_divideComplex, imaginary part

Percentage Accurate: 61.5% → 83.2%

Time: 1.8s

Alternatives: 10

Speedup: 1.5×

Specification

Math

\[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (/ (- (* x.im y.re) (* x.re y.im)) (+ (* y.re y.re) (* y.im y.im))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
}

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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    code = ((x_46im * y_46re) - (x_46re * y_46im)) / ((y_46re * y_46re) + (y_46im * y_46im))
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	return ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im))

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	return Float64(Float64(Float64(x_46_im * y_46_re) - Float64(x_46_re * y_46_im)) / Float64(Float64(y_46_re * y_46_re) + Float64(y_46_im * y_46_im)))
end

MATLAB

function tmp = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := N[(N[(N[(x$46$im * y$46$re), $MachinePrecision] - N[(x$46$re * y$46$im), $MachinePrecision]), $MachinePrecision] / N[(N[(y$46$re * y$46$re), $MachinePrecision] + N[(y$46$im * y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 61.5% accurate, 1.0× speedup

Math

\[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (/ (- (* x.im y.re) (* x.re y.im)) (+ (* y.re y.re) (* y.im y.im))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
}

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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    code = ((x_46im * y_46re) - (x_46re * y_46im)) / ((y_46re * y_46re) + (y_46im * y_46im))
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	return ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im))

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	return Float64(Float64(Float64(x_46_im * y_46_re) - Float64(x_46_re * y_46_im)) / Float64(Float64(y_46_re * y_46_re) + Float64(y_46_im * y_46_im)))
end

MATLAB

function tmp = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := N[(N[(N[(x$46$im * y$46$re), $MachinePrecision] - N[(x$46$re * y$46$im), $MachinePrecision]), $MachinePrecision] / N[(N[(y$46$re * y$46$re), $MachinePrecision] + N[(y$46$im * y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 83.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} t_0 := \frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im}\\ t_1 := \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im}\\ \mathbf{if}\;y.im \leq -1549999999999999942316721355256651167768960321267789649373183178612670464:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y.im \leq \frac{-8593944123082061}{226156424291633194186662080095093570025917938800079226639565593765455331328}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y.im \leq \frac{3630826122770869}{1037378892220248239628101965922790287753111558060609224998914332422663202853227036599926762236775948572049471652825197295598787768852943826971718708528490921765295450850377380921344}:\\ \;\;\;\;\frac{x.im - \frac{x.re}{\frac{y.re}{y.im}}}{y.re}\\ \mathbf{elif}\;y.im \leq 339999999999999980010654200175664637847404227877287010028207688682110504545216375346111184896:\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (let* ((t_0
        (/
         (- (* x.im y.re) (* x.re y.im))
         (+ (* y.re y.re) (* y.im y.im))))
       (t_1 (/ (- (- x.re) (* (- y.re) (/ x.im y.im))) y.im)))
  (if (<=
       y.im
       -1549999999999999942316721355256651167768960321267789649373183178612670464)
    t_1
    (if (<=
         y.im
         -8593944123082061/226156424291633194186662080095093570025917938800079226639565593765455331328)
      t_0
      (if (<=
           y.im
           3630826122770869/1037378892220248239628101965922790287753111558060609224998914332422663202853227036599926762236775948572049471652825197295598787768852943826971718708528490921765295450850377380921344)
        (/ (- x.im (/ x.re (/ y.re y.im))) y.re)
        (if (<=
             y.im
             339999999999999980010654200175664637847404227877287010028207688682110504545216375346111184896)
          t_0
          t_1))))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
	double t_1 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	double tmp;
	if (y_46_im <= -1.55e+72) {
		tmp = t_1;
	} else if (y_46_im <= -3.8e-59) {
		tmp = t_0;
	} else if (y_46_im <= 3.5e-165) {
		tmp = (x_46_im - (x_46_re / (y_46_re / y_46_im))) / y_46_re;
	} else if (y_46_im <= 3.4e+92) {
		tmp = 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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = ((x_46im * y_46re) - (x_46re * y_46im)) / ((y_46re * y_46re) + (y_46im * y_46im))
    t_1 = (-x_46re - (-y_46re * (x_46im / y_46im))) / y_46im
    if (y_46im <= (-1.55d+72)) then
        tmp = t_1
    else if (y_46im <= (-3.8d-59)) then
        tmp = t_0
    else if (y_46im <= 3.5d-165) then
        tmp = (x_46im - (x_46re / (y_46re / y_46im))) / y_46re
    else if (y_46im <= 3.4d+92) then
        tmp = t_0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
	double t_1 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	double tmp;
	if (y_46_im <= -1.55e+72) {
		tmp = t_1;
	} else if (y_46_im <= -3.8e-59) {
		tmp = t_0;
	} else if (y_46_im <= 3.5e-165) {
		tmp = (x_46_im - (x_46_re / (y_46_re / y_46_im))) / y_46_re;
	} else if (y_46_im <= 3.4e+92) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	t_0 = ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im))
	t_1 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im
	tmp = 0
	if y_46_im <= -1.55e+72:
		tmp = t_1
	elif y_46_im <= -3.8e-59:
		tmp = t_0
	elif y_46_im <= 3.5e-165:
		tmp = (x_46_im - (x_46_re / (y_46_re / y_46_im))) / y_46_re
	elif y_46_im <= 3.4e+92:
		tmp = t_0
	else:
		tmp = t_1
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = Float64(Float64(Float64(x_46_im * y_46_re) - Float64(x_46_re * y_46_im)) / Float64(Float64(y_46_re * y_46_re) + Float64(y_46_im * y_46_im)))
	t_1 = Float64(Float64(Float64(-x_46_re) - Float64(Float64(-y_46_re) * Float64(x_46_im / y_46_im))) / y_46_im)
	tmp = 0.0
	if (y_46_im <= -1.55e+72)
		tmp = t_1;
	elseif (y_46_im <= -3.8e-59)
		tmp = t_0;
	elseif (y_46_im <= 3.5e-165)
		tmp = Float64(Float64(x_46_im - Float64(x_46_re / Float64(y_46_re / y_46_im))) / y_46_re);
	elseif (y_46_im <= 3.4e+92)
		tmp = t_0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = ((x_46_im * y_46_re) - (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
	t_1 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	tmp = 0.0;
	if (y_46_im <= -1.55e+72)
		tmp = t_1;
	elseif (y_46_im <= -3.8e-59)
		tmp = t_0;
	elseif (y_46_im <= 3.5e-165)
		tmp = (x_46_im - (x_46_re / (y_46_re / y_46_im))) / y_46_re;
	elseif (y_46_im <= 3.4e+92)
		tmp = t_0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := Block[{t$95$0 = N[(N[(N[(x$46$im * y$46$re), $MachinePrecision] - N[(x$46$re * y$46$im), $MachinePrecision]), $MachinePrecision] / N[(N[(y$46$re * y$46$re), $MachinePrecision] + N[(y$46$im * y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[((-x$46$re) - N[((-y$46$re) * N[(x$46$im / y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / y$46$im), $MachinePrecision]}, If[LessEqual[y$46$im, -1549999999999999942316721355256651167768960321267789649373183178612670464], t$95$1, If[LessEqual[y$46$im, -8593944123082061/226156424291633194186662080095093570025917938800079226639565593765455331328], t$95$0, If[LessEqual[y$46$im, 3630826122770869/1037378892220248239628101965922790287753111558060609224998914332422663202853227036599926762236775948572049471652825197295598787768852943826971718708528490921765295450850377380921344], N[(N[(x$46$im - N[(x$46$re / N[(y$46$re / y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / y$46$re), $MachinePrecision], If[LessEqual[y$46$im, 339999999999999980010654200175664637847404227877287010028207688682110504545216375346111184896], t$95$0, t$95$1]]]]]]

