Octave 3.8, jcobi/4

Percentage Accurate: 15.7% → 85.0%

Time: 5.3s

Alternatives: 5

Speedup: 75.0×

Specification

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

Math

\[\begin{array}{l} t_0 := i \cdot \left(\left(\alpha + \beta\right) + i\right)\\ t_1 := \left(\alpha + \beta\right) + 2 \cdot i\\ t_2 := t\_1 \cdot t\_1\\ \frac{\frac{t\_0 \cdot \left(\beta \cdot \alpha + t\_0\right)}{t\_2}}{t\_2 - 1} \end{array} \]

FPCore

(FPCore (alpha beta i)
  :precision binary64
  (let* ((t_0 (* i (+ (+ alpha beta) i)))
       (t_1 (+ (+ alpha beta) (* 2.0 i)))
       (t_2 (* t_1 t_1)))
  (/ (/ (* t_0 (+ (* beta alpha) t_0)) t_2) (- t_2 1.0))))

C

double code(double alpha, double beta, double i) {
	double t_0 = i * ((alpha + beta) + i);
	double t_1 = (alpha + beta) + (2.0 * i);
	double t_2 = t_1 * t_1;
	return ((t_0 * ((beta * alpha) + t_0)) / t_2) / (t_2 - 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, i)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8), intent (in) :: i
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    t_0 = i * ((alpha + beta) + i)
    t_1 = (alpha + beta) + (2.0d0 * i)
    t_2 = t_1 * t_1
    code = ((t_0 * ((beta * alpha) + t_0)) / t_2) / (t_2 - 1.0d0)
end function

Java

public static double code(double alpha, double beta, double i) {
	double t_0 = i * ((alpha + beta) + i);
	double t_1 = (alpha + beta) + (2.0 * i);
	double t_2 = t_1 * t_1;
	return ((t_0 * ((beta * alpha) + t_0)) / t_2) / (t_2 - 1.0);
}

Python

def code(alpha, beta, i):
	t_0 = i * ((alpha + beta) + i)
	t_1 = (alpha + beta) + (2.0 * i)
	t_2 = t_1 * t_1
	return ((t_0 * ((beta * alpha) + t_0)) / t_2) / (t_2 - 1.0)

Julia

function code(alpha, beta, i)
	t_0 = Float64(i * Float64(Float64(alpha + beta) + i))
	t_1 = Float64(Float64(alpha + beta) + Float64(2.0 * i))
	t_2 = Float64(t_1 * t_1)
	return Float64(Float64(Float64(t_0 * Float64(Float64(beta * alpha) + t_0)) / t_2) / Float64(t_2 - 1.0))
end

MATLAB

function tmp = code(alpha, beta, i)
	t_0 = i * ((alpha + beta) + i);
	t_1 = (alpha + beta) + (2.0 * i);
	t_2 = t_1 * t_1;
	tmp = ((t_0 * ((beta * alpha) + t_0)) / t_2) / (t_2 - 1.0);
end

Wolfram

code[alpha_, beta_, i_] := Block[{t$95$0 = N[(i * N[(N[(alpha + beta), $MachinePrecision] + i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * t$95$1), $MachinePrecision]}, N[(N[(N[(t$95$0 * N[(N[(beta * alpha), $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision] / t$95$2), $MachinePrecision] / N[(t$95$2 - 1.0), $MachinePrecision]), $MachinePrecision]]]]

Initial Program: 15.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} t_0 := i \cdot \left(\left(\alpha + \beta\right) + i\right)\\ t_1 := \left(\alpha + \beta\right) + 2 \cdot i\\ t_2 := t\_1 \cdot t\_1\\ \frac{\frac{t\_0 \cdot \left(\beta \cdot \alpha + t\_0\right)}{t\_2}}{t\_2 - 1} \end{array} \]

FPCore

(FPCore (alpha beta i)
  :precision binary64
  (let* ((t_0 (* i (+ (+ alpha beta) i)))
       (t_1 (+ (+ alpha beta) (* 2.0 i)))
       (t_2 (* t_1 t_1)))
  (/ (/ (* t_0 (+ (* beta alpha) t_0)) t_2) (- t_2 1.0))))

C

double code(double alpha, double beta, double i) {
	double t_0 = i * ((alpha + beta) + i);
	double t_1 = (alpha + beta) + (2.0 * i);
	double t_2 = t_1 * t_1;
	return ((t_0 * ((beta * alpha) + t_0)) / t_2) / (t_2 - 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, i)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8), intent (in) :: i
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    t_0 = i * ((alpha + beta) + i)
    t_1 = (alpha + beta) + (2.0d0 * i)
    t_2 = t_1 * t_1
    code = ((t_0 * ((beta * alpha) + t_0)) / t_2) / (t_2 - 1.0d0)
end function

