Diagrams.Trail:splitAtParam from diagrams-lib-1.3.0.3, D

Percentage Accurate: 65.9% → 99.9%

Time: 3.5s

Alternatives: 16

Speedup: 2.0×

Specification

Math

\[\begin{array}{l} \\ 1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- 1.0 (/ (* (- 1.0 x) y) (+ y 1.0))))

C

double code(double x, double y) {
	return 1.0 - (((1.0 - x) * y) / (y + 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(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 - (((1.0d0 - x) * y) / (y + 1.0d0))
end function

Java

public static double code(double x, double y) {
	return 1.0 - (((1.0 - x) * y) / (y + 1.0));
}

Python

def code(x, y):
	return 1.0 - (((1.0 - x) * y) / (y + 1.0))

Julia

function code(x, y)
	return Float64(1.0 - Float64(Float64(Float64(1.0 - x) * y) / Float64(y + 1.0)))
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 - (((1.0 - x) * y) / (y + 1.0));
end

Wolfram

code[x_, y_] := N[(1.0 - N[(N[(N[(1.0 - x), $MachinePrecision] * y), $MachinePrecision] / N[(y + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 65.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- 1.0 (/ (* (- 1.0 x) y) (+ y 1.0))))

C

double code(double x, double y) {
	return 1.0 - (((1.0 - x) * y) / (y + 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(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 - (((1.0d0 - x) * y) / (y + 1.0d0))
end function

Java

public static double code(double x, double y) {
	return 1.0 - (((1.0 - x) * y) / (y + 1.0));
}

Python

def code(x, y):
	return 1.0 - (((1.0 - x) * y) / (y + 1.0))

Julia

function code(x, y)
	return Float64(1.0 - Float64(Float64(Float64(1.0 - x) * y) / Float64(y + 1.0)))
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 - (((1.0 - x) * y) / (y + 1.0));
end

Wolfram

code[x_, y_] := N[(1.0 - N[(N[(N[(1.0 - x), $MachinePrecision] * y), $MachinePrecision] / N[(y + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 \cdot \left(y + 1\right)\\ \mathbf{if}\;y \leq -1 \cdot 10^{+16}:\\ \;\;\;\;x + \left(\mathsf{fma}\left(\frac{-1}{y}, \frac{\mathsf{fma}\left(-1, x, 1\right)}{y}, {y}^{-1}\right) - \frac{x}{y}\right)\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{+15}:\\ \;\;\;\;\frac{t\_0 - 2 \cdot \left(\left(1 - x\right) \cdot y\right)}{t\_0}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{x - 1}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* 2.0 (+ y 1.0))))
   (if (<= y -1e+16)
     (+ x (- (fma (/ -1.0 y) (/ (fma -1.0 x 1.0) y) (pow y -1.0)) (/ x y)))
     (if (<= y 4.4e+15)
       (/ (- t_0 (* 2.0 (* (- 1.0 x) y))) t_0)
       (- x (/ (- x 1.0) y))))))

C

double code(double x, double y) {
	double t_0 = 2.0 * (y + 1.0);
	double tmp;
	if (y <= -1e+16) {
		tmp = x + (fma((-1.0 / y), (fma(-1.0, x, 1.0) / y), pow(y, -1.0)) - (x / y));
	} else if (y <= 4.4e+15) {
		tmp = (t_0 - (2.0 * ((1.0 - x) * y))) / t_0;
	} else {
		tmp = x - ((x - 1.0) / y);
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(2.0 * Float64(y + 1.0))
	tmp = 0.0
	if (y <= -1e+16)
		tmp = Float64(x + Float64(fma(Float64(-1.0 / y), Float64(fma(-1.0, x, 1.0) / y), (y ^ -1.0)) - Float64(x / y)));
	elseif (y <= 4.4e+15)
		tmp = Float64(Float64(t_0 - Float64(2.0 * Float64(Float64(1.0 - x) * y))) / t_0);
	else
		tmp = Float64(x - Float64(Float64(x - 1.0) / y));
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(2.0 * N[(y + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1e+16], N[(x + N[(N[(N[(-1.0 / y), $MachinePrecision] * N[(N[(-1.0 * x + 1.0), $MachinePrecision] / y), $MachinePrecision] + N[Power[y, -1.0], $MachinePrecision]), $MachinePrecision] - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 4.4e+15], N[(N[(t$95$0 - N[(2.0 * N[(N[(1.0 - x), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t$95$0), $MachinePrecision], N[(x - N[(N[(x - 1.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1e16

   1. Initial program 29.6%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

      1. associate--l+ N/A

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

      2. lower-+.f64 N/A

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

      3. lower--.f64 N/A

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

      4. associate-*r/ N/A

         \[\leadsto x + \left(\left(\frac{-1 \cdot \left(1 + -1 \cdot x\right)}{{y}^{2}} + \frac{1}{y}\right) - \frac{x}{y}\right) \]

      5. unpow2 N/A

         \[\leadsto x + \left(\left(\frac{-1 \cdot \left(1 + -1 \cdot x\right)}{y \cdot y} + \frac{1}{y}\right) - \frac{x}{y}\right) \]

      6. times-frac N/A

         \[\leadsto x + \left(\left(\frac{-1}{y} \cdot \frac{1 + -1 \cdot x}{y} + \frac{1}{y}\right) - \frac{x}{y}\right) \]

      7. lower-fma.f64 N/A

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

      8. lower-/.f64 N/A

         \[\leadsto x + \left(\mathsf{fma}\left(\frac{-1}{y}, \frac{1 + -1 \cdot x}{y}, \frac{1}{y}\right) - \frac{x}{y}\right) \]

      9. lower-/.f64 N/A

         \[\leadsto x + \left(\mathsf{fma}\left(\frac{-1}{y}, \frac{1 + -1 \cdot x}{y}, \frac{1}{y}\right) - \frac{x}{y}\right) \]

      10. +-commutative N/A

          \[\leadsto x + \left(\mathsf{fma}\left(\frac{-1}{y}, \frac{-1 \cdot x + 1}{y}, \frac{1}{y}\right) - \frac{x}{y}\right) \]

      11. lower-fma.f64 N/A

          \[\leadsto x + \left(\mathsf{fma}\left(\frac{-1}{y}, \frac{\mathsf{fma}\left(-1, x, 1\right)}{y}, \frac{1}{y}\right) - \frac{x}{y}\right) \]

      12. inv-pow N/A

          \[\leadsto x + \left(\mathsf{fma}\left(\frac{-1}{y}, \frac{\mathsf{fma}\left(-1, x, 1\right)}{y}, {y}^{-1}\right) - \frac{x}{y}\right) \]

      13. lower-pow.f64 N/A

          \[\leadsto x + \left(\mathsf{fma}\left(\frac{-1}{y}, \frac{\mathsf{fma}\left(-1, x, 1\right)}{y}, {y}^{-1}\right) - \frac{x}{y}\right) \]

      14. lower-/.f64 100.0

          \[\leadsto x + \left(\mathsf{fma}\left(\frac{-1}{y}, \frac{\mathsf{fma}\left(-1, x, 1\right)}{y}, {y}^{-1}\right) - \frac{x}{\color{blue}{y}}\right) \]

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{x + \left(\mathsf{fma}\left(\frac{-1}{y}, \frac{\mathsf{fma}\left(-1, x, 1\right)}{y}, {y}^{-1}\right) - \frac{x}{y}\right)} \]

   if -1e16 < y < 4.4e15

   1. Initial program 98.9%

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

      1. lift--.f64 N/A

         \[\leadsto \color{blue}{1 - \frac{\left(1 - x\right) \cdot y}{y + 1}} \]