Derivation

1. Split input into 3 regimes

2. if y.im < -1.5499999999999999e72 or 3.3999999999999998e92 < y.im

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]
   3. Step-by-step derivation

      1. lower-/.f64 43.0%

         \[\leadsto \frac{x.im}{\color{blue}{y.re}} \]

   4. Applied rewrites 43.0%

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]

   5. Taylor expanded in y.im around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{-1 \cdot x.re + \frac{x.im \cdot y.re}{y.im}}{\color{blue}{y.im}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.4%

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

   7. Applied rewrites 52.4%

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

      1. lift-+.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. associate-/l* N/A

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

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

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

      7. lower--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. mul-1-neg N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(x.re\right)\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      10. lift-neg.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      11. lower-*.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      12. lower-neg.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      13. lower-/.f64 53.6%

          \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

   9. Applied rewrites 53.6%

      \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

   if -1.5499999999999999e72 < y.im < -3.7999999999999998e-59 or 3.5000000000000002e-165 < y.im < 3.3999999999999998e92

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   if -3.7999999999999998e-59 < y.im < 3.5000000000000002e-165

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{x.im + -1 \cdot \frac{x.re \cdot y.im}{y.re}}{\color{blue}{y.re}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.8%

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

   4. Applied rewrites 52.8%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. lower--.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. mul-1-neg N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      6. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      7. distribute-neg-frac N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{y.re}\right)\right)}{y.re} \]

      8. distribute-frac-neg2 N/A

         \[\leadsto \frac{x.im - \frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{\mathsf{neg}\left(y.re\right)}}{y.re} \]

      9. frac-2neg N/A

         \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      10. lift-/.f64 52.8%

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      11. lift-*.f64 N/A

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      12. *-commutative N/A

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      13. lift-*.f64 52.8%

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

   6. Applied rewrites 52.8%

      \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      3. associate-*l/ N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      5. lower-*.f64 54.5%

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

   8. Applied rewrites 54.5%

      \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      3. div-flip-rev N/A

         \[\leadsto \frac{x.im - \frac{1}{\frac{y.re}{y.im}} \cdot x.re}{y.re} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{1}{\frac{y.re}{y.im}} \cdot x.re}{y.re} \]

      5. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{1}{\frac{y.re}{y.im}} \cdot x.re}{y.re} \]

      6. *-commutative N/A

         \[\leadsto \frac{x.im - x.re \cdot \frac{1}{\frac{y.re}{y.im}}}{y.re} \]

      7. lift-/.f64 N/A

         \[\leadsto \frac{x.im - x.re \cdot \frac{1}{\frac{y.re}{y.im}}}{y.re} \]

      8. mult-flip-rev N/A

         \[\leadsto \frac{x.im - \frac{x.re}{\frac{y.re}{y.im}}}{y.re} \]

      9. lower-/.f64 54.5%

         \[\leadsto \frac{x.im - \frac{x.re}{\frac{y.re}{y.im}}}{y.re} \]

   10. Applied rewrites 54.5%

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

Alternative 2: 78.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_0 := \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im}\\ \mathbf{if}\;y.im \leq -2100000000000000136991060992552505181349115988754604270400850104669962240:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y.im \leq \frac{-498150793720287}{3213876088517980551083924184682325205044405987565585670602752}:\\ \;\;\;\;\frac{-1 \cdot \left(x.re \cdot y.im\right)}{y.re \cdot y.re + y.im \cdot y.im}\\ \mathbf{elif}\;y.im \leq 7999999999999999517673330972825428688896:\\ \;\;\;\;\frac{x.im - \frac{x.re}{\frac{y.re}{y.im}}}{y.re}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (let* ((t_0 (/ (- (- x.re) (* (- y.re) (/ x.im y.im))) y.im)))
  (if (<=
       y.im
       -2100000000000000136991060992552505181349115988754604270400850104669962240)
    t_0
    (if (<=
         y.im
         -498150793720287/3213876088517980551083924184682325205044405987565585670602752)
      (/ (* -1 (* x.re y.im)) (+ (* y.re y.re) (* y.im y.im)))
      (if (<= y.im 7999999999999999517673330972825428688896)
        (/ (- x.im (/ x.re (/ y.re y.im))) y.re)
        t_0)))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	double tmp;
	if (y_46_im <= -2.1e+72) {
		tmp = t_0;
	} else if (y_46_im <= -1.55e-46) {
		tmp = (-1.0 * (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
	} else if (y_46_im <= 8e+39) {
		tmp = (x_46_im - (x_46_re / (y_46_re / y_46_im))) / y_46_re;
	} 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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (-x_46re - (-y_46re * (x_46im / y_46im))) / y_46im
    if (y_46im <= (-2.1d+72)) then
        tmp = t_0
    else if (y_46im <= (-1.55d-46)) then
        tmp = ((-1.0d0) * (x_46re * y_46im)) / ((y_46re * y_46re) + (y_46im * y_46im))
    else if (y_46im <= 8d+39) then
        tmp = (x_46im - (x_46re / (y_46re / y_46im))) / y_46re
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	double tmp;
	if (y_46_im <= -2.1e+72) {
		tmp = t_0;
	} else if (y_46_im <= -1.55e-46) {
		tmp = (-1.0 * (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
	} else if (y_46_im <= 8e+39) {
		tmp = (x_46_im - (x_46_re / (y_46_re / y_46_im))) / y_46_re;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im
	tmp = 0
	if y_46_im <= -2.1e+72:
		tmp = t_0
	elif y_46_im <= -1.55e-46:
		tmp = (-1.0 * (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im))
	elif y_46_im <= 8e+39:
		tmp = (x_46_im - (x_46_re / (y_46_re / y_46_im))) / y_46_re
	else:
		tmp = t_0
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = Float64(Float64(Float64(-x_46_re) - Float64(Float64(-y_46_re) * Float64(x_46_im / y_46_im))) / y_46_im)
	tmp = 0.0
	if (y_46_im <= -2.1e+72)
		tmp = t_0;
	elseif (y_46_im <= -1.55e-46)
		tmp = Float64(Float64(-1.0 * Float64(x_46_re * y_46_im)) / Float64(Float64(y_46_re * y_46_re) + Float64(y_46_im * y_46_im)));
	elseif (y_46_im <= 8e+39)
		tmp = Float64(Float64(x_46_im - Float64(x_46_re / Float64(y_46_re / y_46_im))) / y_46_re);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	tmp = 0.0;
	if (y_46_im <= -2.1e+72)
		tmp = t_0;
	elseif (y_46_im <= -1.55e-46)
		tmp = (-1.0 * (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
	elseif (y_46_im <= 8e+39)
		tmp = (x_46_im - (x_46_re / (y_46_re / y_46_im))) / y_46_re;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := Block[{t$95$0 = N[(N[((-x$46$re) - N[((-y$46$re) * N[(x$46$im / y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / y$46$im), $MachinePrecision]}, If[LessEqual[y$46$im, -2100000000000000136991060992552505181349115988754604270400850104669962240], t$95$0, If[LessEqual[y$46$im, -498150793720287/3213876088517980551083924184682325205044405987565585670602752], N[(N[(-1 * N[(x$46$re * y$46$im), $MachinePrecision]), $MachinePrecision] / N[(N[(y$46$re * y$46$re), $MachinePrecision] + N[(y$46$im * y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y$46$im, 7999999999999999517673330972825428688896], N[(N[(x$46$im - N[(x$46$re / N[(y$46$re / y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / y$46$re), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 3 regimes

