691357 * alpha) - 0.011574074074074073)) - 0.027777777777777776));
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Python

[alpha, beta] = sort([alpha, beta])
def code(alpha, beta):
	tmp = 0
	if beta <= 2.1:
		tmp = 0.08333333333333333 + (alpha * ((alpha * ((0.024691358024691357 * alpha) - 0.011574074074074073)) - 0.027777777777777776))
	else:
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0)
	return tmp

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	tmp = 0.0
	if (beta <= 2.1)
		tmp = Float64(0.08333333333333333 + Float64(alpha * Float64(Float64(alpha * Float64(Float64(0.024691358024691357 * alpha) - 0.011574074074074073)) - 0.027777777777777776)));
	else
		tmp = Float64(Float64(Float64(alpha - -1.0) / beta) / Float64(Float64(alpha + beta) - -3.0));
	end
	return tmp
end

MATLAB

alpha, beta = num2cell(sort([alpha, beta])){:}
function tmp_2 = code(alpha, beta)
	tmp = 0.0;
	if (beta <= 2.1)
		tmp = 0.08333333333333333 + (alpha * ((alpha * ((0.024691358024691357 * alpha) - 0.011574074074074073)) - 0.027777777777777776));
	else
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := If[LessEqual[beta, 2.1], N[(0.08333333333333333 + N[(alpha * N[(N[(alpha * N[(N[(0.024691358024691357 * alpha), $MachinePrecision] - 0.011574074074074073), $MachinePrecision]), $MachinePrecision] - 0.027777777777777776), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(alpha - -1.0), $MachinePrecision] / beta), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 2.10000000000000009

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around 0

      \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 48.2

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

   4. Applied rewrites 48.2%

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

   5. Taylor expanded in alpha around 0

      \[\leadsto \frac{1}{12} + \color{blue}{\alpha \cdot \left(\alpha \cdot \left(\frac{2}{81} \cdot \alpha - \frac{5}{432}\right) - \frac{1}{36}\right)} \]
   6. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto \frac{1}{12} + \alpha \cdot \color{blue}{\left(\alpha \cdot \left(\frac{2}{81} \cdot \alpha - \frac{5}{432}\right) - \frac{1}{36}\right)} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{12} + \alpha \cdot \left(\alpha \cdot \left(\frac{2}{81} \cdot \alpha - \frac{5}{432}\right) - \color{blue}{\frac{1}{36}}\right) \]

      3. lower--.f64 N/A

         \[\leadsto \frac{1}{12} + \alpha \cdot \left(\alpha \cdot \left(\frac{2}{81} \cdot \alpha - \frac{5}{432}\right) - \frac{1}{36}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \frac{1}{12} + \alpha \cdot \left(\alpha \cdot \left(\frac{2}{81} \cdot \alpha - \frac{5}{432}\right) - \frac{1}{36}\right) \]

      5. lower--.f64 N/A

         \[\leadsto \frac{1}{12} + \alpha \cdot \left(\alpha \cdot \left(\frac{2}{81} \cdot \alpha - \frac{5}{432}\right) - \frac{1}{36}\right) \]

      6. lower-*.f64 45.3

         \[\leadsto 0.08333333333333333 + \alpha \cdot \left(\alpha \cdot \left(0.024691358024691357 \cdot \alpha - 0.011574074074074073\right) - 0.027777777777777776\right) \]

   7. Applied rewrites 45.3%

      \[\leadsto 0.08333333333333333 + \color{blue}{\alpha \cdot \left(\alpha \cdot \left(0.024691358024691357 \cdot \alpha - 0.011574074074074073\right) - 0.027777777777777776\right)} \]

   if 2.10000000000000009 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 56.1