      2. metadata-eval N/A

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

      3. lift-+.f64 N/A

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

      4. lift-/.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. frac-sub N/A

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

      8. lower-/.f64 N/A

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

      9. lower--.f64 N/A

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

      10. lower-*.f64 N/A

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

      11. lift-+.f64 N/A

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

      12. lower-*.f64 N/A

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

      13. lift-*.f64 N/A

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

      14. lift--.f64 N/A

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

      15. lower-*.f64 N/A

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

      16. lift-+.f64 99.8

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

   4. Applied rewrites 99.8%

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

   if 4.4e15 < y

   1. Initial program 29.1%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around -inf

      \[\leadsto \color{blue}{x + -1 \cdot \frac{x - 1}{y}} \]
   4. Step-by-step derivation

      1. fp-cancel-sign-sub-inv N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(-1\right)\right) \cdot \frac{x - 1}{y}} \]

      2. metadata-eval N/A

         \[\leadsto x - 1 \cdot \frac{\color{blue}{x - 1}}{y} \]

      3. metadata-eval N/A

         \[\leadsto x - \frac{-1}{-1} \cdot \frac{\color{blue}{x - 1}}{y} \]

      4. times-frac N/A

         \[\leadsto x - \frac{-1 \cdot \left(x - 1\right)}{\color{blue}{-1 \cdot y}} \]

      5. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\color{blue}{-1} \cdot y} \]

      6. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\mathsf{neg}\left(y\right)} \]

      7. frac-2neg N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      8. lower--.f64 N/A

         \[\leadsto x - \color{blue}{\frac{x - 1}{y}} \]

      9. lower-/.f64 N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      10. lower--.f64 100.0

          \[\leadsto x - \frac{x - 1}{y} \]

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{x - \frac{x - 1}{y}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 2: 99.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 2 \cdot \left(y + 1\right)\\ t_1 := x - \frac{x - 1}{y}\\ \mathbf{if}\;y \leq -1.4 \cdot 10^{+15}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{+15}:\\ \;\;\;\;\frac{t\_0 - 2 \cdot \left(\left(1 - x\right) \cdot y\right)}{t\_0}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* 2.0 (+ y 1.0))) (t_1 (- x (/ (- x 1.0) y))))
   (if (<= y -1.4e+15)
     t_1
     (if (<= y 4.4e+15) (/ (- t_0 (* 2.0 (* (- 1.0 x) y))) t_0) t_1))))

C

double code(double x, double y) {
	double t_0 = 2.0 * (y + 1.0);
	double t_1 = x - ((x - 1.0) / y);
	double tmp;
	if (y <= -1.4e+15) {
		tmp = t_1;
	} else if (y <= 4.4e+15) {
		tmp = (t_0 - (2.0 * ((1.0 - x) * y))) / t_0;
	} else {
		tmp = t_1;
	}
	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(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 2.0d0 * (y + 1.0d0)
    t_1 = x - ((x - 1.0d0) / y)
    if (y <= (-1.4d+15)) then
        tmp = t_1
    else if (y <= 4.4d+15) then
        tmp = (t_0 - (2.0d0 * ((1.0d0 - x) * y))) / t_0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = 2.0 * (y + 1.0);
	double t_1 = x - ((x - 1.0) / y);
	double tmp;
	if (y <= -1.4e+15) {
		tmp = t_1;
	} else if (y <= 4.4e+15) {
		tmp = (t_0 - (2.0 * ((1.0 - x) * y))) / t_0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 2.0 * (y + 1.0)
	t_1 = x - ((x - 1.0) / y)
	tmp = 0
	if y <= -1.4e+15:
		tmp = t_1
	elif y <= 4.4e+15:
		tmp = (t_0 - (2.0 * ((1.0 - x) * y))) / t_0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y)
	t_0 = Float64(2.0 * Float64(y + 1.0))
	t_1 = Float64(x - Float64(Float64(x - 1.0) / y))
	tmp = 0.0
	if (y <= -1.4e+15)
		tmp = t_1;
	elseif (y <= 4.4e+15)
		tmp = Float64(Float64(t_0 - Float64(2.0 * Float64(Float64(1.0 - x) * y))) / t_0);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 2.0 * (y + 1.0);
	t_1 = x - ((x - 1.0) / y);
	tmp = 0.0;
	if (y <= -1.4e+15)
		tmp = t_1;
	elseif (y <= 4.4e+15)
		tmp = (t_0 - (2.0 * ((1.0 - x) * y))) / t_0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(2.0 * N[(y + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x - N[(N[(x - 1.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.4e+15], t$95$1, If[LessEqual[y, 4.4e+15], N[(N[(t$95$0 - N[(2.0 * N[(N[(1.0 - x), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t$95$0), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.4e15 or 4.4e15 < y

   1. Initial program 29.4%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around -inf

      \[\leadsto \color{blue}{x + -1 \cdot \frac{x - 1}{y}} \]
   4. Step-by-step derivation

      1. fp-cancel-sign-sub-inv N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(-1\right)\right) \cdot \frac{x - 1}{y}} \]

      2. metadata-eval N/A

         \[\leadsto x - 1 \cdot \frac{\color{blue}{x - 1}}{y} \]

      3. metadata-eval N/A

         \[\leadsto x - \frac{-1}{-1} \cdot \frac{\color{blue}{x - 1}}{y} \]

      4. times-frac N/A

         \[\leadsto x - \frac{-1 \cdot \left(x - 1\right)}{\color{blue}{-1 \cdot y}} \]

      5. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\color{blue}{-1} \cdot y} \]

      6. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\mathsf{neg}\left(y\right)} \]

      7. frac-2neg N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      8. lower--.f64 N/A

         \[\leadsto x - \color{blue}{\frac{x - 1}{y}} \]

      9. lower-/.f64 N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      10. lower--.f64 100.0

          \[\leadsto x - \frac{x - 1}{y} \]

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{x - \frac{x - 1}{y}} \]

   if -1.4e15 < y < 4.4e15

   1. Initial program 99.0%

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

      1. lift--.f64 N/A

         \[\leadsto \color{blue}{1 - \frac{\left(1 - x\right) \cdot y}{y + 1}} \]

      2. metadata-eval N/A

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

      3. lift-+.f64 N/A

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

      4. lift-/.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. frac-sub N/A

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

      8. lower-/.f64 N/A

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

      9. lower--.f64 N/A

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

      10. lower-*.f64 N/A

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

      11. lift-+.f64 N/A

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

      12. lower-*.f64 N/A

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

      13. lift-*.f64 N/A

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

      14. lift--.f64 N/A

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

      15. lower-*.f64 N/A

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

      16. lift-+.f64 99.8

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

   4. Applied rewrites 99.8%

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

Alternative 3: 99.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(\frac{\left(-\frac{x - 1}{y}\right) - \left(-\left(x - 1\right)\right)}{y}, -1, x\right)\\ \mathbf{if}\;y \leq -250000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 270000:\\ \;\;\;\;1 - \frac{\left(1 - x\right) \cdot y}{y + 1}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (fma (/ (- (- (/ (- x 1.0) y)) (- (- x 1.0))) y) -1.0 x)))
   (if (<= y -250000.0)
     t_0
     (if (<= y 270000.0) (- 1.0 (/ (* (- 1.0 x) y) (+ y 1.0))) t_0))))