2. if y.im < -2.1000000000000001e72 or 7.9999999999999995e39 < y.im

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]
   3. Step-by-step derivation

      1. lower-/.f64 43.0%

         \[\leadsto \frac{x.im}{\color{blue}{y.re}} \]

   4. Applied rewrites 43.0%

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]

   5. Taylor expanded in y.im around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{-1 \cdot x.re + \frac{x.im \cdot y.re}{y.im}}{\color{blue}{y.im}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.4%

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

   7. Applied rewrites 52.4%

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

      1. lift-+.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. associate-/l* N/A

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

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

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

      7. lower--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. mul-1-neg N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(x.re\right)\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      10. lift-neg.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      11. lower-*.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      12. lower-neg.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      13. lower-/.f64 53.6%

          \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

   9. Applied rewrites 53.6%

      \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

   if -2.1000000000000001e72 < y.im < -1.55e-46

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in x.re around inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 39.6%

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

   4. Applied rewrites 39.6%

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

   if -1.55e-46 < y.im < 7.9999999999999995e39

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{x.im + -1 \cdot \frac{x.re \cdot y.im}{y.re}}{\color{blue}{y.re}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.8%

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

   4. Applied rewrites 52.8%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. lower--.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. mul-1-neg N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      6. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      7. distribute-neg-frac N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{y.re}\right)\right)}{y.re} \]

      8. distribute-frac-neg2 N/A

         \[\leadsto \frac{x.im - \frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{\mathsf{neg}\left(y.re\right)}}{y.re} \]

      9. frac-2neg N/A

         \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      10. lift-/.f64 52.8%

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      11. lift-*.f64 N/A

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      12. *-commutative N/A

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      13. lift-*.f64 52.8%

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

   6. Applied rewrites 52.8%

      \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      3. associate-*l/ N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      5. lower-*.f64 54.5%

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

   8. Applied rewrites 54.5%

      \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      3. div-flip-rev N/A

         \[\leadsto \frac{x.im - \frac{1}{\frac{y.re}{y.im}} \cdot x.re}{y.re} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{1}{\frac{y.re}{y.im}} \cdot x.re}{y.re} \]

      5. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{1}{\frac{y.re}{y.im}} \cdot x.re}{y.re} \]

      6. *-commutative N/A

         \[\leadsto \frac{x.im - x.re \cdot \frac{1}{\frac{y.re}{y.im}}}{y.re} \]

      7. lift-/.f64 N/A

         \[\leadsto \frac{x.im - x.re \cdot \frac{1}{\frac{y.re}{y.im}}}{y.re} \]

      8. mult-flip-rev N/A

         \[\leadsto \frac{x.im - \frac{x.re}{\frac{y.re}{y.im}}}{y.re} \]

      9. lower-/.f64 54.5%

         \[\leadsto \frac{x.im - \frac{x.re}{\frac{y.re}{y.im}}}{y.re} \]