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

   4. Applied rewrites 56.1%

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

      1. metadata-eval 56.1

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

      2. metadata-eval 56.1

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

   6. Applied rewrites 56.1%

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

Alternative 12: 97.1% accurate, 2.4× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} \mathbf{if}\;\beta \leq 2.35:\\ \;\;\;\;\frac{\alpha - -1}{\mathsf{fma}\left(\mathsf{fma}\left(7, \alpha, 16\right), \alpha, 12\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\beta}}{\left(\alpha + \beta\right) - -3}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (if (<= beta 2.35)
   (/ (- alpha -1.0) (fma (fma 7.0 alpha 16.0) alpha 12.0))
   (/ (/ (- alpha -1.0) beta) (- (+ alpha beta) -3.0))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double tmp;
	if (beta <= 2.35) {
		tmp = (alpha - -1.0) / fma(fma(7.0, alpha, 16.0), alpha, 12.0);
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	tmp = 0.0
	if (beta <= 2.35)
		tmp = Float64(Float64(alpha - -1.0) / fma(fma(7.0, alpha, 16.0), alpha, 12.0));
	else
		tmp = Float64(Float64(Float64(alpha - -1.0) / beta) / Float64(Float64(alpha + beta) - -3.0));
	end
	return tmp
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := If[LessEqual[beta, 2.35], N[(N[(alpha - -1.0), $MachinePrecision] / N[(N[(7.0 * alpha + 16.0), $MachinePrecision] * alpha + 12.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(alpha - -1.0), $MachinePrecision] / beta), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 2.35000000000000009

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around 0

      \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 48.2

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

   4. Applied rewrites 48.2%

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

   5. Taylor expanded in alpha around 0

      \[\leadsto \frac{1 + \alpha}{12 + \color{blue}{\alpha \cdot \left(16 + 7 \cdot \alpha\right)}} \]
   6. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto \frac{1 + \alpha}{12 + \alpha \cdot \color{blue}{\left(16 + 7 \cdot \alpha\right)}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1 + \alpha}{12 + \alpha \cdot \left(16 + \color{blue}{7 \cdot \alpha}\right)} \]

      3. lower-+.f64 N/A

         \[\leadsto \frac{1 + \alpha}{12 + \alpha \cdot \left(16 + 7 \cdot \color{blue}{\alpha}\right)} \]

      4. lower-*.f64 46.7

         \[\leadsto \frac{1 + \alpha}{12 + \alpha \cdot \left(16 + 7 \cdot \alpha\right)} \]

   7. Applied rewrites 46.7%

      \[\leadsto \frac{1 + \alpha}{12 + \color{blue}{\alpha \cdot \left(16 + 7 \cdot \alpha\right)}} \]
   8. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{1 + \alpha}{\color{blue}{12} + \alpha \cdot \left(16 + 7 \cdot \alpha\right)} \]

      2. +-commutative N/A

         \[\leadsto \frac{\alpha + 1}{\color{blue}{12} + \alpha \cdot \left(16 + 7 \cdot \alpha\right)} \]

      3. metadata-eval N/A

         \[\leadsto \frac{\alpha + \left(\mathsf{neg}\left(-1\right)\right)}{12 + \alpha \cdot \left(16 + 7 \cdot \alpha\right)} \]

      4. sub-flip N/A

         \[\leadsto \frac{\alpha - -1}{\color{blue}{12} + \alpha \cdot \left(16 + 7 \cdot \alpha\right)} \]

      5. lift--.f64 46.7

         \[\leadsto \frac{\alpha - -1}{\color{blue}{12} + \alpha \cdot \left(16 + 7 \cdot \alpha\right)} \]

      6. lift-+.f64 N/A

         \[\leadsto \frac{\alpha - -1}{12 + \alpha \cdot \color{blue}{\left(16 + 7 \cdot \alpha\right)}} \]

      7. +-commutative N/A

         \[\leadsto \frac{\alpha - -1}{\alpha \cdot \left(16 + 7 \cdot \alpha\right) + 12} \]

      8. lift-*.f64 N/A

         \[\leadsto \frac{\alpha - -1}{\alpha \cdot \left(16 + 7 \cdot \alpha\right) + 12} \]

      9. *-commutative N/A

         \[\leadsto \frac{\alpha - -1}{\left(16 + 7 \cdot \alpha\right) \cdot \alpha + 12} \]

      10. lower-fma.f64 46.7

          \[\leadsto \frac{\alpha - -1}{\mathsf{fma}\left(16 + 7 \cdot \alpha, \alpha, 12\right)} \]

      11. lift-+.f64 N/A

          \[\leadsto \frac{\alpha - -1}{\mathsf{fma}\left(16 + 7 \cdot \alpha, \alpha, 12\right)} \]

      12. +-commutative N/A

          \[\leadsto \frac{\alpha - -1}{\mathsf{fma}\left(7 \cdot \alpha + 16, \alpha, 12\right)} \]

      13. lift-*.f64 N/A

          \[\leadsto \frac{\alpha - -1}{\mathsf{fma}\left(7 \cdot \alpha + 16, \alpha, 12\right)} \]