C

double code(double x, double y) {
	double t_0 = fma(((-((x - 1.0) / y) - -(x - 1.0)) / y), -1.0, x);
	double tmp;
	if (y <= -250000.0) {
		tmp = t_0;
	} else if (y <= 270000.0) {
		tmp = 1.0 - (((1.0 - x) * y) / (y + 1.0));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = fma(Float64(Float64(Float64(-Float64(Float64(x - 1.0) / y)) - Float64(-Float64(x - 1.0))) / y), -1.0, x)
	tmp = 0.0
	if (y <= -250000.0)
		tmp = t_0;
	elseif (y <= 270000.0)
		tmp = Float64(1.0 - Float64(Float64(Float64(1.0 - x) * y) / Float64(y + 1.0)));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[((-N[(N[(x - 1.0), $MachinePrecision] / y), $MachinePrecision]) - (-N[(x - 1.0), $MachinePrecision])), $MachinePrecision] / y), $MachinePrecision] * -1.0 + x), $MachinePrecision]}, If[LessEqual[y, -250000.0], t$95$0, If[LessEqual[y, 270000.0], N[(1.0 - N[(N[(N[(1.0 - x), $MachinePrecision] * y), $MachinePrecision] / N[(y + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -2.5e5 or 2.7e5 < y

   1. Initial program 30.9%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around -inf

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

      1. +-commutative N/A

         \[\leadsto -1 \cdot \frac{-1 \cdot \frac{x - 1}{y} - -1 \cdot \left(x - 1\right)}{y} + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \frac{-1 \cdot \frac{x - 1}{y} - -1 \cdot \left(x - 1\right)}{y} \cdot -1 + x \]

      3. lower-fma.f64 N/A

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

      4. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1 \cdot \frac{x - 1}{y} - -1 \cdot \left(x - 1\right)}{y}, -1, x\right) \]

      5. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1 \cdot \frac{x - 1}{y} - -1 \cdot \left(x - 1\right)}{y}, -1, x\right) \]

      6. mul-1-neg N/A

         \[\leadsto \mathsf{fma}\left(\frac{\left(\mathsf{neg}\left(\frac{x - 1}{y}\right)\right) - -1 \cdot \left(x - 1\right)}{y}, -1, x\right) \]

      7. lower-neg.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\left(-\frac{x - 1}{y}\right) - -1 \cdot \left(x - 1\right)}{y}, -1, x\right) \]

      8. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\left(-\frac{x - 1}{y}\right) - -1 \cdot \left(x - 1\right)}{y}, -1, x\right) \]

      9. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\left(-\frac{x - 1}{y}\right) - -1 \cdot \left(x - 1\right)}{y}, -1, x\right) \]

      10. mul-1-neg N/A

          \[\leadsto \mathsf{fma}\left(\frac{\left(-\frac{x - 1}{y}\right) - \left(\mathsf{neg}\left(\left(x - 1\right)\right)\right)}{y}, -1, x\right) \]

      11. lower-neg.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{\left(-\frac{x - 1}{y}\right) - \left(-\left(x - 1\right)\right)}{y}, -1, x\right) \]

      12. lower--.f64 99.9

          \[\leadsto \mathsf{fma}\left(\frac{\left(-\frac{x - 1}{y}\right) - \left(-\left(x - 1\right)\right)}{y}, -1, x\right) \]

   5. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\left(-\frac{x - 1}{y}\right) - \left(-\left(x - 1\right)\right)}{y}, -1, x\right)} \]

   if -2.5e5 < y < 2.7e5

   1. Initial program 99.8%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 99.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x - \frac{x - 1}{y}\\ \mathbf{if}\;y \leq -440000000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 108000000:\\ \;\;\;\;1 - \frac{\left(1 - x\right) \cdot y}{y + 1}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- x (/ (- x 1.0) y))))
   (if (<= y -440000000.0)
     t_0
     (if (<= y 108000000.0) (- 1.0 (/ (* (- 1.0 x) y) (+ y 1.0))) t_0))))

C

double code(double x, double y) {
	double t_0 = x - ((x - 1.0) / y);
	double tmp;
	if (y <= -440000000.0) {
		tmp = t_0;
	} else if (y <= 108000000.0) {
		tmp = 1.0 - (((1.0 - x) * y) / (y + 1.0));
	} else {
		tmp = t_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(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x - ((x - 1.0d0) / y)
    if (y <= (-440000000.0d0)) then
        tmp = t_0
    else if (y <= 108000000.0d0) then
        tmp = 1.0d0 - (((1.0d0 - x) * y) / (y + 1.0d0))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = x - ((x - 1.0) / y);
	double tmp;
	if (y <= -440000000.0) {
		tmp = t_0;
	} else if (y <= 108000000.0) {
		tmp = 1.0 - (((1.0 - x) * y) / (y + 1.0));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x - ((x - 1.0) / y)
	tmp = 0
	if y <= -440000000.0:
		tmp = t_0
	elif y <= 108000000.0:
		tmp = 1.0 - (((1.0 - x) * y) / (y + 1.0))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x - Float64(Float64(x - 1.0) / y))
	tmp = 0.0
	if (y <= -440000000.0)
		tmp = t_0;
	elseif (y <= 108000000.0)
		tmp = Float64(1.0 - Float64(Float64(Float64(1.0 - x) * y) / Float64(y + 1.0)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x - ((x - 1.0) / y);
	tmp = 0.0;
	if (y <= -440000000.0)
		tmp = t_0;
	elseif (y <= 108000000.0)
		tmp = 1.0 - (((1.0 - x) * y) / (y + 1.0));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x - N[(N[(x - 1.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -440000000.0], t$95$0, If[LessEqual[y, 108000000.0], N[(1.0 - N[(N[(N[(1.0 - x), $MachinePrecision] * y), $MachinePrecision] / N[(y + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -4.4e8 or 1.08e8 < y

   1. Initial program 30.4%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around -inf

      \[\leadsto \color{blue}{x + -1 \cdot \frac{x - 1}{y}} \]
   4. Step-by-step derivation

      1. fp-cancel-sign-sub-inv N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(-1\right)\right) \cdot \frac{x - 1}{y}} \]

      2. metadata-eval N/A

         \[\leadsto x - 1 \cdot \frac{\color{blue}{x - 1}}{y} \]

      3. metadata-eval N/A

         \[\leadsto x - \frac{-1}{-1} \cdot \frac{\color{blue}{x - 1}}{y} \]

      4. times-frac N/A

         \[\leadsto x - \frac{-1 \cdot \left(x - 1\right)}{\color{blue}{-1 \cdot y}} \]

      5. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\color{blue}{-1} \cdot y} \]

      6. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\mathsf{neg}\left(y\right)} \]

      7. frac-2neg N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      8. lower--.f64 N/A

         \[\leadsto x - \color{blue}{\frac{x - 1}{y}} \]

      9. lower-/.f64 N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      10. lower--.f64 99.8

          \[\leadsto x - \frac{x - 1}{y} \]

   5. Applied rewrites 99.8%

      \[\leadsto \color{blue}{x - \frac{x - 1}{y}} \]

   if -4.4e8 < y < 1.08e8

   1. Initial program 99.7%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 98.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x - \frac{x - 1}{y}\\ \mathbf{if}\;y \leq -34:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 125000:\\ \;\;\;\;1 - \frac{\left(-x\right) \cdot y}{y + 1}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- x (/ (- x 1.0) y))))
   (if (<= y -34.0)
     t_0
     (if (<= y 125000.0) (- 1.0 (/ (* (- x) y) (+ y 1.0))) t_0))))