   10. Applied rewrites 54.5%

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

Alternative 3: 78.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_0 := \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im}\\ \mathbf{if}\;y.im \leq -2100000000000000136991060992552505181349115988754604270400850104669962240:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y.im \leq \frac{-498150793720287}{3213876088517980551083924184682325205044405987565585670602752}:\\ \;\;\;\;\frac{-1 \cdot \left(x.re \cdot y.im\right)}{y.re \cdot y.re + y.im \cdot y.im}\\ \mathbf{elif}\;y.im \leq 7999999999999999517673330972825428688896:\\ \;\;\;\;\frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (let* ((t_0 (/ (- (- x.re) (* (- y.re) (/ x.im y.im))) y.im)))
  (if (<=
       y.im
       -2100000000000000136991060992552505181349115988754604270400850104669962240)
    t_0
    (if (<=
         y.im
         -498150793720287/3213876088517980551083924184682325205044405987565585670602752)
      (/ (* -1 (* x.re y.im)) (+ (* y.re y.re) (* y.im y.im)))
      (if (<= y.im 7999999999999999517673330972825428688896)
        (/ (- x.im (* (/ y.im y.re) x.re)) y.re)
        t_0)))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	double tmp;
	if (y_46_im <= -2.1e+72) {
		tmp = t_0;
	} else if (y_46_im <= -1.55e-46) {
		tmp = (-1.0 * (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
	} else if (y_46_im <= 8e+39) {
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re;
	} 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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (-x_46re - (-y_46re * (x_46im / y_46im))) / y_46im
    if (y_46im <= (-2.1d+72)) then
        tmp = t_0
    else if (y_46im <= (-1.55d-46)) then
        tmp = ((-1.0d0) * (x_46re * y_46im)) / ((y_46re * y_46re) + (y_46im * y_46im))
    else if (y_46im <= 8d+39) then
        tmp = (x_46im - ((y_46im / y_46re) * x_46re)) / y_46re
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	double tmp;
	if (y_46_im <= -2.1e+72) {
		tmp = t_0;
	} else if (y_46_im <= -1.55e-46) {
		tmp = (-1.0 * (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
	} else if (y_46_im <= 8e+39) {
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im
	tmp = 0
	if y_46_im <= -2.1e+72:
		tmp = t_0
	elif y_46_im <= -1.55e-46:
		tmp = (-1.0 * (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im))
	elif y_46_im <= 8e+39:
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re
	else:
		tmp = t_0
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = Float64(Float64(Float64(-x_46_re) - Float64(Float64(-y_46_re) * Float64(x_46_im / y_46_im))) / y_46_im)
	tmp = 0.0
	if (y_46_im <= -2.1e+72)
		tmp = t_0;
	elseif (y_46_im <= -1.55e-46)
		tmp = Float64(Float64(-1.0 * Float64(x_46_re * y_46_im)) / Float64(Float64(y_46_re * y_46_re) + Float64(y_46_im * y_46_im)));
	elseif (y_46_im <= 8e+39)
		tmp = Float64(Float64(x_46_im - Float64(Float64(y_46_im / y_46_re) * x_46_re)) / y_46_re);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	tmp = 0.0;
	if (y_46_im <= -2.1e+72)
		tmp = t_0;
	elseif (y_46_im <= -1.55e-46)
		tmp = (-1.0 * (x_46_re * y_46_im)) / ((y_46_re * y_46_re) + (y_46_im * y_46_im));
	elseif (y_46_im <= 8e+39)
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := Block[{t$95$0 = N[(N[((-x$46$re) - N[((-y$46$re) * N[(x$46$im / y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / y$46$im), $MachinePrecision]}, If[LessEqual[y$46$im, -2100000000000000136991060992552505181349115988754604270400850104669962240], t$95$0, If[LessEqual[y$46$im, -498150793720287/3213876088517980551083924184682325205044405987565585670602752], N[(N[(-1 * N[(x$46$re * y$46$im), $MachinePrecision]), $MachinePrecision] / N[(N[(y$46$re * y$46$re), $MachinePrecision] + N[(y$46$im * y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y$46$im, 7999999999999999517673330972825428688896], N[(N[(x$46$im - N[(N[(y$46$im / y$46$re), $MachinePrecision] * x$46$re), $MachinePrecision]), $MachinePrecision] / y$46$re), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 3 regimes

2. if y.im < -2.1000000000000001e72 or 7.9999999999999995e39 < y.im

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]
   3. Step-by-step derivation

      1. lower-/.f64 43.0%

         \[\leadsto \frac{x.im}{\color{blue}{y.re}} \]

   4. Applied rewrites 43.0%

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]

   5. Taylor expanded in y.im around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{-1 \cdot x.re + \frac{x.im \cdot y.re}{y.im}}{\color{blue}{y.im}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.4%

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

   7. Applied rewrites 52.4%

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

      1. lift-+.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. associate-/l* N/A

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

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

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

      7. lower--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. mul-1-neg N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(x.re\right)\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      10. lift-neg.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      11. lower-*.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      12. lower-neg.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      13. lower-/.f64 53.6%

          \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

   9. Applied rewrites 53.6%

      \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

   if -2.1000000000000001e72 < y.im < -1.55e-46

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in x.re around inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 39.6%

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

   4. Applied rewrites 39.6%

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

   if -1.55e-46 < y.im < 7.9999999999999995e39

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{x.im + -1 \cdot \frac{x.re \cdot y.im}{y.re}}{\color{blue}{y.re}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.8%

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

   4. Applied rewrites 52.8%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. lower--.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. mul-1-neg N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      6. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      7. distribute-neg-frac N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{y.re}\right)\right)}{y.re} \]

      8. distribute-frac-neg2 N/A

         \[\leadsto \frac{x.im - \frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{\mathsf{neg}\left(y.re\right)}}{y.re} \]

      9. frac-2neg N/A

         \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      10. lift-/.f64 52.8%

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      11. lift-*.f64 N/A

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      12. *-commutative N/A

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      13. lift-*.f64 52.8%

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

   6. Applied rewrites 52.8%

      \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      3. associate-*l/ N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      5. lower-*.f64 54.5%

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

   8. Applied rewrites 54.5%

      \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 78.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} t_0 := \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im}\\ \mathbf{if}\;y.im \leq -195000000000000005714904619635812128629974360554371271422985808308600832:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y.im \leq 7999999999999999517673330972825428688896:\\ \;\;\;\;\frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (let* ((t_0 (/ (- (- x.re) (* (- y.re) (/ x.im y.im))) y.im)))
  (if (<=
       y.im
       -195000000000000005714904619635812128629974360554371271422985808308600832)
    t_0
    (if (<= y.im 7999999999999999517673330972825428688896)
      (/ (- x.im (* (/ y.im y.re) x.re)) y.re)
      t_0))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	double tmp;
	if (y_46_im <= -1.95e+71) {
		tmp = t_0;
	} else if (y_46_im <= 8e+39) {
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re;
	} 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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (-x_46re - (-y_46re * (x_46im / y_46im))) / y_46im
    if (y_46im <= (-1.95d+71)) then
        tmp = t_0
    else if (y_46im <= 8d+39) then
        tmp = (x_46im - ((y_46im / y_46re) * x_46re)) / y_46re
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	double tmp;
	if (y_46_im <= -1.95e+71) {
		tmp = t_0;
	} else if (y_46_im <= 8e+39) {
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im
	tmp = 0
	if y_46_im <= -1.95e+71:
		tmp = t_0
	elif y_46_im <= 8e+39:
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re
	else:
		tmp = t_0
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = Float64(Float64(Float64(-x_46_re) - Float64(Float64(-y_46_re) * Float64(x_46_im / y_46_im))) / y_46_im)
	tmp = 0.0
	if (y_46_im <= -1.95e+71)
		tmp = t_0;
	elseif (y_46_im <= 8e+39)
		tmp = Float64(Float64(x_46_im - Float64(Float64(y_46_im / y_46_re) * x_46_re)) / y_46_re);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = (-x_46_re - (-y_46_re * (x_46_im / y_46_im))) / y_46_im;
	tmp = 0.0;
	if (y_46_im <= -1.95e+71)
		tmp = t_0;
	elseif (y_46_im <= 8e+39)
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := Block[{t$95$0 = N[(N[((-x$46$re) - N[((-y$46$re) * N[(x$46$im / y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / y$46$im), $MachinePrecision]}, If[LessEqual[y$46$im, -195000000000000005714904619635812128629974360554371271422985808308600832], t$95$0, If[LessEqual[y$46$im, 7999999999999999517673330972825428688896], N[(N[(x$46$im - N[(N[(y$46$im / y$46$re), $MachinePrecision] * x$46$re), $MachinePrecision]), $MachinePrecision] / y$46$re), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y.im < -1.9500000000000001e71 or 7.9999999999999995e39 < y.im