Java

public static double code(double alpha, double beta, double i) {
	double t_0 = i * ((alpha + beta) + i);
	double t_1 = (alpha + beta) + (2.0 * i);
	double t_2 = t_1 * t_1;
	return ((t_0 * ((beta * alpha) + t_0)) / t_2) / (t_2 - 1.0);
}

Python

def code(alpha, beta, i):
	t_0 = i * ((alpha + beta) + i)
	t_1 = (alpha + beta) + (2.0 * i)
	t_2 = t_1 * t_1
	return ((t_0 * ((beta * alpha) + t_0)) / t_2) / (t_2 - 1.0)

Julia

function code(alpha, beta, i)
	t_0 = Float64(i * Float64(Float64(alpha + beta) + i))
	t_1 = Float64(Float64(alpha + beta) + Float64(2.0 * i))
	t_2 = Float64(t_1 * t_1)
	return Float64(Float64(Float64(t_0 * Float64(Float64(beta * alpha) + t_0)) / t_2) / Float64(t_2 - 1.0))
end

MATLAB

function tmp = code(alpha, beta, i)
	t_0 = i * ((alpha + beta) + i);
	t_1 = (alpha + beta) + (2.0 * i);
	t_2 = t_1 * t_1;
	tmp = ((t_0 * ((beta * alpha) + t_0)) / t_2) / (t_2 - 1.0);
end

Wolfram

code[alpha_, beta_, i_] := Block[{t$95$0 = N[(i * N[(N[(alpha + beta), $MachinePrecision] + i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * t$95$1), $MachinePrecision]}, N[(N[(N[(t$95$0 * N[(N[(beta * alpha), $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision] / t$95$2), $MachinePrecision] / N[(t$95$2 - 1.0), $MachinePrecision]), $MachinePrecision]]]]

Alternative 1: 85.0% accurate, 1.7× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\mathsf{max}\left(\alpha, \beta\right) \leq 1.06 \cdot 10^{+188}:\\ \;\;\;\;\frac{\mathsf{fma}\left(0.0625, i, \mathsf{max}\left(\alpha, \beta\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\mathsf{max}\left(\alpha, \beta\right)}{i}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(\mathsf{min}\left(\alpha, \beta\right) + i\right) \cdot \frac{i}{\mathsf{max}\left(\alpha, \beta\right)}}{\mathsf{fma}\left(i, 2, \mathsf{max}\left(\alpha, \beta\right) + \mathsf{min}\left(\alpha, \beta\right)\right) - 1}\\ \end{array} \]

FPCore

(FPCore (alpha beta i)
  :precision binary64
  (if (<= (fmax alpha beta) 1.06e+188)
  (-
   (/ (fma 0.0625 i (* (fmax alpha beta) 0.125)) i)
   (* 0.125 (/ (fmax alpha beta) i)))
  (/
   (* (+ (fmin alpha beta) i) (/ i (fmax alpha beta)))
   (- (fma i 2.0 (+ (fmax alpha beta) (fmin alpha beta))) 1.0))))

C

double code(double alpha, double beta, double i) {
	double tmp;
	if (fmax(alpha, beta) <= 1.06e+188) {
		tmp = (fma(0.0625, i, (fmax(alpha, beta) * 0.125)) / i) - (0.125 * (fmax(alpha, beta) / i));
	} else {
		tmp = ((fmin(alpha, beta) + i) * (i / fmax(alpha, beta))) / (fma(i, 2.0, (fmax(alpha, beta) + fmin(alpha, beta))) - 1.0);
	}
	return tmp;
}

Julia

function code(alpha, beta, i)
	tmp = 0.0
	if (fmax(alpha, beta) <= 1.06e+188)
		tmp = Float64(Float64(fma(0.0625, i, Float64(fmax(alpha, beta) * 0.125)) / i) - Float64(0.125 * Float64(fmax(alpha, beta) / i)));
	else
		tmp = Float64(Float64(Float64(fmin(alpha, beta) + i) * Float64(i / fmax(alpha, beta))) / Float64(fma(i, 2.0, Float64(fmax(alpha, beta) + fmin(alpha, beta))) - 1.0));
	end
	return tmp
end