C

double code(double x, double y) {
	double t_0 = x - ((x - 1.0) / y);
	double tmp;
	if (y <= -34.0) {
		tmp = t_0;
	} else if (y <= 125000.0) {
		tmp = 1.0 - ((-x * y) / (y + 1.0));
	} else {
		tmp = t_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(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x - ((x - 1.0d0) / y)
    if (y <= (-34.0d0)) then
        tmp = t_0
    else if (y <= 125000.0d0) then
        tmp = 1.0d0 - ((-x * y) / (y + 1.0d0))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = x - ((x - 1.0) / y);
	double tmp;
	if (y <= -34.0) {
		tmp = t_0;
	} else if (y <= 125000.0) {
		tmp = 1.0 - ((-x * y) / (y + 1.0));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x - ((x - 1.0) / y)
	tmp = 0
	if y <= -34.0:
		tmp = t_0
	elif y <= 125000.0:
		tmp = 1.0 - ((-x * y) / (y + 1.0))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x - Float64(Float64(x - 1.0) / y))
	tmp = 0.0
	if (y <= -34.0)
		tmp = t_0;
	elseif (y <= 125000.0)
		tmp = Float64(1.0 - Float64(Float64(Float64(-x) * y) / Float64(y + 1.0)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x - ((x - 1.0) / y);
	tmp = 0.0;
	if (y <= -34.0)
		tmp = t_0;
	elseif (y <= 125000.0)
		tmp = 1.0 - ((-x * y) / (y + 1.0));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x - N[(N[(x - 1.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -34.0], t$95$0, If[LessEqual[y, 125000.0], N[(1.0 - N[(N[((-x) * y), $MachinePrecision] / N[(y + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -34 or 125000 < y

   1. Initial program 31.3%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around -inf

      \[\leadsto \color{blue}{x + -1 \cdot \frac{x - 1}{y}} \]
   4. Step-by-step derivation

      1. fp-cancel-sign-sub-inv N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(-1\right)\right) \cdot \frac{x - 1}{y}} \]

      2. metadata-eval N/A

         \[\leadsto x - 1 \cdot \frac{\color{blue}{x - 1}}{y} \]

      3. metadata-eval N/A

         \[\leadsto x - \frac{-1}{-1} \cdot \frac{\color{blue}{x - 1}}{y} \]

      4. times-frac N/A

         \[\leadsto x - \frac{-1 \cdot \left(x - 1\right)}{\color{blue}{-1 \cdot y}} \]

      5. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\color{blue}{-1} \cdot y} \]

      6. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\mathsf{neg}\left(y\right)} \]

      7. frac-2neg N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      8. lower--.f64 N/A

         \[\leadsto x - \color{blue}{\frac{x - 1}{y}} \]

      9. lower-/.f64 N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      10. lower--.f64 99.1

          \[\leadsto x - \frac{x - 1}{y} \]

   5. Applied rewrites 99.1%

      \[\leadsto \color{blue}{x - \frac{x - 1}{y}} \]

   if -34 < y < 125000

   1. Initial program 99.9%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

         \[\leadsto 1 - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y + 1} \]

      2. lower-neg.f64 98.6

         \[\leadsto 1 - \frac{\left(-x\right) \cdot y}{y + 1} \]

   5. Applied rewrites 98.6%

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

Alternative 6: 84.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;x + \frac{-x}{y}\\ \mathbf{elif}\;y \leq 1.05:\\ \;\;\;\;\mathsf{fma}\left(x - 1, y, 1\right)\\ \mathbf{elif}\;y \leq 1.7 \cdot 10^{+109}:\\ \;\;\;\;\frac{2}{2 \cdot y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.0)
   (+ x (/ (- x) y))
   (if (<= y 1.05)
     (fma (- x 1.0) y 1.0)
     (if (<= y 1.7e+109) (/ 2.0 (* 2.0 y)) x))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = x + (-x / y);
	} else if (y <= 1.05) {
		tmp = fma((x - 1.0), y, 1.0);
	} else if (y <= 1.7e+109) {
		tmp = 2.0 / (2.0 * y);
	} else {
		tmp = x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.0)
		tmp = Float64(x + Float64(Float64(-x) / y));
	elseif (y <= 1.05)
		tmp = fma(Float64(x - 1.0), y, 1.0);
	elseif (y <= 1.7e+109)
		tmp = Float64(2.0 / Float64(2.0 * y));
	else
		tmp = x;
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.0], N[(x + N[((-x) / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.05], N[(N[(x - 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision], If[LessEqual[y, 1.7e+109], N[(2.0 / N[(2.0 * y), $MachinePrecision]), $MachinePrecision], x]]]

Derivation

1. Split input into 4 regimes

2. if y < -1

   1. Initial program 32.2%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

         \[\leadsto 1 - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y + 1} \]

      2. lower-neg.f64 27.4

         \[\leadsto 1 - \frac{\left(-x\right) \cdot y}{y + 1} \]

   5. Applied rewrites 27.4%

      \[\leadsto 1 - \frac{\color{blue}{\left(-x\right)} \cdot y}{y + 1} \]

   6. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\left(x + \frac{1}{y}\right) - \frac{x}{y}} \]
   7. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \left(\color{blue}{x} + \frac{1}{y}\right) - \frac{x}{y} \]

      2. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{\color{blue}{y}}\right) - \frac{x}{y} \]

      3. frac-sub N/A

         \[\leadsto \color{blue}{\left(x + \frac{1}{y}\right)} - \frac{x}{y} \]

      4. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{y}\right) - \frac{x}{y} \]

      5. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{\color{blue}{y}}\right) - \frac{x}{y} \]

      6. associate--l+ N/A

         \[\leadsto x + \color{blue}{\left(\frac{1}{y} - \frac{x}{y}\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto x + \color{blue}{\left(\frac{1}{y} - \frac{x}{y}\right)} \]

      8. sub-div N/A

         \[\leadsto x + \frac{1 - x}{\color{blue}{y}} \]

      9. lower-/.f64 N/A

         \[\leadsto x + \frac{1 - x}{\color{blue}{y}} \]

      10. lower--.f64 98.5

          \[\leadsto x + \frac{1 - x}{y} \]

   8. Applied rewrites 98.5%

      \[\leadsto \color{blue}{x + \frac{1 - x}{y}} \]

   9. Taylor expanded in x around inf

      \[\leadsto x + -1 \cdot \color{blue}{\frac{x}{y}} \]
   10. Step-by-step derivation

       1. associate-*r/ N/A

          \[\leadsto x + \frac{-1 \cdot x}{y} \]

       2. mul-1-neg N/A

          \[\leadsto x + \frac{\mathsf{neg}\left(x\right)}{y} \]

       3. lift-neg.f64 N/A

          \[\leadsto x + \frac{-x}{y} \]

       4. lower-/.f64 72.5

          \[\leadsto x + \frac{-x}{y} \]

   11. Applied rewrites 72.5%

       \[\leadsto x + \frac{-x}{\color{blue}{y}} \]

   if -1 < y < 1.05000000000000004

   1. Initial program 100.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

         \[\leadsto y \cdot \left(x - 1\right) + \color{blue}{1} \]

      2. *-commutative N/A

         \[\leadsto \left(x - 1\right) \cdot y + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x - 1, \color{blue}{y}, 1\right) \]

      4. lower--.f64 98.7

         \[\leadsto \mathsf{fma}\left(x - 1, y, 1\right) \]

   5. Applied rewrites 98.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - 1, y, 1\right)} \]

   if 1.05000000000000004 < y < 1.70000000000000003e109

   1. Initial program 51.6%

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

      1. lift--.f64 N/A

         \[\leadsto \color{blue}{1 - \frac{\left(1 - x\right) \cdot y}{y + 1}} \]