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]
   3. Step-by-step derivation

      1. lower-/.f64 43.0%

         \[\leadsto \frac{x.im}{\color{blue}{y.re}} \]

   4. Applied rewrites 43.0%

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]

   5. Taylor expanded in y.im around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{-1 \cdot x.re + \frac{x.im \cdot y.re}{y.im}}{\color{blue}{y.im}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.4%

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

   7. Applied rewrites 52.4%

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

      1. lift-+.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. associate-/l* N/A

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

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

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

      7. lower--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. mul-1-neg N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(x.re\right)\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      10. lift-neg.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      11. lower-*.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(\mathsf{neg}\left(y.re\right)\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      12. lower-neg.f64 N/A

          \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

      13. lower-/.f64 53.6%

          \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

   9. Applied rewrites 53.6%

      \[\leadsto \frac{\left(-x.re\right) - \left(-y.re\right) \cdot \frac{x.im}{y.im}}{y.im} \]

   if -1.9500000000000001e71 < y.im < 7.9999999999999995e39

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{x.im + -1 \cdot \frac{x.re \cdot y.im}{y.re}}{\color{blue}{y.re}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.8%

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

   4. Applied rewrites 52.8%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. lower--.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. mul-1-neg N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      6. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      7. distribute-neg-frac N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{y.re}\right)\right)}{y.re} \]

      8. distribute-frac-neg2 N/A

         \[\leadsto \frac{x.im - \frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{\mathsf{neg}\left(y.re\right)}}{y.re} \]

      9. frac-2neg N/A

         \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      10. lift-/.f64 52.8%

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      11. lift-*.f64 N/A

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      12. *-commutative N/A

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      13. lift-*.f64 52.8%

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

   6. Applied rewrites 52.8%

      \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      3. associate-*l/ N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      5. lower-*.f64 54.5%

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

   8. Applied rewrites 54.5%

      \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 78.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} t_0 := \frac{x.im - \frac{x.re}{y.re} \cdot y.im}{y.re}\\ \mathbf{if}\;y.re \leq \frac{-6941760285187145}{2722258935367507707706996859454145691648}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y.re \leq 1900000000000000011885089336066048:\\ \;\;\;\;\frac{\frac{y.re \cdot x.im}{y.im} - x.re}{y.im}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (let* ((t_0 (/ (- x.im (* (/ x.re y.re) y.im)) y.re)))
  (if (<=
       y.re
       -6941760285187145/2722258935367507707706996859454145691648)
    t_0
    (if (<= y.re 1900000000000000011885089336066048)
      (/ (- (/ (* y.re x.im) y.im) x.re) y.im)
      t_0))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = (x_46_im - ((x_46_re / y_46_re) * y_46_im)) / y_46_re;
	double tmp;
	if (y_46_re <= -2.55e-24) {
		tmp = t_0;
	} else if (y_46_re <= 1.9e+33) {
		tmp = (((y_46_re * x_46_im) / y_46_im) - x_46_re) / y_46_im;
	} 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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x_46im - ((x_46re / y_46re) * y_46im)) / y_46re
    if (y_46re <= (-2.55d-24)) then
        tmp = t_0
    else if (y_46re <= 1.9d+33) then
        tmp = (((y_46re * x_46im) / y_46im) - x_46re) / y_46im
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = (x_46_im - ((x_46_re / y_46_re) * y_46_im)) / y_46_re;
	double tmp;
	if (y_46_re <= -2.55e-24) {
		tmp = t_0;
	} else if (y_46_re <= 1.9e+33) {
		tmp = (((y_46_re * x_46_im) / y_46_im) - x_46_re) / y_46_im;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	t_0 = (x_46_im - ((x_46_re / y_46_re) * y_46_im)) / y_46_re
	tmp = 0
	if y_46_re <= -2.55e-24:
		tmp = t_0
	elif y_46_re <= 1.9e+33:
		tmp = (((y_46_re * x_46_im) / y_46_im) - x_46_re) / y_46_im
	else:
		tmp = t_0
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = Float64(Float64(x_46_im - Float64(Float64(x_46_re / y_46_re) * y_46_im)) / y_46_re)
	tmp = 0.0
	if (y_46_re <= -2.55e-24)
		tmp = t_0;
	elseif (y_46_re <= 1.9e+33)
		tmp = Float64(Float64(Float64(Float64(y_46_re * x_46_im) / y_46_im) - x_46_re) / y_46_im);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = (x_46_im - ((x_46_re / y_46_re) * y_46_im)) / y_46_re;
	tmp = 0.0;
	if (y_46_re <= -2.55e-24)
		tmp = t_0;
	elseif (y_46_re <= 1.9e+33)
		tmp = (((y_46_re * x_46_im) / y_46_im) - x_46_re) / y_46_im;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := Block[{t$95$0 = N[(N[(x$46$im - N[(N[(x$46$re / y$46$re), $MachinePrecision] * y$46$im), $MachinePrecision]), $MachinePrecision] / y$46$re), $MachinePrecision]}, If[LessEqual[y$46$re, -6941760285187145/2722258935367507707706996859454145691648], t$95$0, If[LessEqual[y$46$re, 1900000000000000011885089336066048], N[(N[(N[(N[(y$46$re * x$46$im), $MachinePrecision] / y$46$im), $MachinePrecision] - x$46$re), $MachinePrecision] / y$46$im), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y.re < -2.5500000000000001e-24 or 1.9e33 < y.re

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{x.im + -1 \cdot \frac{x.re \cdot y.im}{y.re}}{\color{blue}{y.re}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.8%

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

   4. Applied rewrites 52.8%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. lower--.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. mul-1-neg N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      6. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      7. distribute-neg-frac N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{y.re}\right)\right)}{y.re} \]

      8. distribute-frac-neg2 N/A

         \[\leadsto \frac{x.im - \frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{\mathsf{neg}\left(y.re\right)}}{y.re} \]

      9. frac-2neg N/A

         \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      10. lift-/.f64 52.8%

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      11. lift-*.f64 N/A

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      12. *-commutative N/A

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      13. lift-*.f64 52.8%

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

   6. Applied rewrites 52.8%

      \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      3. associate-/l* N/A

         \[\leadsto \frac{x.im - y.im \cdot \frac{x.re}{y.re}}{y.re} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{x.im - y.im \cdot \frac{x.re}{y.re}}{y.re} \]