Wolfram

code[alpha_, beta_, i_] := If[LessEqual[N[Max[alpha, beta], $MachinePrecision], 1.06e+188], N[(N[(N[(0.0625 * i + N[(N[Max[alpha, beta], $MachinePrecision] * 0.125), $MachinePrecision]), $MachinePrecision] / i), $MachinePrecision] - N[(0.125 * N[(N[Max[alpha, beta], $MachinePrecision] / i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[Min[alpha, beta], $MachinePrecision] + i), $MachinePrecision] * N[(i / N[Max[alpha, beta], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(i * 2.0 + N[(N[Max[alpha, beta], $MachinePrecision] + N[Min[alpha, beta], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 1.0600000000000001e188

   1. Initial program 15.7%

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

   2. Taylor expanded in i around inf

      \[\leadsto \color{blue}{\left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i}} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \color{blue}{\frac{1}{8} \cdot \frac{\alpha + \beta}{i}} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      4. lower-/.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      5. lower-fma.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \color{blue}{\frac{\alpha + \beta}{i}} \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{\color{blue}{i}} \]

      9. lower-+.f64 77.5%

         \[\leadsto \left(0.0625 + 0.0625 \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - 0.125 \cdot \frac{\alpha + \beta}{i} \]

   4. Applied rewrites 77.5%

      \[\leadsto \color{blue}{\left(0.0625 + 0.0625 \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - 0.125 \cdot \frac{\alpha + \beta}{i}} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      3. lift-/.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      4. associate-*r/ N/A

         \[\leadsto \left(\frac{1}{16} + \frac{\frac{1}{16} \cdot \mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      5. add-to-fraction N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i} - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      6. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i} - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      7. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \alpha + 2 \cdot \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      8. lift-*.f64 N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \alpha + 2 \cdot \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      9. distribute-lft-out N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \left(\alpha + \beta\right)\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      10. lift-+.f64 N/A

          \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \left(\alpha + \beta\right)\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      11. associate-*r* N/A

          \[\leadsto \frac{\frac{1}{16} \cdot i + \left(\frac{1}{16} \cdot 2\right) \cdot \left(\alpha + \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      12. metadata-eval N/A

          \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{8} \cdot \left(\alpha + \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      13. lower-fma.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \frac{1}{8} \cdot \left(\alpha + \beta\right)\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      14. *-commutative N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \left(\alpha + \beta\right) \cdot \frac{1}{8}\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      15. lower-*.f64 77.5%

          \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\alpha + \beta\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\alpha + \beta}{i} \]

      16. lift-+.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \left(\alpha + \beta\right) \cdot \frac{1}{8}\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      17. +-commutative N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \left(\beta + \alpha\right) \cdot \frac{1}{8}\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      18. lift-+.f64 77.5%

          \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\alpha + \beta}{i} \]

   6. Applied rewrites 77.5%

      \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - \color{blue}{0.125} \cdot \frac{\alpha + \beta}{i} \]

   7. Taylor expanded in alpha around 0

      \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{\color{blue}{i}} \]
   8. Step-by-step derivation

      1. lower-/.f64 73.8%

         \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{i} \]

   9. Applied rewrites 73.8%

      \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{\color{blue}{i}} \]

   10. Taylor expanded in alpha around 0

       \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \beta \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{i} \]
   11. Step-by-step derivation

   12. Applied rewrites 74.7%

       \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \beta \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{i} \]

   if 1.0600000000000001e188 < beta

   1. Initial program 15.7%

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

   2. Taylor expanded in alpha around -inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-*.f64 13.4%

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

   4. Applied rewrites 13.4%

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

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. difference-of-sqr-1 N/A

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

      5. associate-/r* N/A

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

   6. Applied rewrites 17.3%

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

   7. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 12.2%

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

   9. Applied rewrites 12.2%

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

       1. lift-/.f64 N/A

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

       2. lift-*.f64 N/A

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

       3. *-commutative N/A

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

       4. associate-/l* N/A

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

       5. lower-*.f64 N/A

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

       6. lower-/.f64 15.9%

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

   11. Applied rewrites 15.9%

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

Alternative 2: 85.0% accurate, 1.7× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\mathsf{max}\left(\alpha, \beta\right) \leq 1.06 \cdot 10^{+188}:\\ \;\;\;\;\frac{\mathsf{fma}\left(0.0625, i, \mathsf{max}\left(\alpha, \beta\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\mathsf{max}\left(\alpha, \beta\right)}{i}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\mathsf{min}\left(\alpha, \beta\right) + i}{\mathsf{max}\left(\alpha, \beta\right)} \cdot i}{\mathsf{fma}\left(i, 2, \mathsf{max}\left(\alpha, \beta\right) + \mathsf{min}\left(\alpha, \beta\right)\right) - 1}\\ \end{array} \]