      14. lower-fma.f64 46.7

          \[\leadsto \frac{\alpha - -1}{\mathsf{fma}\left(\mathsf{fma}\left(7, \alpha, 16\right), \alpha, 12\right)} \]

   9. Applied rewrites 46.7%

      \[\leadsto \frac{\alpha - -1}{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(7, \alpha, 16\right), \alpha, 12\right)}} \]

   if 2.35000000000000009 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 56.1

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

   4. Applied rewrites 56.1%

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

      1. metadata-eval 56.1

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

      2. metadata-eval 56.1

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

   6. Applied rewrites 56.1%

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

Alternative 13: 97.1% accurate, 2.6× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} \mathbf{if}\;\beta \leq 1.9:\\ \;\;\;\;0.08333333333333333 + \alpha \cdot \left(-0.011574074074074073 \cdot \alpha - 0.027777777777777776\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\alpha - -1}{\beta}}{\left(\alpha + \beta\right) - -3}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (if (<= beta 1.9)
   (+
    0.08333333333333333
    (* alpha (- (* -0.011574074074074073 alpha) 0.027777777777777776)))
   (/ (/ (- alpha -1.0) beta) (- (+ alpha beta) -3.0))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double tmp;
	if (beta <= 1.9) {
		tmp = 0.08333333333333333 + (alpha * ((-0.011574074074074073 * alpha) - 0.027777777777777776));
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Fortran

NOTE: alpha and beta should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(alpha, beta)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8) :: tmp
    if (beta <= 1.9d0) then
        tmp = 0.08333333333333333d0 + (alpha * (((-0.011574074074074073d0) * alpha) - 0.027777777777777776d0))
    else
        tmp = ((alpha - (-1.0d0)) / beta) / ((alpha + beta) - (-3.0d0))
    end if
    code = tmp
end function

Java

assert alpha < beta;
public static double code(double alpha, double beta) {
	double tmp;
	if (beta <= 1.9) {
		tmp = 0.08333333333333333 + (alpha * ((-0.011574074074074073 * alpha) - 0.027777777777777776));
	} else {
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	}
	return tmp;
}

Python

[alpha, beta] = sort([alpha, beta])
def code(alpha, beta):
	tmp = 0
	if beta <= 1.9:
		tmp = 0.08333333333333333 + (alpha * ((-0.011574074074074073 * alpha) - 0.027777777777777776))
	else:
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0)
	return tmp

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	tmp = 0.0
	if (beta <= 1.9)
		tmp = Float64(0.08333333333333333 + Float64(alpha * Float64(Float64(-0.011574074074074073 * alpha) - 0.027777777777777776)));
	else
		tmp = Float64(Float64(Float64(alpha - -1.0) / beta) / Float64(Float64(alpha + beta) - -3.0));
	end
	return tmp
end

MATLAB

alpha, beta = num2cell(sort([alpha, beta])){:}
function tmp_2 = code(alpha, beta)
	tmp = 0.0;
	if (beta <= 1.9)
		tmp = 0.08333333333333333 + (alpha * ((-0.011574074074074073 * alpha) - 0.027777777777777776));
	else
		tmp = ((alpha - -1.0) / beta) / ((alpha + beta) - -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := If[LessEqual[beta, 1.9], N[(0.08333333333333333 + N[(alpha * N[(N[(-0.011574074074074073 * alpha), $MachinePrecision] - 0.027777777777777776), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(alpha - -1.0), $MachinePrecision] / beta), $MachinePrecision] / N[(N[(alpha + beta), $MachinePrecision] - -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 1.8999999999999999

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around 0

      \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 48.2

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

   4. Applied rewrites 48.2%

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

   5. Taylor expanded in alpha around 0

      \[\leadsto \frac{1}{12} + \color{blue}{\alpha \cdot \left(\frac{-5}{432} \cdot \alpha - \frac{1}{36}\right)} \]
   6. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto \frac{1}{12} + \alpha \cdot \color{blue}{\left(\frac{-5}{432} \cdot \alpha - \frac{1}{36}\right)} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{12} + \alpha \cdot \left(\frac{-5}{432} \cdot \alpha - \color{blue}{\frac{1}{36}}\right) \]

      3. lower--.f64 N/A

         \[\leadsto \frac{1}{12} + \alpha \cdot \left(\frac{-5}{432} \cdot \alpha - \frac{1}{36}\right) \]

      4. lower-*.f64 45.2

         \[\leadsto 0.08333333333333333 + \alpha \cdot \left(-0.011574074074074073 \cdot \alpha - 0.027777777777777776\right) \]