      5. *-commutative N/A

         \[\leadsto \frac{x.im - \frac{x.re}{y.re} \cdot y.im}{y.re} \]

      6. lower-*.f64 54.0%

         \[\leadsto \frac{x.im - \frac{x.re}{y.re} \cdot y.im}{y.re} \]

   8. Applied rewrites 54.0%

      \[\leadsto \frac{x.im - \frac{x.re}{y.re} \cdot y.im}{y.re} \]

   if -2.5500000000000001e-24 < y.re < 1.9e33

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]
   3. Step-by-step derivation

      1. lower-/.f64 43.0%

         \[\leadsto \frac{x.im}{\color{blue}{y.re}} \]

   4. Applied rewrites 43.0%

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]

   5. Taylor expanded in y.im around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{-1 \cdot x.re + \frac{x.im \cdot y.re}{y.im}}{\color{blue}{y.im}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.4%

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

   7. Applied rewrites 52.4%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. mul-1-neg N/A

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

      5. sub-flip-reverse N/A

         \[\leadsto \frac{\frac{x.im \cdot y.re}{y.im} - x.re}{y.im} \]

      6. lower--.f64 52.4%

         \[\leadsto \frac{\frac{x.im \cdot y.re}{y.im} - x.re}{y.im} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{\frac{x.im \cdot y.re}{y.im} - x.re}{y.im} \]

      8. *-commutative N/A

         \[\leadsto \frac{\frac{y.re \cdot x.im}{y.im} - x.re}{y.im} \]

      9. lower-*.f64 52.4%

         \[\leadsto \frac{\frac{y.re \cdot x.im}{y.im} - x.re}{y.im} \]

   9. Applied rewrites 52.4%

      \[\leadsto \frac{\frac{y.re \cdot x.im}{y.im} - x.re}{y.im} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 73.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} t_0 := \frac{-x.re}{y.im}\\ \mathbf{if}\;y.im \leq -195000000000000005714904619635812128629974360554371271422985808308600832:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y.im \leq 39999999999999998543585179860992:\\ \;\;\;\;\frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (let* ((t_0 (/ (- x.re) y.im)))
  (if (<=
       y.im
       -195000000000000005714904619635812128629974360554371271422985808308600832)
    t_0
    (if (<= y.im 39999999999999998543585179860992)
      (/ (- x.im (* (/ y.im y.re) x.re)) y.re)
      t_0))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = -x_46_re / y_46_im;
	double tmp;
	if (y_46_im <= -1.95e+71) {
		tmp = t_0;
	} else if (y_46_im <= 4e+31) {
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re;
	} 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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    real(8) :: t_0
    real(8) :: tmp
    t_0 = -x_46re / y_46im
    if (y_46im <= (-1.95d+71)) then
        tmp = t_0
    else if (y_46im <= 4d+31) then
        tmp = (x_46im - ((y_46im / y_46re) * x_46re)) / y_46re
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = -x_46_re / y_46_im;
	double tmp;
	if (y_46_im <= -1.95e+71) {
		tmp = t_0;
	} else if (y_46_im <= 4e+31) {
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	t_0 = -x_46_re / y_46_im
	tmp = 0
	if y_46_im <= -1.95e+71:
		tmp = t_0
	elif y_46_im <= 4e+31:
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re
	else:
		tmp = t_0
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = Float64(Float64(-x_46_re) / y_46_im)
	tmp = 0.0
	if (y_46_im <= -1.95e+71)
		tmp = t_0;
	elseif (y_46_im <= 4e+31)
		tmp = Float64(Float64(x_46_im - Float64(Float64(y_46_im / y_46_re) * x_46_re)) / y_46_re);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = -x_46_re / y_46_im;
	tmp = 0.0;
	if (y_46_im <= -1.95e+71)
		tmp = t_0;
	elseif (y_46_im <= 4e+31)
		tmp = (x_46_im - ((y_46_im / y_46_re) * x_46_re)) / y_46_re;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := Block[{t$95$0 = N[((-x$46$re) / y$46$im), $MachinePrecision]}, If[LessEqual[y$46$im, -195000000000000005714904619635812128629974360554371271422985808308600832], t$95$0, If[LessEqual[y$46$im, 39999999999999998543585179860992], N[(N[(x$46$im - N[(N[(y$46$im / y$46$re), $MachinePrecision] * x$46$re), $MachinePrecision]), $MachinePrecision] / y$46$re), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y.im < -1.9500000000000001e71 or 3.9999999999999999e31 < y.im

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around 0

      \[\leadsto \color{blue}{-1 \cdot \frac{x.re}{y.im}} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

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

      2. lower-/.f64 42.7%

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

   4. Applied rewrites 42.7%

      \[\leadsto \color{blue}{-1 \cdot \frac{x.re}{y.im}} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-*r/ N/A

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

      4. lift-*.f64 N/A

         \[\leadsto \frac{-1 \cdot x.re}{y.im} \]

      5. lower-/.f64 42.7%

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

      6. lift-*.f64 N/A

         \[\leadsto \frac{-1 \cdot x.re}{y.im} \]

      7. mul-1-neg N/A

         \[\leadsto \frac{\mathsf{neg}\left(x.re\right)}{y.im} \]

      8. lift-neg.f64 42.7%

         \[\leadsto \frac{-x.re}{y.im} \]

   6. Applied rewrites 42.7%

      \[\leadsto \frac{-x.re}{\color{blue}{y.im}} \]

   if -1.9500000000000001e71 < y.im < 3.9999999999999999e31

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{x.im + -1 \cdot \frac{x.re \cdot y.im}{y.re}}{\color{blue}{y.re}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.8%

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

   4. Applied rewrites 52.8%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. lower--.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. mul-1-neg N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      6. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      7. distribute-neg-frac N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{y.re}\right)\right)}{y.re} \]

      8. distribute-frac-neg2 N/A

         \[\leadsto \frac{x.im - \frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{\mathsf{neg}\left(y.re\right)}}{y.re} \]

      9. frac-2neg N/A

         \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      10. lift-/.f64 52.8%

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      11. lift-*.f64 N/A

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      12. *-commutative N/A

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      13. lift-*.f64 52.8%

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

   6. Applied rewrites 52.8%

      \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      3. associate-*l/ N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

      5. lower-*.f64 54.5%

         \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]