      2. metadata-eval N/A

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

      3. lift-+.f64 N/A

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

      4. lift-/.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. frac-sub N/A

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

      8. lower-/.f64 N/A

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

      9. lower--.f64 N/A

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

      10. lower-*.f64 N/A

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

      11. lift-+.f64 N/A

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

      12. lower-*.f64 N/A

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

      13. lift-*.f64 N/A

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

      14. lift--.f64 N/A

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

      15. lower-*.f64 N/A

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

      16. lift-+.f64 54.2

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

   4. Applied rewrites 54.2%

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

   5. Taylor expanded in y around 0

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

   7. Applied rewrites 40.2%

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

   8. Taylor expanded in y around inf

      \[\leadsto \frac{2}{2 \cdot \color{blue}{y}} \]
   9. Step-by-step derivation

   10. Applied rewrites 37.3%

       \[\leadsto \frac{2}{2 \cdot \color{blue}{y}} \]

   if 1.70000000000000003e109 < y

   1. Initial program 20.9%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

   5. Applied rewrites 84.7%

      \[\leadsto \color{blue}{x} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 7: 84.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;x + \frac{-x}{y}\\ \mathbf{elif}\;y \leq 1.12:\\ \;\;\;\;\mathsf{fma}\left(x - 1, y, 1\right)\\ \mathbf{elif}\;y \leq 9.5 \cdot 10^{+108}:\\ \;\;\;\;\frac{1 - x}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.0)
   (+ x (/ (- x) y))
   (if (<= y 1.12)
     (fma (- x 1.0) y 1.0)
     (if (<= y 9.5e+108) (/ (- 1.0 x) y) x))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = x + (-x / y);
	} else if (y <= 1.12) {
		tmp = fma((x - 1.0), y, 1.0);
	} else if (y <= 9.5e+108) {
		tmp = (1.0 - x) / y;
	} else {
		tmp = x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.0)
		tmp = Float64(x + Float64(Float64(-x) / y));
	elseif (y <= 1.12)
		tmp = fma(Float64(x - 1.0), y, 1.0);
	elseif (y <= 9.5e+108)
		tmp = Float64(Float64(1.0 - x) / y);
	else
		tmp = x;
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.0], N[(x + N[((-x) / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.12], N[(N[(x - 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision], If[LessEqual[y, 9.5e+108], N[(N[(1.0 - x), $MachinePrecision] / y), $MachinePrecision], x]]]

Derivation

1. Split input into 4 regimes

2. if y < -1

   1. Initial program 32.2%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

         \[\leadsto 1 - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y + 1} \]

      2. lower-neg.f64 27.4

         \[\leadsto 1 - \frac{\left(-x\right) \cdot y}{y + 1} \]

   5. Applied rewrites 27.4%

      \[\leadsto 1 - \frac{\color{blue}{\left(-x\right)} \cdot y}{y + 1} \]

   6. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\left(x + \frac{1}{y}\right) - \frac{x}{y}} \]
   7. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \left(\color{blue}{x} + \frac{1}{y}\right) - \frac{x}{y} \]

      2. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{\color{blue}{y}}\right) - \frac{x}{y} \]

      3. frac-sub N/A

         \[\leadsto \color{blue}{\left(x + \frac{1}{y}\right)} - \frac{x}{y} \]

      4. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{y}\right) - \frac{x}{y} \]

      5. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{\color{blue}{y}}\right) - \frac{x}{y} \]

      6. associate--l+ N/A

         \[\leadsto x + \color{blue}{\left(\frac{1}{y} - \frac{x}{y}\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto x + \color{blue}{\left(\frac{1}{y} - \frac{x}{y}\right)} \]

      8. sub-div N/A

         \[\leadsto x + \frac{1 - x}{\color{blue}{y}} \]

      9. lower-/.f64 N/A

         \[\leadsto x + \frac{1 - x}{\color{blue}{y}} \]

      10. lower--.f64 98.5

          \[\leadsto x + \frac{1 - x}{y} \]

   8. Applied rewrites 98.5%

      \[\leadsto \color{blue}{x + \frac{1 - x}{y}} \]

   9. Taylor expanded in x around inf

      \[\leadsto x + -1 \cdot \color{blue}{\frac{x}{y}} \]
   10. Step-by-step derivation

       1. associate-*r/ N/A

          \[\leadsto x + \frac{-1 \cdot x}{y} \]

       2. mul-1-neg N/A

          \[\leadsto x + \frac{\mathsf{neg}\left(x\right)}{y} \]

       3. lift-neg.f64 N/A

          \[\leadsto x + \frac{-x}{y} \]

       4. lower-/.f64 72.5

          \[\leadsto x + \frac{-x}{y} \]

   11. Applied rewrites 72.5%

       \[\leadsto x + \frac{-x}{\color{blue}{y}} \]

   if -1 < y < 1.1200000000000001

   1. Initial program 100.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

         \[\leadsto y \cdot \left(x - 1\right) + \color{blue}{1} \]

      2. *-commutative N/A

         \[\leadsto \left(x - 1\right) \cdot y + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x - 1, \color{blue}{y}, 1\right) \]

      4. lower--.f64 98.7

         \[\leadsto \mathsf{fma}\left(x - 1, y, 1\right) \]

   5. Applied rewrites 98.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - 1, y, 1\right)} \]

   if 1.1200000000000001 < y < 9.50000000000000097e108

   1. Initial program 51.5%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

         \[\leadsto 1 - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y + 1} \]

      2. lower-neg.f64 47.0

         \[\leadsto 1 - \frac{\left(-x\right) \cdot y}{y + 1} \]

   5. Applied rewrites 47.0%

      \[\leadsto 1 - \frac{\color{blue}{\left(-x\right)} \cdot y}{y + 1} \]

   6. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\left(x + \frac{1}{y}\right) - \frac{x}{y}} \]
   7. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \left(\color{blue}{x} + \frac{1}{y}\right) - \frac{x}{y} \]

      2. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{\color{blue}{y}}\right) - \frac{x}{y} \]

      3. frac-sub N/A

         \[\leadsto \color{blue}{\left(x + \frac{1}{y}\right)} - \frac{x}{y} \]

      4. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{y}\right) - \frac{x}{y} \]

      5. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{\color{blue}{y}}\right) - \frac{x}{y} \]

      6. associate--l+ N/A

         \[\leadsto x + \color{blue}{\left(\frac{1}{y} - \frac{x}{y}\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto x + \color{blue}{\left(\frac{1}{y} - \frac{x}{y}\right)} \]

      8. sub-div N/A

         \[\leadsto x + \frac{1 - x}{\color{blue}{y}} \]

      9. lower-/.f64 N/A

         \[\leadsto x + \frac{1 - x}{\color{blue}{y}} \]

      10. lower--.f64 95.1

          \[\leadsto x + \frac{1 - x}{y} \]

   8. Applied rewrites 95.1%

      \[\leadsto \color{blue}{x + \frac{1 - x}{y}} \]

   9. Taylor expanded in y around 0

      \[\leadsto \frac{1 - x}{\color{blue}{y}} \]
   10. Step-by-step derivation

       1. lift-/.f64 N/A

          \[\leadsto \frac{1 - x}{y} \]

       2. lift--.f64 36.2

          \[\leadsto \frac{1 - x}{y} \]