FPCore

(FPCore (alpha beta i)
  :precision binary64
  (if (<= (fmax alpha beta) 1.06e+188)
  (-
   (/ (fma 0.0625 i (* (fmax alpha beta) 0.125)) i)
   (* 0.125 (/ (fmax alpha beta) i)))
  (/
   (* (/ (+ (fmin alpha beta) i) (fmax alpha beta)) i)
   (- (fma i 2.0 (+ (fmax alpha beta) (fmin alpha beta))) 1.0))))

C

double code(double alpha, double beta, double i) {
	double tmp;
	if (fmax(alpha, beta) <= 1.06e+188) {
		tmp = (fma(0.0625, i, (fmax(alpha, beta) * 0.125)) / i) - (0.125 * (fmax(alpha, beta) / i));
	} else {
		tmp = (((fmin(alpha, beta) + i) / fmax(alpha, beta)) * i) / (fma(i, 2.0, (fmax(alpha, beta) + fmin(alpha, beta))) - 1.0);
	}
	return tmp;
}

Julia

function code(alpha, beta, i)
	tmp = 0.0
	if (fmax(alpha, beta) <= 1.06e+188)
		tmp = Float64(Float64(fma(0.0625, i, Float64(fmax(alpha, beta) * 0.125)) / i) - Float64(0.125 * Float64(fmax(alpha, beta) / i)));
	else
		tmp = Float64(Float64(Float64(Float64(fmin(alpha, beta) + i) / fmax(alpha, beta)) * i) / Float64(fma(i, 2.0, Float64(fmax(alpha, beta) + fmin(alpha, beta))) - 1.0));
	end
	return tmp
end

Wolfram

code[alpha_, beta_, i_] := If[LessEqual[N[Max[alpha, beta], $MachinePrecision], 1.06e+188], N[(N[(N[(0.0625 * i + N[(N[Max[alpha, beta], $MachinePrecision] * 0.125), $MachinePrecision]), $MachinePrecision] / i), $MachinePrecision] - N[(0.125 * N[(N[Max[alpha, beta], $MachinePrecision] / i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(N[Min[alpha, beta], $MachinePrecision] + i), $MachinePrecision] / N[Max[alpha, beta], $MachinePrecision]), $MachinePrecision] * i), $MachinePrecision] / N[(N[(i * 2.0 + N[(N[Max[alpha, beta], $MachinePrecision] + N[Min[alpha, beta], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 1.0600000000000001e188

   1. Initial program 15.7%

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

   2. Taylor expanded in i around inf

      \[\leadsto \color{blue}{\left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i}} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \color{blue}{\frac{1}{8} \cdot \frac{\alpha + \beta}{i}} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      4. lower-/.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      5. lower-fma.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \color{blue}{\frac{\alpha + \beta}{i}} \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{\color{blue}{i}} \]

      9. lower-+.f64 77.5%

         \[\leadsto \left(0.0625 + 0.0625 \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - 0.125 \cdot \frac{\alpha + \beta}{i} \]

   4. Applied rewrites 77.5%

      \[\leadsto \color{blue}{\left(0.0625 + 0.0625 \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - 0.125 \cdot \frac{\alpha + \beta}{i}} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      3. lift-/.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      4. associate-*r/ N/A

         \[\leadsto \left(\frac{1}{16} + \frac{\frac{1}{16} \cdot \mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      5. add-to-fraction N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i} - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      6. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i} - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      7. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \alpha + 2 \cdot \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      8. lift-*.f64 N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \alpha + 2 \cdot \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      9. distribute-lft-out N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \left(\alpha + \beta\right)\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      10. lift-+.f64 N/A

          \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \left(\alpha + \beta\right)\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      11. associate-*r* N/A

          \[\leadsto \frac{\frac{1}{16} \cdot i + \left(\frac{1}{16} \cdot 2\right) \cdot \left(\alpha + \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      12. metadata-eval N/A

          \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{8} \cdot \left(\alpha + \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      13. lower-fma.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \frac{1}{8} \cdot \left(\alpha + \beta\right)\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      14. *-commutative N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \left(\alpha + \beta\right) \cdot \frac{1}{8}\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      15. lower-*.f64 77.5%

          \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\alpha + \beta\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\alpha + \beta}{i} \]