   8. Applied rewrites 54.5%

      \[\leadsto \frac{x.im - \frac{y.im}{y.re} \cdot x.re}{y.re} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 73.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} t_0 := \frac{-x.re}{y.im}\\ \mathbf{if}\;y.im \leq -195000000000000005714904619635812128629974360554371271422985808308600832:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y.im \leq 39999999999999998543585179860992:\\ \;\;\;\;\frac{x.im - \frac{x.re}{y.re} \cdot y.im}{y.re}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (let* ((t_0 (/ (- x.re) y.im)))
  (if (<=
       y.im
       -195000000000000005714904619635812128629974360554371271422985808308600832)
    t_0
    (if (<= y.im 39999999999999998543585179860992)
      (/ (- x.im (* (/ x.re y.re) y.im)) y.re)
      t_0))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = -x_46_re / y_46_im;
	double tmp;
	if (y_46_im <= -1.95e+71) {
		tmp = t_0;
	} else if (y_46_im <= 4e+31) {
		tmp = (x_46_im - ((x_46_re / y_46_re) * y_46_im)) / y_46_re;
	} 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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    real(8) :: t_0
    real(8) :: tmp
    t_0 = -x_46re / y_46im
    if (y_46im <= (-1.95d+71)) then
        tmp = t_0
    else if (y_46im <= 4d+31) then
        tmp = (x_46im - ((x_46re / y_46re) * y_46im)) / y_46re
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = -x_46_re / y_46_im;
	double tmp;
	if (y_46_im <= -1.95e+71) {
		tmp = t_0;
	} else if (y_46_im <= 4e+31) {
		tmp = (x_46_im - ((x_46_re / y_46_re) * y_46_im)) / y_46_re;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	t_0 = -x_46_re / y_46_im
	tmp = 0
	if y_46_im <= -1.95e+71:
		tmp = t_0
	elif y_46_im <= 4e+31:
		tmp = (x_46_im - ((x_46_re / y_46_re) * y_46_im)) / y_46_re
	else:
		tmp = t_0
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = Float64(Float64(-x_46_re) / y_46_im)
	tmp = 0.0
	if (y_46_im <= -1.95e+71)
		tmp = t_0;
	elseif (y_46_im <= 4e+31)
		tmp = Float64(Float64(x_46_im - Float64(Float64(x_46_re / y_46_re) * y_46_im)) / y_46_re);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = -x_46_re / y_46_im;
	tmp = 0.0;
	if (y_46_im <= -1.95e+71)
		tmp = t_0;
	elseif (y_46_im <= 4e+31)
		tmp = (x_46_im - ((x_46_re / y_46_re) * y_46_im)) / y_46_re;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := Block[{t$95$0 = N[((-x$46$re) / y$46$im), $MachinePrecision]}, If[LessEqual[y$46$im, -195000000000000005714904619635812128629974360554371271422985808308600832], t$95$0, If[LessEqual[y$46$im, 39999999999999998543585179860992], N[(N[(x$46$im - N[(N[(x$46$re / y$46$re), $MachinePrecision] * y$46$im), $MachinePrecision]), $MachinePrecision] / y$46$re), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y.im < -1.9500000000000001e71 or 3.9999999999999999e31 < y.im

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around 0

      \[\leadsto \color{blue}{-1 \cdot \frac{x.re}{y.im}} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

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

      2. lower-/.f64 42.7%

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

   4. Applied rewrites 42.7%

      \[\leadsto \color{blue}{-1 \cdot \frac{x.re}{y.im}} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-*r/ N/A

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

      4. lift-*.f64 N/A

         \[\leadsto \frac{-1 \cdot x.re}{y.im} \]

      5. lower-/.f64 42.7%

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

      6. lift-*.f64 N/A

         \[\leadsto \frac{-1 \cdot x.re}{y.im} \]

      7. mul-1-neg N/A

         \[\leadsto \frac{\mathsf{neg}\left(x.re\right)}{y.im} \]

      8. lift-neg.f64 42.7%

         \[\leadsto \frac{-x.re}{y.im} \]

   6. Applied rewrites 42.7%

      \[\leadsto \frac{-x.re}{\color{blue}{y.im}} \]

   if -1.9500000000000001e71 < y.im < 3.9999999999999999e31

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{x.im + -1 \cdot \frac{x.re \cdot y.im}{y.re}}{\color{blue}{y.re}} \]

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 52.8%

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

   4. Applied rewrites 52.8%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

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

      3. lower--.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. mul-1-neg N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      6. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{x.re \cdot y.im}{y.re}\right)\right)\right)\right)}{y.re} \]

      7. distribute-neg-frac N/A

         \[\leadsto \frac{x.im - \left(\mathsf{neg}\left(\frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{y.re}\right)\right)}{y.re} \]

      8. distribute-frac-neg2 N/A

         \[\leadsto \frac{x.im - \frac{\mathsf{neg}\left(x.re \cdot y.im\right)}{\mathsf{neg}\left(y.re\right)}}{y.re} \]

      9. frac-2neg N/A

         \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      10. lift-/.f64 52.8%

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      11. lift-*.f64 N/A

          \[\leadsto \frac{x.im - \frac{x.re \cdot y.im}{y.re}}{y.re} \]

      12. *-commutative N/A

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      13. lift-*.f64 52.8%

          \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

   6. Applied rewrites 52.8%

      \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{x.im - \frac{y.im \cdot x.re}{y.re}}{y.re} \]

      3. associate-/l* N/A

         \[\leadsto \frac{x.im - y.im \cdot \frac{x.re}{y.re}}{y.re} \]

      4. lift-/.f64 N/A

         \[\leadsto \frac{x.im - y.im \cdot \frac{x.re}{y.re}}{y.re} \]

      5. *-commutative N/A

         \[\leadsto \frac{x.im - \frac{x.re}{y.re} \cdot y.im}{y.re} \]

      6. lower-*.f64 54.0%

         \[\leadsto \frac{x.im - \frac{x.re}{y.re} \cdot y.im}{y.re} \]

   8. Applied rewrites 54.0%

      \[\leadsto \frac{x.im - \frac{x.re}{y.re} \cdot y.im}{y.re} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 64.1% accurate, 1.5× speedup