   11. Applied rewrites 36.2%

       \[\leadsto \frac{1 - x}{\color{blue}{y}} \]

   if 9.50000000000000097e108 < y

   1. Initial program 21.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

   5. Applied rewrites 84.7%

      \[\leadsto \color{blue}{x} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 8: 84.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.12:\\ \;\;\;\;\mathsf{fma}\left(x - 1, y, 1\right)\\ \mathbf{elif}\;y \leq 9.5 \cdot 10^{+108}:\\ \;\;\;\;\frac{1 - x}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.0)
   x
   (if (<= y 1.12)
     (fma (- x 1.0) y 1.0)
     (if (<= y 9.5e+108) (/ (- 1.0 x) y) x))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = x;
	} else if (y <= 1.12) {
		tmp = fma((x - 1.0), y, 1.0);
	} else if (y <= 9.5e+108) {
		tmp = (1.0 - x) / y;
	} else {
		tmp = x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.0)
		tmp = x;
	elseif (y <= 1.12)
		tmp = fma(Float64(x - 1.0), y, 1.0);
	elseif (y <= 9.5e+108)
		tmp = Float64(Float64(1.0 - x) / y);
	else
		tmp = x;
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.0], x, If[LessEqual[y, 1.12], N[(N[(x - 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision], If[LessEqual[y, 9.5e+108], N[(N[(1.0 - x), $MachinePrecision] / y), $MachinePrecision], x]]]

Derivation

1. Split input into 3 regimes

2. if y < -1 or 9.50000000000000097e108 < y

   1. Initial program 27.8%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

   5. Applied rewrites 77.0%

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

   if -1 < y < 1.1200000000000001

   1. Initial program 100.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

         \[\leadsto y \cdot \left(x - 1\right) + \color{blue}{1} \]

      2. *-commutative N/A

         \[\leadsto \left(x - 1\right) \cdot y + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x - 1, \color{blue}{y}, 1\right) \]

      4. lower--.f64 98.7

         \[\leadsto \mathsf{fma}\left(x - 1, y, 1\right) \]

   5. Applied rewrites 98.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - 1, y, 1\right)} \]

   if 1.1200000000000001 < y < 9.50000000000000097e108

   1. Initial program 51.5%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

         \[\leadsto 1 - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y + 1} \]

      2. lower-neg.f64 47.0

         \[\leadsto 1 - \frac{\left(-x\right) \cdot y}{y + 1} \]

   5. Applied rewrites 47.0%

      \[\leadsto 1 - \frac{\color{blue}{\left(-x\right)} \cdot y}{y + 1} \]

   6. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\left(x + \frac{1}{y}\right) - \frac{x}{y}} \]
   7. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \left(\color{blue}{x} + \frac{1}{y}\right) - \frac{x}{y} \]

      2. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{\color{blue}{y}}\right) - \frac{x}{y} \]

      3. frac-sub N/A

         \[\leadsto \color{blue}{\left(x + \frac{1}{y}\right)} - \frac{x}{y} \]

      4. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{y}\right) - \frac{x}{y} \]

      5. +-commutative N/A

         \[\leadsto \left(x + \frac{1}{\color{blue}{y}}\right) - \frac{x}{y} \]

      6. associate--l+ N/A

         \[\leadsto x + \color{blue}{\left(\frac{1}{y} - \frac{x}{y}\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto x + \color{blue}{\left(\frac{1}{y} - \frac{x}{y}\right)} \]

      8. sub-div N/A

         \[\leadsto x + \frac{1 - x}{\color{blue}{y}} \]

      9. lower-/.f64 N/A

         \[\leadsto x + \frac{1 - x}{\color{blue}{y}} \]

      10. lower--.f64 95.1

          \[\leadsto x + \frac{1 - x}{y} \]

   8. Applied rewrites 95.1%

      \[\leadsto \color{blue}{x + \frac{1 - x}{y}} \]

   9. Taylor expanded in y around 0

      \[\leadsto \frac{1 - x}{\color{blue}{y}} \]
   10. Step-by-step derivation

       1. lift-/.f64 N/A

          \[\leadsto \frac{1 - x}{y} \]

       2. lift--.f64 36.2

          \[\leadsto \frac{1 - x}{y} \]

   11. Applied rewrites 36.2%

       \[\leadsto \frac{1 - x}{\color{blue}{y}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 98.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x - \frac{x - 1}{y}\\ \mathbf{if}\;y \leq -1:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(1 - x, y, x\right) - 1, y, 1\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- x (/ (- x 1.0) y))))
   (if (<= y -1.0)
     t_0
     (if (<= y 1.0) (fma (- (fma (- 1.0 x) y x) 1.0) y 1.0) t_0))))

C

double code(double x, double y) {
	double t_0 = x - ((x - 1.0) / y);
	double tmp;
	if (y <= -1.0) {
		tmp = t_0;
	} else if (y <= 1.0) {
		tmp = fma((fma((1.0 - x), y, x) - 1.0), y, 1.0);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(x - Float64(Float64(x - 1.0) / y))
	tmp = 0.0
	if (y <= -1.0)
		tmp = t_0;
	elseif (y <= 1.0)
		tmp = fma(Float64(fma(Float64(1.0 - x), y, x) - 1.0), y, 1.0);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x - N[(N[(x - 1.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.0], t$95$0, If[LessEqual[y, 1.0], N[(N[(N[(N[(1.0 - x), $MachinePrecision] * y + x), $MachinePrecision] - 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1 or 1 < y

   1. Initial program 32.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around -inf

      \[\leadsto \color{blue}{x + -1 \cdot \frac{x - 1}{y}} \]
   4. Step-by-step derivation

      1. fp-cancel-sign-sub-inv N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(-1\right)\right) \cdot \frac{x - 1}{y}} \]

      2. metadata-eval N/A

         \[\leadsto x - 1 \cdot \frac{\color{blue}{x - 1}}{y} \]

      3. metadata-eval N/A

         \[\leadsto x - \frac{-1}{-1} \cdot \frac{\color{blue}{x - 1}}{y} \]

      4. times-frac N/A

         \[\leadsto x - \frac{-1 \cdot \left(x - 1\right)}{\color{blue}{-1 \cdot y}} \]

      5. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\color{blue}{-1} \cdot y} \]

      6. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\mathsf{neg}\left(y\right)} \]

      7. frac-2neg N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      8. lower--.f64 N/A

         \[\leadsto x - \color{blue}{\frac{x - 1}{y}} \]

      9. lower-/.f64 N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      10. lower--.f64 98.4

          \[\leadsto x - \frac{x - 1}{y} \]

   5. Applied rewrites 98.4%

      \[\leadsto \color{blue}{x - \frac{x - 1}{y}} \]

   if -1 < y < 1

   1. Initial program 100.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

         \[\leadsto y \cdot \left(\left(x + y \cdot \left(1 - x\right)\right) - 1\right) + \color{blue}{1} \]

      2. *-commutative N/A

         \[\leadsto \left(\left(x + y \cdot \left(1 - x\right)\right) - 1\right) \cdot y + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\left(x + y \cdot \left(1 - x\right)\right) - 1, \color{blue}{y}, 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(\left(x + y \cdot \left(1 - x\right)\right) - 1, y, 1\right) \]

      5. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\left(y \cdot \left(1 - x\right) + x\right) - 1, y, 1\right) \]

      6. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\left(\left(1 - x\right) \cdot y + x\right) - 1, y, 1\right) \]

      7. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(1 - x, y, x\right) - 1, y, 1\right) \]

      8. lift--.f64 99.4

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(1 - x, y, x\right) - 1, y, 1\right) \]

   5. Applied rewrites 99.4%

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

Alternative 10: 98.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x - \frac{x - 1}{y}\\ \mathbf{if}\;y \leq -1:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;\mathsf{fma}\left(x - 1, y, 1\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- x (/ (- x 1.0) y))))
   (if (<= y -1.0) t_0 (if (<= y 1.0) (fma (- x 1.0) y 1.0) t_0))))