      16. lift-+.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \left(\alpha + \beta\right) \cdot \frac{1}{8}\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      17. +-commutative N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \left(\beta + \alpha\right) \cdot \frac{1}{8}\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      18. lift-+.f64 77.5%

          \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\alpha + \beta}{i} \]

   6. Applied rewrites 77.5%

      \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - \color{blue}{0.125} \cdot \frac{\alpha + \beta}{i} \]

   7. Taylor expanded in alpha around 0

      \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{\color{blue}{i}} \]
   8. Step-by-step derivation

      1. lower-/.f64 73.8%

         \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{i} \]

   9. Applied rewrites 73.8%

      \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{\color{blue}{i}} \]

   10. Taylor expanded in alpha around 0

       \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \beta \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{i} \]
   11. Step-by-step derivation

   12. Applied rewrites 74.7%

       \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \beta \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{i} \]

   if 1.0600000000000001e188 < beta

   1. Initial program 15.7%

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

   2. Taylor expanded in alpha around -inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-*.f64 13.4%

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

   4. Applied rewrites 13.4%

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

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. difference-of-sqr-1 N/A

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

      5. associate-/r* N/A

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

   6. Applied rewrites 17.3%

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

   7. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 12.2%

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

   9. Applied rewrites 12.2%

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

       1. lift-/.f64 N/A

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

       2. lift-*.f64 N/A

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

       3. associate-/l* N/A

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

       4. *-commutative N/A

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

       5. lower-*.f64 N/A

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

       6. lower-/.f64 15.9%

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

   11. Applied rewrites 15.9%

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

Alternative 3: 79.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} t_0 := \mathsf{min}\left(\alpha, \beta\right) + \mathsf{max}\left(\alpha, \beta\right)\\ t_1 := t\_0 + 2 \cdot i\\ t_2 := t\_1 \cdot t\_1\\ t_3 := i \cdot \left(t\_0 + i\right)\\ \mathbf{if}\;\frac{\frac{t\_3 \cdot \left(\mathsf{max}\left(\alpha, \beta\right) \cdot \mathsf{min}\left(\alpha, \beta\right) + t\_3\right)}{t\_2}}{t\_2 - 1} \leq 5 \cdot 10^{-24}:\\ \;\;\;\;\frac{\frac{i \cdot \left(\mathsf{min}\left(\alpha, \beta\right) + i\right)}{\mathsf{max}\left(\alpha, \beta\right)}}{\mathsf{fma}\left(i, 2, \mathsf{max}\left(\alpha, \beta\right)\right) - 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(0.0625, i, \mathsf{max}\left(\alpha, \beta\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\mathsf{max}\left(\alpha, \beta\right)}{i}\\ \end{array} \]

FPCore

(FPCore (alpha beta i)
  :precision binary64
  (let* ((t_0 (+ (fmin alpha beta) (fmax alpha beta)))
       (t_1 (+ t_0 (* 2.0 i)))
       (t_2 (* t_1 t_1))
       (t_3 (* i (+ t_0 i))))
  (if (<=
       (/
        (/
         (* t_3 (+ (* (fmax alpha beta) (fmin alpha beta)) t_3))
         t_2)
        (- t_2 1.0))
       5e-24)
    (/
     (/ (* i (+ (fmin alpha beta) i)) (fmax alpha beta))
     (- (fma i 2.0 (fmax alpha beta)) 1.0))
    (-
     (/ (fma 0.0625 i (* (fmax alpha beta) 0.125)) i)
     (* 0.125 (/ (fmax alpha beta) i))))))

C

double code(double alpha, double beta, double i) {
	double t_0 = fmin(alpha, beta) + fmax(alpha, beta);
	double t_1 = t_0 + (2.0 * i);
	double t_2 = t_1 * t_1;
	double t_3 = i * (t_0 + i);
	double tmp;
	if ((((t_3 * ((fmax(alpha, beta) * fmin(alpha, beta)) + t_3)) / t_2) / (t_2 - 1.0)) <= 5e-24) {
		tmp = ((i * (fmin(alpha, beta) + i)) / fmax(alpha, beta)) / (fma(i, 2.0, fmax(alpha, beta)) - 1.0);
	} else {
		tmp = (fma(0.0625, i, (fmax(alpha, beta) * 0.125)) / i) - (0.125 * (fmax(alpha, beta) / i));
	}
	return tmp;
}