Math

\[\begin{array}{l} t_0 := \frac{-x.re}{y.im}\\ \mathbf{if}\;y.im \leq \frac{-1156995391866473}{6427752177035961102167848369364650410088811975131171341205504}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y.im \leq 39999999999999998543585179860992:\\ \;\;\;\;\frac{x.im}{y.re}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (let* ((t_0 (/ (- x.re) y.im)))
  (if (<=
       y.im
       -1156995391866473/6427752177035961102167848369364650410088811975131171341205504)
    t_0
    (if (<= y.im 39999999999999998543585179860992)
      (/ x.im y.re)
      t_0))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = -x_46_re / y_46_im;
	double tmp;
	if (y_46_im <= -1.8e-46) {
		tmp = t_0;
	} else if (y_46_im <= 4e+31) {
		tmp = x_46_im / y_46_re;
	} 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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    real(8) :: t_0
    real(8) :: tmp
    t_0 = -x_46re / y_46im
    if (y_46im <= (-1.8d-46)) then
        tmp = t_0
    else if (y_46im <= 4d+31) then
        tmp = x_46im / y_46re
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = -x_46_re / y_46_im;
	double tmp;
	if (y_46_im <= -1.8e-46) {
		tmp = t_0;
	} else if (y_46_im <= 4e+31) {
		tmp = x_46_im / y_46_re;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	t_0 = -x_46_re / y_46_im
	tmp = 0
	if y_46_im <= -1.8e-46:
		tmp = t_0
	elif y_46_im <= 4e+31:
		tmp = x_46_im / y_46_re
	else:
		tmp = t_0
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = Float64(Float64(-x_46_re) / y_46_im)
	tmp = 0.0
	if (y_46_im <= -1.8e-46)
		tmp = t_0;
	elseif (y_46_im <= 4e+31)
		tmp = Float64(x_46_im / y_46_re);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = -x_46_re / y_46_im;
	tmp = 0.0;
	if (y_46_im <= -1.8e-46)
		tmp = t_0;
	elseif (y_46_im <= 4e+31)
		tmp = x_46_im / y_46_re;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := Block[{t$95$0 = N[((-x$46$re) / y$46$im), $MachinePrecision]}, If[LessEqual[y$46$im, -1156995391866473/6427752177035961102167848369364650410088811975131171341205504], t$95$0, If[LessEqual[y$46$im, 39999999999999998543585179860992], N[(x$46$im / y$46$re), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y.im < -1.8e-46 or 3.9999999999999999e31 < y.im

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around 0

      \[\leadsto \color{blue}{-1 \cdot \frac{x.re}{y.im}} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

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

      2. lower-/.f64 42.7%

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

   4. Applied rewrites 42.7%

      \[\leadsto \color{blue}{-1 \cdot \frac{x.re}{y.im}} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. associate-*r/ N/A

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

      4. lift-*.f64 N/A

         \[\leadsto \frac{-1 \cdot x.re}{y.im} \]

      5. lower-/.f64 42.7%

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

      6. lift-*.f64 N/A

         \[\leadsto \frac{-1 \cdot x.re}{y.im} \]

      7. mul-1-neg N/A

         \[\leadsto \frac{\mathsf{neg}\left(x.re\right)}{y.im} \]

      8. lift-neg.f64 42.7%

         \[\leadsto \frac{-x.re}{y.im} \]

   6. Applied rewrites 42.7%

      \[\leadsto \frac{-x.re}{\color{blue}{y.im}} \]

   if -1.8e-46 < y.im < 3.9999999999999999e31

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]
   3. Step-by-step derivation

      1. lower-/.f64 43.0%

         \[\leadsto \frac{x.im}{\color{blue}{y.re}} \]

   4. Applied rewrites 43.0%

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 44.9% accurate, 2.1× speedup

Math

\[\begin{array}{l} \mathbf{if}\;y.im \leq 800000000000000011524758069796219084664894897942650104109698519483991416553009053014906061005811648644043497466369868300288:\\ \;\;\;\;\frac{x.im}{y.re}\\ \mathbf{else}:\\ \;\;\;\;\frac{0}{y.re}\\ \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (if (<=
     y.im
     800000000000000011524758069796219084664894897942650104109698519483991416553009053014906061005811648644043497466369868300288)
  (/ x.im y.re)
  (/ 0 y.re)))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double tmp;
	if (y_46_im <= 8e+122) {
		tmp = x_46_im / y_46_re;
	} else {
		tmp = 0.0 / y_46_re;
	}
	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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    real(8) :: tmp
    if (y_46im <= 8d+122) then
        tmp = x_46im / y_46re
    else
        tmp = 0.0d0 / y_46re
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double tmp;
	if (y_46_im <= 8e+122) {
		tmp = x_46_im / y_46_re;
	} else {
		tmp = 0.0 / y_46_re;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	tmp = 0
	if y_46_im <= 8e+122:
		tmp = x_46_im / y_46_re
	else:
		tmp = 0.0 / y_46_re
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = 0.0
	if (y_46_im <= 8e+122)
		tmp = Float64(x_46_im / y_46_re);
	else
		tmp = Float64(0.0 / y_46_re);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = 0.0;
	if (y_46_im <= 8e+122)
		tmp = x_46_im / y_46_re;
	else
		tmp = 0.0 / y_46_re;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := If[LessEqual[y$46$im, 800000000000000011524758069796219084664894897942650104109698519483991416553009053014906061005811648644043497466369868300288], N[(x$46$im / y$46$re), $MachinePrecision], N[(0 / y$46$re), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y.im < 8.0000000000000001e122

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]
   3. Step-by-step derivation

      1. lower-/.f64 43.0%

         \[\leadsto \frac{x.im}{\color{blue}{y.re}} \]

   4. Applied rewrites 43.0%

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]

   if 8.0000000000000001e122 < y.im

   1. Initial program 61.5%

      \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

   2. Taylor expanded in y.re around inf

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]
   3. Step-by-step derivation

      1. lower-/.f64 43.0%

         \[\leadsto \frac{x.im}{\color{blue}{y.re}} \]

   4. Applied rewrites 43.0%

      \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]

   5. Taylor expanded in undef-var around zero

      \[\leadsto \frac{0}{y.re} \]
   6. Step-by-step derivation

   7. Applied rewrites 17.5%

      \[\leadsto \frac{0}{y.re} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 43.0% accurate, 3.2× speedup

Math

\[\frac{x.im}{y.re} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (/ x.im y.re))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return x_46_im / y_46_re;
}

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_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    code = x_46im / y_46re
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return x_46_im / y_46_re;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	return x_46_im / y_46_re

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	return Float64(x_46_im / y_46_re)
end

MATLAB

function tmp = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = x_46_im / y_46_re;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := N[(x$46$im / y$46$re), $MachinePrecision]

Derivation

1. Initial program 61.5%

   \[\frac{x.im \cdot y.re - x.re \cdot y.im}{y.re \cdot y.re + y.im \cdot y.im} \]

2. Taylor expanded in y.re around inf

   \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]
3. Step-by-step derivation

   1. lower-/.f64 43.0%

      \[\leadsto \frac{x.im}{\color{blue}{y.re}} \]

4. Applied rewrites 43.0%

   \[\leadsto \color{blue}{\frac{x.im}{y.re}} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025285 -o generate:evaluate
(FPCore (x.re x.im y.re y.im)
  :name "_divideComplex, imaginary part"
  :precision binary64
  (/ (- (* x.im y.re) (* x.re y.im)) (+ (* y.re y.re) (* y.im y.im))))