C

double code(double x, double y) {
	double t_0 = x - ((x - 1.0) / y);
	double tmp;
	if (y <= -1.0) {
		tmp = t_0;
	} else if (y <= 1.0) {
		tmp = fma((x - 1.0), y, 1.0);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(x - Float64(Float64(x - 1.0) / y))
	tmp = 0.0
	if (y <= -1.0)
		tmp = t_0;
	elseif (y <= 1.0)
		tmp = fma(Float64(x - 1.0), y, 1.0);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x - N[(N[(x - 1.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.0], t$95$0, If[LessEqual[y, 1.0], N[(N[(x - 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1 or 1 < y

   1. Initial program 32.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around -inf

      \[\leadsto \color{blue}{x + -1 \cdot \frac{x - 1}{y}} \]
   4. Step-by-step derivation

      1. fp-cancel-sign-sub-inv N/A

         \[\leadsto x - \color{blue}{\left(\mathsf{neg}\left(-1\right)\right) \cdot \frac{x - 1}{y}} \]

      2. metadata-eval N/A

         \[\leadsto x - 1 \cdot \frac{\color{blue}{x - 1}}{y} \]

      3. metadata-eval N/A

         \[\leadsto x - \frac{-1}{-1} \cdot \frac{\color{blue}{x - 1}}{y} \]

      4. times-frac N/A

         \[\leadsto x - \frac{-1 \cdot \left(x - 1\right)}{\color{blue}{-1 \cdot y}} \]

      5. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\color{blue}{-1} \cdot y} \]

      6. mul-1-neg N/A

         \[\leadsto x - \frac{\mathsf{neg}\left(\left(x - 1\right)\right)}{\mathsf{neg}\left(y\right)} \]

      7. frac-2neg N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      8. lower--.f64 N/A

         \[\leadsto x - \color{blue}{\frac{x - 1}{y}} \]

      9. lower-/.f64 N/A

         \[\leadsto x - \frac{x - 1}{\color{blue}{y}} \]

      10. lower--.f64 98.4

          \[\leadsto x - \frac{x - 1}{y} \]

   5. Applied rewrites 98.4%

      \[\leadsto \color{blue}{x - \frac{x - 1}{y}} \]

   if -1 < y < 1

   1. Initial program 100.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

         \[\leadsto y \cdot \left(x - 1\right) + \color{blue}{1} \]

      2. *-commutative N/A

         \[\leadsto \left(x - 1\right) \cdot y + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x - 1, \color{blue}{y}, 1\right) \]

      4. lower--.f64 98.7

         \[\leadsto \mathsf{fma}\left(x - 1, y, 1\right) \]

   5. Applied rewrites 98.7%

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

Alternative 11: 86.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 46000000000:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(-x, y, x\right), y, 1\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.0) x (if (<= y 46000000000.0) (fma (fma (- x) y x) y 1.0) x)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = x;
	} else if (y <= 46000000000.0) {
		tmp = fma(fma(-x, y, x), y, 1.0);
	} else {
		tmp = x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.0)
		tmp = x;
	elseif (y <= 46000000000.0)
		tmp = fma(fma(Float64(-x), y, x), y, 1.0);
	else
		tmp = x;
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.0], x, If[LessEqual[y, 46000000000.0], N[(N[((-x) * y + x), $MachinePrecision] * y + 1.0), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if y < -1 or 4.6e10 < y

   1. Initial program 31.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

   5. Applied rewrites 74.5%

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

   if -1 < y < 4.6e10

   1. Initial program 99.7%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

         \[\leadsto y \cdot \left(\left(x + y \cdot \left(1 - x\right)\right) - 1\right) + \color{blue}{1} \]

      2. *-commutative N/A

         \[\leadsto \left(\left(x + y \cdot \left(1 - x\right)\right) - 1\right) \cdot y + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\left(x + y \cdot \left(1 - x\right)\right) - 1, \color{blue}{y}, 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(\left(x + y \cdot \left(1 - x\right)\right) - 1, y, 1\right) \]

      5. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\left(y \cdot \left(1 - x\right) + x\right) - 1, y, 1\right) \]

      6. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\left(\left(1 - x\right) \cdot y + x\right) - 1, y, 1\right) \]

      7. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(1 - x, y, x\right) - 1, y, 1\right) \]

      8. lift--.f64 97.7

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(1 - x, y, x\right) - 1, y, 1\right) \]

   5. Applied rewrites 97.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(1 - x, y, x\right) - 1, y, 1\right)} \]

   6. Taylor expanded in x around -inf

      \[\leadsto \mathsf{fma}\left(-1 \cdot \left(x \cdot \left(y - 1\right)\right), y, 1\right) \]
   7. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \mathsf{fma}\left(\left(-1 \cdot x\right) \cdot \left(y - 1\right), y, 1\right) \]

      2. mul-1-neg N/A

         \[\leadsto \mathsf{fma}\left(\left(\mathsf{neg}\left(x\right)\right) \cdot \left(y - 1\right), y, 1\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\left(\mathsf{neg}\left(x\right)\right) \cdot \left(y - 1\right), y, 1\right) \]

      4. lower-neg.f64 N/A

         \[\leadsto \mathsf{fma}\left(\left(-x\right) \cdot \left(y - 1\right), y, 1\right) \]

      5. lower--.f64 97.1

         \[\leadsto \mathsf{fma}\left(\left(-x\right) \cdot \left(y - 1\right), y, 1\right) \]

   8. Applied rewrites 97.1%

      \[\leadsto \mathsf{fma}\left(\left(-x\right) \cdot \left(y - 1\right), y, 1\right) \]

   9. Taylor expanded in y around 0

      \[\leadsto \mathsf{fma}\left(x + -1 \cdot \left(x \cdot y\right), y, 1\right) \]
   10. Step-by-step derivation

       1. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(-1 \cdot \left(x \cdot y\right) + x, y, 1\right) \]

       2. associate-*r* N/A

          \[\leadsto \mathsf{fma}\left(\left(-1 \cdot x\right) \cdot y + x, y, 1\right) \]

       3. mul-1-neg N/A

          \[\leadsto \mathsf{fma}\left(\left(\mathsf{neg}\left(x\right)\right) \cdot y + x, y, 1\right) \]

       4. lift-neg.f64 N/A

          \[\leadsto \mathsf{fma}\left(\left(-x\right) \cdot y + x, y, 1\right) \]

       5. lower-fma.f64 97.1

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(-x, y, x\right), y, 1\right) \]

   11. Applied rewrites 97.1%

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(-x, y, x\right), y, 1\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 86.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;\mathsf{fma}\left(x - 1, y, 1\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.0) x (if (<= y 1.0) (fma (- x 1.0) y 1.0) x)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = x;
	} else if (y <= 1.0) {
		tmp = fma((x - 1.0), y, 1.0);
	} else {
		tmp = x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.0)
		tmp = x;
	elseif (y <= 1.0)
		tmp = fma(Float64(x - 1.0), y, 1.0);
	else
		tmp = x;
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.0], x, If[LessEqual[y, 1.0], N[(N[(x - 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if y < -1 or 1 < y

   1. Initial program 32.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

   5. Applied rewrites 73.6%

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

   if -1 < y < 1

   1. Initial program 100.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

         \[\leadsto y \cdot \left(x - 1\right) + \color{blue}{1} \]