Julia

function code(alpha, beta, i)
	t_0 = Float64(fmin(alpha, beta) + fmax(alpha, beta))
	t_1 = Float64(t_0 + Float64(2.0 * i))
	t_2 = Float64(t_1 * t_1)
	t_3 = Float64(i * Float64(t_0 + i))
	tmp = 0.0
	if (Float64(Float64(Float64(t_3 * Float64(Float64(fmax(alpha, beta) * fmin(alpha, beta)) + t_3)) / t_2) / Float64(t_2 - 1.0)) <= 5e-24)
		tmp = Float64(Float64(Float64(i * Float64(fmin(alpha, beta) + i)) / fmax(alpha, beta)) / Float64(fma(i, 2.0, fmax(alpha, beta)) - 1.0));
	else
		tmp = Float64(Float64(fma(0.0625, i, Float64(fmax(alpha, beta) * 0.125)) / i) - Float64(0.125 * Float64(fmax(alpha, beta) / i)));
	end
	return tmp
end

Wolfram

code[alpha_, beta_, i_] := Block[{t$95$0 = N[(N[Min[alpha, beta], $MachinePrecision] + N[Max[alpha, beta], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 + N[(2.0 * i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(i * N[(t$95$0 + i), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(N[(t$95$3 * N[(N[(N[Max[alpha, beta], $MachinePrecision] * N[Min[alpha, beta], $MachinePrecision]), $MachinePrecision] + t$95$3), $MachinePrecision]), $MachinePrecision] / t$95$2), $MachinePrecision] / N[(t$95$2 - 1.0), $MachinePrecision]), $MachinePrecision], 5e-24], N[(N[(N[(i * N[(N[Min[alpha, beta], $MachinePrecision] + i), $MachinePrecision]), $MachinePrecision] / N[Max[alpha, beta], $MachinePrecision]), $MachinePrecision] / N[(N[(i * 2.0 + N[Max[alpha, beta], $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(0.0625 * i + N[(N[Max[alpha, beta], $MachinePrecision] * 0.125), $MachinePrecision]), $MachinePrecision] / i), $MachinePrecision] - N[(0.125 * N[(N[Max[alpha, beta], $MachinePrecision] / i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (/.f64 (*.f64 (*.f64 i (+.f64 (+.f64 alpha beta) i)) (+.f64 (*.f64 beta alpha) (*.f64 i (+.f64 (+.f64 alpha beta) i)))) (*.f64 (+.f64 (+.f64 alpha beta) (*.f64 #s(literal 2 binary64) i)) (+.f64 (+.f64 alpha beta) (*.f64 #s(literal 2 binary64) i)))) (-.f64 (*.f64 (+.f64 (+.f64 alpha beta) (*.f64 #s(literal 2 binary64) i)) (+.f64 (+.f64 alpha beta) (*.f64 #s(literal 2 binary64) i))) #s(literal 1 binary64))) < 4.9999999999999998e-24

   1. Initial program 15.7%

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

   2. Taylor expanded in alpha around -inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-*.f64 13.4%

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

   4. Applied rewrites 13.4%

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

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. difference-of-sqr-1 N/A

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

      5. associate-/r* N/A

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

   6. Applied rewrites 17.3%

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

   7. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 12.2%

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

   9. Applied rewrites 12.2%

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

   10. Taylor expanded in alpha around 0

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

   12. Applied rewrites 12.2%

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

   if 4.9999999999999998e-24 < (/.f64 (/.f64 (*.f64 (*.f64 i (+.f64 (+.f64 alpha beta) i)) (+.f64 (*.f64 beta alpha) (*.f64 i (+.f64 (+.f64 alpha beta) i)))) (*.f64 (+.f64 (+.f64 alpha beta) (*.f64 #s(literal 2 binary64) i)) (+.f64 (+.f64 alpha beta) (*.f64 #s(literal 2 binary64) i)))) (-.f64 (*.f64 (+.f64 (+.f64 alpha beta) (*.f64 #s(literal 2 binary64) i)) (+.f64 (+.f64 alpha beta) (*.f64 #s(literal 2 binary64) i))) #s(literal 1 binary64)))

   1. Initial program 15.7%

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

   2. Taylor expanded in i around inf

      \[\leadsto \color{blue}{\left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i}} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \color{blue}{\frac{1}{8} \cdot \frac{\alpha + \beta}{i}} \]

      2. lower-+.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      4. lower-/.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{2 \cdot \alpha + 2 \cdot \beta}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      5. lower-fma.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \color{blue}{\frac{\alpha + \beta}{i}} \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{\color{blue}{i}} \]

      9. lower-+.f64 77.5%

         \[\leadsto \left(0.0625 + 0.0625 \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - 0.125 \cdot \frac{\alpha + \beta}{i} \]