   7. Applied rewrites 45.2%

      \[\leadsto 0.08333333333333333 + \color{blue}{\alpha \cdot \left(-0.011574074074074073 \cdot \alpha - 0.027777777777777776\right)} \]

   if 1.8999999999999999 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 56.1

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

   4. Applied rewrites 56.1%

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

      1. metadata-eval 56.1

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

      2. metadata-eval 56.1

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

   6. Applied rewrites 56.1%

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

Alternative 14: 97.1% accurate, 3.0× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \begin{array}{l} \mathbf{if}\;\beta \leq 1.92:\\ \;\;\;\;0.08333333333333333 + \alpha \cdot \left(-0.011574074074074073 \cdot \alpha - 0.027777777777777776\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1 + \alpha}{\beta}}{3 + \beta}\\ \end{array} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (if (<= beta 1.92)
   (+
    0.08333333333333333
    (* alpha (- (* -0.011574074074074073 alpha) 0.027777777777777776)))
   (/ (/ (+ 1.0 alpha) beta) (+ 3.0 beta))))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	double tmp;
	if (beta <= 1.92) {
		tmp = 0.08333333333333333 + (alpha * ((-0.011574074074074073 * alpha) - 0.027777777777777776));
	} else {
		tmp = ((1.0 + alpha) / beta) / (3.0 + beta);
	}
	return tmp;
}

Fortran

NOTE: alpha and beta should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(alpha, beta)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8) :: tmp
    if (beta <= 1.92d0) then
        tmp = 0.08333333333333333d0 + (alpha * (((-0.011574074074074073d0) * alpha) - 0.027777777777777776d0))
    else
        tmp = ((1.0d0 + alpha) / beta) / (3.0d0 + beta)
    end if
    code = tmp
end function

Java

assert alpha < beta;
public static double code(double alpha, double beta) {
	double tmp;
	if (beta <= 1.92) {
		tmp = 0.08333333333333333 + (alpha * ((-0.011574074074074073 * alpha) - 0.027777777777777776));
	} else {
		tmp = ((1.0 + alpha) / beta) / (3.0 + beta);
	}
	return tmp;
}

Python

[alpha, beta] = sort([alpha, beta])
def code(alpha, beta):
	tmp = 0
	if beta <= 1.92:
		tmp = 0.08333333333333333 + (alpha * ((-0.011574074074074073 * alpha) - 0.027777777777777776))
	else:
		tmp = ((1.0 + alpha) / beta) / (3.0 + beta)
	return tmp

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	tmp = 0.0
	if (beta <= 1.92)
		tmp = Float64(0.08333333333333333 + Float64(alpha * Float64(Float64(-0.011574074074074073 * alpha) - 0.027777777777777776)));
	else
		tmp = Float64(Float64(Float64(1.0 + alpha) / beta) / Float64(3.0 + beta));
	end
	return tmp
end

MATLAB

alpha, beta = num2cell(sort([alpha, beta])){:}
function tmp_2 = code(alpha, beta)
	tmp = 0.0;
	if (beta <= 1.92)
		tmp = 0.08333333333333333 + (alpha * ((-0.011574074074074073 * alpha) - 0.027777777777777776));
	else
		tmp = ((1.0 + alpha) / beta) / (3.0 + beta);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := If[LessEqual[beta, 1.92], N[(0.08333333333333333 + N[(alpha * N[(N[(-0.011574074074074073 * alpha), $MachinePrecision] - 0.027777777777777776), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(1.0 + alpha), $MachinePrecision] / beta), $MachinePrecision] / N[(3.0 + beta), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 1.9199999999999999

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around 0

      \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 48.2

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

   4. Applied rewrites 48.2%

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

   5. Taylor expanded in alpha around 0

      \[\leadsto \frac{1}{12} + \color{blue}{\alpha \cdot \left(\frac{-5}{432} \cdot \alpha - \frac{1}{36}\right)} \]
   6. Step-by-step derivation

      1. lower-+.f64 N/A

         \[\leadsto \frac{1}{12} + \alpha \cdot \color{blue}{\left(\frac{-5}{432} \cdot \alpha - \frac{1}{36}\right)} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{12} + \alpha \cdot \left(\frac{-5}{432} \cdot \alpha - \color{blue}{\frac{1}{36}}\right) \]