      2. *-commutative N/A

         \[\leadsto \left(x - 1\right) \cdot y + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x - 1, \color{blue}{y}, 1\right) \]

      4. lower--.f64 98.7

         \[\leadsto \mathsf{fma}\left(x - 1, y, 1\right) \]

   5. Applied rewrites 98.7%

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

Alternative 13: 85.8% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 46000000000:\\ \;\;\;\;\mathsf{fma}\left(x, y, 1\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.0) x (if (<= y 46000000000.0) (fma x y 1.0) x)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = x;
	} else if (y <= 46000000000.0) {
		tmp = fma(x, y, 1.0);
	} else {
		tmp = x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.0)
		tmp = x;
	elseif (y <= 46000000000.0)
		tmp = fma(x, y, 1.0);
	else
		tmp = x;
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.0], x, If[LessEqual[y, 46000000000.0], N[(x * y + 1.0), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if y < -1 or 4.6e10 < y

   1. Initial program 31.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

   5. Applied rewrites 74.5%

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

   if -1 < y < 4.6e10

   1. Initial program 99.7%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

         \[\leadsto y \cdot \left(x - 1\right) + \color{blue}{1} \]

      2. *-commutative N/A

         \[\leadsto \left(x - 1\right) \cdot y + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x - 1, \color{blue}{y}, 1\right) \]

      4. lower--.f64 97.1

         \[\leadsto \mathsf{fma}\left(x - 1, y, 1\right) \]

   5. Applied rewrites 97.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - 1, y, 1\right)} \]

   6. Taylor expanded in x around inf

      \[\leadsto \mathsf{fma}\left(x, y, 1\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 96.7%

      \[\leadsto \mathsf{fma}\left(x, y, 1\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 74.7% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;\mathsf{fma}\left(-1, y, 1\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.0) x (if (<= y 1.0) (fma -1.0 y 1.0) x)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = x;
	} else if (y <= 1.0) {
		tmp = fma(-1.0, y, 1.0);
	} else {
		tmp = x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.0)
		tmp = x;
	elseif (y <= 1.0)
		tmp = fma(-1.0, y, 1.0);
	else
		tmp = x;
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.0], x, If[LessEqual[y, 1.0], N[(-1.0 * y + 1.0), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if y < -1 or 1 < y

   1. Initial program 32.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

   5. Applied rewrites 73.6%

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

   if -1 < y < 1

   1. Initial program 100.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

         \[\leadsto y \cdot \left(x - 1\right) + \color{blue}{1} \]

      2. *-commutative N/A

         \[\leadsto \left(x - 1\right) \cdot y + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x - 1, \color{blue}{y}, 1\right) \]

      4. lower--.f64 98.7

         \[\leadsto \mathsf{fma}\left(x - 1, y, 1\right) \]

   5. Applied rewrites 98.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - 1, y, 1\right)} \]

   6. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(-1, y, 1\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 75.8%

      \[\leadsto \mathsf{fma}\left(-1, y, 1\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 15: 74.3% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 46000000000:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.0) x (if (<= y 46000000000.0) 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = x;
	} else if (y <= 46000000000.0) {
		tmp = 1.0;
	} else {
		tmp = x;
	}
	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(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-1.0d0)) then
        tmp = x
    else if (y <= 46000000000.0d0) then
        tmp = 1.0d0
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -1.0) {
		tmp = x;
	} else if (y <= 46000000000.0) {
		tmp = 1.0;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.0:
		tmp = x
	elif y <= 46000000000.0:
		tmp = 1.0
	else:
		tmp = x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.0)
		tmp = x;
	elseif (y <= 46000000000.0)
		tmp = 1.0;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1.0)
		tmp = x;
	elseif (y <= 46000000000.0)
		tmp = 1.0;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.0], x, If[LessEqual[y, 46000000000.0], 1.0, x]]

Derivation

1. Split input into 2 regimes

2. if y < -1 or 4.6e10 < y

   1. Initial program 31.0%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

   5. Applied rewrites 74.5%

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

   if -1 < y < 4.6e10

   1. Initial program 99.7%

      \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 74.1%

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

Alternative 16: 39.4% accurate, 26.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 1.0)

C

double code(double x, double y) {
	return 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(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0
end function

Java

public static double code(double x, double y) {
	return 1.0;
}

Python

def code(x, y):
	return 1.0

Julia

function code(x, y)
	return 1.0
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0;
end

Wolfram

code[x_, y_] := 1.0

Derivation

1. Initial program 65.9%

   \[1 - \frac{\left(1 - x\right) \cdot y}{y + 1} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

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

5. Applied rewrites 39.4%

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

Developer Target 1: 99.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{y} - \left(\frac{x}{y} - x\right)\\ \mathbf{if}\;y < -3693.8482788297247:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y < 6799310503.41891:\\ \;\;\;\;1 - \frac{\left(1 - x\right) \cdot y}{y + 1}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- (/ 1.0 y) (- (/ x y) x))))
   (if (< y -3693.8482788297247)
     t_0
     (if (< y 6799310503.41891) (- 1.0 (/ (* (- 1.0 x) y) (+ y 1.0))) t_0))))

C

double code(double x, double y) {
	double t_0 = (1.0 / y) - ((x / y) - x);
	double tmp;
	if (y < -3693.8482788297247) {
		tmp = t_0;
	} else if (y < 6799310503.41891) {
		tmp = 1.0 - (((1.0 - x) * y) / (y + 1.0));
	} else {
		tmp = t_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(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (1.0d0 / y) - ((x / y) - x)
    if (y < (-3693.8482788297247d0)) then
        tmp = t_0
    else if (y < 6799310503.41891d0) then
        tmp = 1.0d0 - (((1.0d0 - x) * y) / (y + 1.0d0))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (1.0 / y) - ((x / y) - x);
	double tmp;
	if (y < -3693.8482788297247) {
		tmp = t_0;
	} else if (y < 6799310503.41891) {
		tmp = 1.0 - (((1.0 - x) * y) / (y + 1.0));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (1.0 / y) - ((x / y) - x)
	tmp = 0
	if y < -3693.8482788297247:
		tmp = t_0
	elif y < 6799310503.41891:
		tmp = 1.0 - (((1.0 - x) * y) / (y + 1.0))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(1.0 / y) - Float64(Float64(x / y) - x))
	tmp = 0.0
	if (y < -3693.8482788297247)
		tmp = t_0;
	elseif (y < 6799310503.41891)
		tmp = Float64(1.0 - Float64(Float64(Float64(1.0 - x) * y) / Float64(y + 1.0)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (1.0 / y) - ((x / y) - x);
	tmp = 0.0;
	if (y < -3693.8482788297247)
		tmp = t_0;
	elseif (y < 6799310503.41891)
		tmp = 1.0 - (((1.0 - x) * y) / (y + 1.0));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(1.0 / y), $MachinePrecision] - N[(N[(x / y), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]}, If[Less[y, -3693.8482788297247], t$95$0, If[Less[y, 6799310503.41891], N[(1.0 - N[(N[(N[(1.0 - x), $MachinePrecision] * y), $MachinePrecision] / N[(y + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Reproduce

herbie shell --seed 2025091 
(FPCore (x y)
  :name "Diagrams.Trail:splitAtParam  from diagrams-lib-1.3.0.3, D"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< y -36938482788297247/10000000000000) (- (/ 1 y) (- (/ x y) x)) (if (< y 679931050341891/100000) (- 1 (/ (* (- 1 x) y) (+ y 1))) (- (/ 1 y) (- (/ x y) x)))))

  (- 1.0 (/ (* (- 1.0 x) y) (+ y 1.0))))