   4. Applied rewrites 77.5%

      \[\leadsto \color{blue}{\left(0.0625 + 0.0625 \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - 0.125 \cdot \frac{\alpha + \beta}{i}} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      3. lift-/.f64 N/A

         \[\leadsto \left(\frac{1}{16} + \frac{1}{16} \cdot \frac{\mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      4. associate-*r/ N/A

         \[\leadsto \left(\frac{1}{16} + \frac{\frac{1}{16} \cdot \mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i}\right) - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      5. add-to-fraction N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i} - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      6. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \mathsf{fma}\left(2, \alpha, 2 \cdot \beta\right)}{i} - \color{blue}{\frac{1}{8}} \cdot \frac{\alpha + \beta}{i} \]

      7. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \alpha + 2 \cdot \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      8. lift-*.f64 N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \alpha + 2 \cdot \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      9. distribute-lft-out N/A

         \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \left(\alpha + \beta\right)\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      10. lift-+.f64 N/A

          \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{16} \cdot \left(2 \cdot \left(\alpha + \beta\right)\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      11. associate-*r* N/A

          \[\leadsto \frac{\frac{1}{16} \cdot i + \left(\frac{1}{16} \cdot 2\right) \cdot \left(\alpha + \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      12. metadata-eval N/A

          \[\leadsto \frac{\frac{1}{16} \cdot i + \frac{1}{8} \cdot \left(\alpha + \beta\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      13. lower-fma.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \frac{1}{8} \cdot \left(\alpha + \beta\right)\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      14. *-commutative N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \left(\alpha + \beta\right) \cdot \frac{1}{8}\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      15. lower-*.f64 77.5%

          \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\alpha + \beta\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\alpha + \beta}{i} \]

      16. lift-+.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \left(\alpha + \beta\right) \cdot \frac{1}{8}\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      17. +-commutative N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{16}, i, \left(\beta + \alpha\right) \cdot \frac{1}{8}\right)}{i} - \frac{1}{8} \cdot \frac{\alpha + \beta}{i} \]

      18. lift-+.f64 77.5%

          \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\alpha + \beta}{i} \]

   6. Applied rewrites 77.5%

      \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - \color{blue}{0.125} \cdot \frac{\alpha + \beta}{i} \]

   7. Taylor expanded in alpha around 0

      \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{\color{blue}{i}} \]
   8. Step-by-step derivation

      1. lower-/.f64 73.8%

         \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{i} \]

   9. Applied rewrites 73.8%

      \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \left(\beta + \alpha\right) \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{\color{blue}{i}} \]

   10. Taylor expanded in alpha around 0

       \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \beta \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{i} \]
   11. Step-by-step derivation

   12. Applied rewrites 74.7%

       \[\leadsto \frac{\mathsf{fma}\left(0.0625, i, \beta \cdot 0.125\right)}{i} - 0.125 \cdot \frac{\beta}{i} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 77.5% accurate, 2.0× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\mathsf{max}\left(\alpha, \beta\right) \leq 2.4 \cdot 10^{+198}:\\ \;\;\;\;0.0625\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{i \cdot \left(\mathsf{min}\left(\alpha, \beta\right) + i\right)}{\mathsf{max}\left(\alpha, \beta\right)}}{\left(\mathsf{min}\left(\alpha, \beta\right) + \mathsf{max}\left(\alpha, \beta\right)\right) - 1}\\ \end{array} \]

FPCore

(FPCore (alpha beta i)
  :precision binary64
  (if (<= (fmax alpha beta) 2.4e+198)
  0.0625
  (/
   (/ (* i (+ (fmin alpha beta) i)) (fmax alpha beta))
   (- (+ (fmin alpha beta) (fmax alpha beta)) 1.0))))

C

double code(double alpha, double beta, double i) {
	double tmp;
	if (fmax(alpha, beta) <= 2.4e+198) {
		tmp = 0.0625;
	} else {
		tmp = ((i * (fmin(alpha, beta) + i)) / fmax(alpha, beta)) / ((fmin(alpha, beta) + fmax(alpha, beta)) - 1.0);
	}
	return tmp;
}

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, i)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8), intent (in) :: i
    real(8) :: tmp
    if (fmax(alpha, beta) <= 2.4d+198) then
        tmp = 0.0625d0
    else
        tmp = ((i * (fmin(alpha, beta) + i)) / fmax(alpha, beta)) / ((fmin(alpha, beta) + fmax(alpha, beta)) - 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double alpha, double beta, double i) {
	double tmp;
	if (fmax(alpha, beta) <= 2.4e+198) {
		tmp = 0.0625;
	} else {
		tmp = ((i * (fmin(alpha, beta) + i)) / fmax(alpha, beta)) / ((fmin(alpha, beta) + fmax(alpha, beta)) - 1.0);
	}
	return tmp;
}