      3. lower--.f64 N/A

         \[\leadsto \frac{1}{12} + \alpha \cdot \left(\frac{-5}{432} \cdot \alpha - \frac{1}{36}\right) \]

      4. lower-*.f64 45.2

         \[\leadsto 0.08333333333333333 + \alpha \cdot \left(-0.011574074074074073 \cdot \alpha - 0.027777777777777776\right) \]

   7. Applied rewrites 45.2%

      \[\leadsto 0.08333333333333333 + \color{blue}{\alpha \cdot \left(-0.011574074074074073 \cdot \alpha - 0.027777777777777776\right)} \]

   if 1.9199999999999999 < beta

   1. Initial program 94.1%

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

   2. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 56.1

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

   4. Applied rewrites 56.1%

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

   5. Taylor expanded in alpha around 0

      \[\leadsto \frac{\frac{1 + \alpha}{\beta}}{\color{blue}{3 + \beta}} \]
   6. Step-by-step derivation

      1. lower-+.f64 56.0

         \[\leadsto \frac{\frac{1 + \alpha}{\beta}}{3 + \color{blue}{\beta}} \]

   7. Applied rewrites 56.0%

      \[\leadsto \frac{\frac{1 + \alpha}{\beta}}{\color{blue}{3 + \beta}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 15: 45.4% accurate, 7.2× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \frac{0.25}{\alpha - -3} \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta) :precision binary64 (/ 0.25 (- alpha -3.0)))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	return 0.25 / (alpha - -3.0);
}

Fortran

NOTE: alpha and beta should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(alpha, beta)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    code = 0.25d0 / (alpha - (-3.0d0))
end function

Java

assert alpha < beta;
public static double code(double alpha, double beta) {
	return 0.25 / (alpha - -3.0);
}

Python

[alpha, beta] = sort([alpha, beta])
def code(alpha, beta):
	return 0.25 / (alpha - -3.0)

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	return Float64(0.25 / Float64(alpha - -3.0))
end

MATLAB

alpha, beta = num2cell(sort([alpha, beta])){:}
function tmp = code(alpha, beta)
	tmp = 0.25 / (alpha - -3.0);
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := N[(0.25 / N[(alpha - -3.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.1%

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

2. Taylor expanded in beta around 0

   \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
3. Step-by-step derivation

   1. lower-/.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-pow.f64 N/A

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

   5. lower-+.f64 N/A

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

   6. lower-+.f64 48.2

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

4. Applied rewrites 48.2%

   \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
5. Step-by-step derivation

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/r* N/A

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

   4. lower-/.f64 N/A

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

   5. lower-/.f64 47.1

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

   6. lift-+.f64 N/A

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

   7. +-commutative N/A

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

   8. add-flip N/A

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

   9. metadata-eval N/A

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

   10. lower--.f64 47.1

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

   11. lift-pow.f64 N/A

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

   12. unpow2 N/A

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

   13. lower-*.f64 47.1

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

   14. lift-+.f64 N/A

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

   15. +-commutative N/A

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

   16. add-flip-rev N/A

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

   17. metadata-eval N/A

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

   18. lower--.f64 47.1

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

   19. lift-+.f64 N/A

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

   20. +-commutative N/A

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

   21. add-flip-rev N/A

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

   22. metadata-eval N/A

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

   23. lower--.f64 47.1

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

   24. lift-+.f64 N/A

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

   25. +-commutative N/A

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

   26. add-flip N/A

       \[\leadsto \frac{\frac{\alpha - -1}{\left(\alpha - -2\right) \cdot \left(\alpha - -2\right)}}{\alpha - \color{blue}{\left(\mathsf{neg}\left(3\right)\right)}} \]

   27. metadata-eval N/A

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

   28. lower--.f64 47.1

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

6. Applied rewrites 47.1%

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

7. Taylor expanded in alpha around 0

   \[\leadsto \frac{\frac{1}{4}}{\color{blue}{\alpha} - -3} \]
8. Step-by-step derivation

9. Applied rewrites 45.4%

   \[\leadsto \frac{0.25}{\color{blue}{\alpha} - -3} \]
10. Add Preprocessing

Alternative 16: 45.0% accurate, 8.4× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ \mathsf{fma}\left(\alpha, -0.027777777777777776, 0.08333333333333333\right) \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta)
 :precision binary64
 (fma alpha -0.027777777777777776 0.08333333333333333))

C

assert(alpha < beta);
double code(double alpha, double beta) {
	return fma(alpha, -0.027777777777777776, 0.08333333333333333);
}