Python

def code(alpha, beta, i):
	tmp = 0
	if fmax(alpha, beta) <= 2.4e+198:
		tmp = 0.0625
	else:
		tmp = ((i * (fmin(alpha, beta) + i)) / fmax(alpha, beta)) / ((fmin(alpha, beta) + fmax(alpha, beta)) - 1.0)
	return tmp

Julia

function code(alpha, beta, i)
	tmp = 0.0
	if (fmax(alpha, beta) <= 2.4e+198)
		tmp = 0.0625;
	else
		tmp = Float64(Float64(Float64(i * Float64(fmin(alpha, beta) + i)) / fmax(alpha, beta)) / Float64(Float64(fmin(alpha, beta) + fmax(alpha, beta)) - 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(alpha, beta, i)
	tmp = 0.0;
	if (max(alpha, beta) <= 2.4e+198)
		tmp = 0.0625;
	else
		tmp = ((i * (min(alpha, beta) + i)) / max(alpha, beta)) / ((min(alpha, beta) + max(alpha, beta)) - 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[alpha_, beta_, i_] := If[LessEqual[N[Max[alpha, beta], $MachinePrecision], 2.4e+198], 0.0625, N[(N[(N[(i * N[(N[Min[alpha, beta], $MachinePrecision] + i), $MachinePrecision]), $MachinePrecision] / N[Max[alpha, beta], $MachinePrecision]), $MachinePrecision] / N[(N[(N[Min[alpha, beta], $MachinePrecision] + N[Max[alpha, beta], $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if beta < 2.4000000000000001e198

   1. Initial program 15.7%

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

   2. Taylor expanded in i around inf

      \[\leadsto \color{blue}{\frac{1}{16}} \]
   3. Step-by-step derivation

   4. Applied rewrites 71.2%

      \[\leadsto \color{blue}{0.0625} \]

   if 2.4000000000000001e198 < beta

   1. Initial program 15.7%

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

   2. Taylor expanded in alpha around -inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-*.f64 13.4%

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

   4. Applied rewrites 13.4%

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

      1. lift-/.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. difference-of-sqr-1 N/A

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

      5. associate-/r* N/A

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

   6. Applied rewrites 17.3%

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

   7. Taylor expanded in beta around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 12.2%

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

   9. Applied rewrites 12.2%

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

   10. Taylor expanded in i around 0

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

       1. lower--.f64 N/A

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

       2. lower-+.f64 12.1%

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

   12. Applied rewrites 12.1%

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

Alternative 5: 71.2% accurate, 75.0× speedup

Math

\[0.0625 \]

FPCore

(FPCore (alpha beta i)
  :precision binary64
  0.0625)

C

double code(double alpha, double beta, double i) {
	return 0.0625;
}

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, i)
use fmin_fmax_functions
    real(8), intent (in) :: alpha
    real(8), intent (in) :: beta
    real(8), intent (in) :: i
    code = 0.0625d0
end function

Java

public static double code(double alpha, double beta, double i) {
	return 0.0625;
}

Python

def code(alpha, beta, i):
	return 0.0625

Julia

function code(alpha, beta, i)
	return 0.0625
end

MATLAB

function tmp = code(alpha, beta, i)
	tmp = 0.0625;
end

Wolfram

code[alpha_, beta_, i_] := 0.0625

Derivation

1. Initial program 15.7%

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

2. Taylor expanded in i around inf

   \[\leadsto \color{blue}{\frac{1}{16}} \]
3. Step-by-step derivation

4. Applied rewrites 71.2%

   \[\leadsto \color{blue}{0.0625} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025213 
(FPCore (alpha beta i)
  :name "Octave 3.8, jcobi/4"
  :precision binary64
  :pre (and (and (> alpha -1.0) (> beta -1.0)) (> i 1.0))
  (/ (/ (* (* i (+ (+ alpha beta) i)) (+ (* beta alpha) (* i (+ (+ alpha beta) i)))) (* (+ (+ alpha beta) (* 2.0 i)) (+ (+ alpha beta) (* 2.0 i)))) (- (* (+ (+ alpha beta) (* 2.0 i)) (+ (+ alpha beta) (* 2.0 i))) 1.0)))