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	return fma(alpha, -0.027777777777777776, 0.08333333333333333)
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := N[(alpha * -0.027777777777777776 + 0.08333333333333333), $MachinePrecision]

Derivation

1. Initial program 94.1%

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

2. Taylor expanded in beta around 0

   \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
3. Step-by-step derivation

   1. lower-/.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-pow.f64 N/A

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

   5. lower-+.f64 N/A

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

   6. lower-+.f64 48.2

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

4. Applied rewrites 48.2%

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

5. Taylor expanded in alpha around 0

   \[\leadsto \frac{1}{12} + \color{blue}{\frac{-1}{36} \cdot \alpha} \]
6. Step-by-step derivation

   1. lower-+.f64 N/A

      \[\leadsto \frac{1}{12} + \frac{-1}{36} \cdot \color{blue}{\alpha} \]

   2. lower-*.f64 45.0

      \[\leadsto 0.08333333333333333 + -0.027777777777777776 \cdot \alpha \]

7. Applied rewrites 45.0%

   \[\leadsto 0.08333333333333333 + \color{blue}{-0.027777777777777776 \cdot \alpha} \]
8. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \frac{1}{12} + \frac{-1}{36} \cdot \color{blue}{\alpha} \]

   2. +-commutative N/A

      \[\leadsto \frac{-1}{36} \cdot \alpha + \frac{1}{12} \]

   3. lift-*.f64 N/A

      \[\leadsto \frac{-1}{36} \cdot \alpha + \frac{1}{12} \]

   4. *-commutative N/A

      \[\leadsto \alpha \cdot \frac{-1}{36} + \frac{1}{12} \]

   5. lower-fma.f64 45.0

      \[\leadsto \mathsf{fma}\left(\alpha, -0.027777777777777776, 0.08333333333333333\right) \]

9. Applied rewrites 45.0%

   \[\leadsto \mathsf{fma}\left(\alpha, -0.027777777777777776, 0.08333333333333333\right) \]
10. Add Preprocessing

Alternative 17: 44.7% accurate, 50.2× speedup

Math

\[\begin{array}{l} [alpha, beta] = \mathsf{sort}([alpha, beta])\\ \\ 0.08333333333333333 \end{array} \]

FPCore

NOTE: alpha and beta should be sorted in increasing order before calling this function.
(FPCore (alpha beta) :precision binary64 0.08333333333333333)

C

assert(alpha < beta);
double code(double alpha, double beta) {
	return 0.08333333333333333;
}

Fortran

NOTE: alpha and beta should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(alpha, beta)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    code = 0.08333333333333333d0
end function

Java

assert alpha < beta;
public static double code(double alpha, double beta) {
	return 0.08333333333333333;
}

Python

[alpha, beta] = sort([alpha, beta])
def code(alpha, beta):
	return 0.08333333333333333

Julia

alpha, beta = sort([alpha, beta])
function code(alpha, beta)
	return 0.08333333333333333
end

MATLAB

alpha, beta = num2cell(sort([alpha, beta])){:}
function tmp = code(alpha, beta)
	tmp = 0.08333333333333333;
end

Wolfram

NOTE: alpha and beta should be sorted in increasing order before calling this function.
code[alpha_, beta_] := 0.08333333333333333

Derivation

1. Initial program 94.1%

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

2. Taylor expanded in beta around 0

   \[\leadsto \color{blue}{\frac{1 + \alpha}{{\left(2 + \alpha\right)}^{2} \cdot \left(3 + \alpha\right)}} \]
3. Step-by-step derivation

   1. lower-/.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-pow.f64 N/A

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

   5. lower-+.f64 N/A

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

   6. lower-+.f64 48.2

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

4. Applied rewrites 48.2%

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

5. Taylor expanded in alpha around 0

   \[\leadsto \frac{1}{12} \]
6. Step-by-step derivation

7. Applied rewrites 44.7%

   \[\leadsto 0.08333333333333333 \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025141 
(FPCore (alpha beta)
  :name "Octave 3.8, jcobi/3"
  :precision binary64
  :pre (and (> alpha -1.0) (> beta -1.0))
  (/ (/ (/ (+ (+ (+ alpha beta) (* beta alpha)) 1.0) (+ (+ alpha beta) (* 2.0 1.0))) (+ (+ alpha beta) (* 2.0 1.0))) (+ (+ (+ alpha beta) (* 2.0 1.0)) 1.0)))