math.cos on complex, imaginary part

Percentage Accurate: 65.7% → 99.9%

Time: 6.2s

Alternatives: 19

Speedup: 1.2×

• 49.4% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (* (* 0.5 (sin re)) (- (exp (- im)) (exp im))))

C

double code(double re, double im) {
	return (0.5 * sin(re)) * (exp(-im) - exp(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(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = (0.5d0 * sin(re)) * (exp(-im) - exp(im))
end function

Java

public static double code(double re, double im) {
	return (0.5 * Math.sin(re)) * (Math.exp(-im) - Math.exp(im));
}

Python

def code(re, im):
	return (0.5 * math.sin(re)) * (math.exp(-im) - math.exp(im))

Julia

function code(re, im)
	return Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(-im)) - exp(im)))
end

MATLAB

function tmp = code(re, im)
	tmp = (0.5 * sin(re)) * (exp(-im) - exp(im));
end

Wolfram

code[re_, im_] := N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[(-im)], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 65.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (* (* 0.5 (sin re)) (- (exp (- im)) (exp im))))

C

double code(double re, double im) {
	return (0.5 * sin(re)) * (exp(-im) - exp(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(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = (0.5d0 * sin(re)) * (exp(-im) - exp(im))
end function

Java

public static double code(double re, double im) {
	return (0.5 * Math.sin(re)) * (Math.exp(-im) - Math.exp(im));
}

Python

def code(re, im):
	return (0.5 * math.sin(re)) * (math.exp(-im) - math.exp(im))

Julia

function code(re, im)
	return Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(-im)) - exp(im)))
end

MATLAB

function tmp = code(re, im)
	tmp = (0.5 * sin(re)) * (exp(-im) - exp(im));
end

Wolfram

code[re_, im_] := N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[(-im)], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \sinh im\right) \end{array} \]

FPCore

(FPCore (re im) :precision binary64 (* (* (sin re) 0.5) (* -2.0 (sinh im))))

C

double code(double re, double im) {
	return (sin(re) * 0.5) * (-2.0 * sinh(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(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = (sin(re) * 0.5d0) * ((-2.0d0) * sinh(im))
end function

Java

public static double code(double re, double im) {
	return (Math.sin(re) * 0.5) * (-2.0 * Math.sinh(im));
}

Python

def code(re, im):
	return (math.sin(re) * 0.5) * (-2.0 * math.sinh(im))

Julia

function code(re, im)
	return Float64(Float64(sin(re) * 0.5) * Float64(-2.0 * sinh(im)))
end

MATLAB

function tmp = code(re, im)
	tmp = (sin(re) * 0.5) * (-2.0 * sinh(im));
end

Wolfram

code[re_, im_] := N[(N[(N[Sin[re], $MachinePrecision] * 0.5), $MachinePrecision] * N[(-2.0 * N[Sinh[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 65.7%

   \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]
2. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \sin re\right)} \cdot \left(e^{-im} - e^{im}\right) \]

   2. lift-sin.f64 N/A

      \[\leadsto \left(\frac{1}{2} \cdot \color{blue}{\sin re}\right) \cdot \left(e^{-im} - e^{im}\right) \]

   3. *-commutative N/A

      \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

   4. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

   5. lift-sin.f64 65.7

      \[\leadsto \left(\color{blue}{\sin re} \cdot 0.5\right) \cdot \left(e^{-im} - e^{im}\right) \]

   6. lift--.f64 N/A

      \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(e^{-im} - e^{im}\right)} \]

   7. lift-exp.f64 N/A

      \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\color{blue}{e^{-im}} - e^{im}\right) \]

   8. lift-neg.f64 N/A

      \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\color{blue}{\mathsf{neg}\left(im\right)}} - e^{im}\right) \]

   9. lift-exp.f64 N/A

      \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - \color{blue}{e^{im}}\right) \]

   10. sub-negate-rev N/A

       \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right)} \]

   11. lower-neg.f64 N/A

       \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)} \]

   12. sinh-undef N/A

       \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

   13. lower-*.f64 N/A

       \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

   14. lower-sinh.f64 99.9

       \[\leadsto \left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \color{blue}{\sinh im}\right) \]

3. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \sinh im\right)} \]
4. Step-by-step derivation

   1. lift-neg.f64 N/A

      \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(\mathsf{neg}\left(2 \cdot \sinh im\right)\right)} \]

   2. lift-*.f64 N/A

      \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\mathsf{neg}\left(\color{blue}{2 \cdot \sinh im}\right)\right) \]

   3. lift-sinh.f64 N/A

      \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\mathsf{neg}\left(2 \cdot \color{blue}{\sinh im}\right)\right) \]

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

      \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(\left(\mathsf{neg}\left(2\right)\right) \cdot \sinh im\right)} \]

   5. metadata-eval N/A

      \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\color{blue}{-2} \cdot \sinh im\right) \]

   6. lower-*.f64 N/A

      \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(-2 \cdot \sinh im\right)} \]

   7. lift-sinh.f64 99.9

      \[\leadsto \left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \color{blue}{\sinh im}\right) \]

5. Applied rewrites 99.9%

   \[\leadsto \left(\sin re \cdot 0.5\right) \cdot \color{blue}{\left(-2 \cdot \sinh im\right)} \]
6. Add Preprocessing

Alternative 2: 89.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;im \leq 2.2:\\ \;\;\;\;\left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im\\ \mathbf{else}:\\ \;\;\;\;\left(\sin re \cdot 0.5\right) \cdot \left(1 - e^{im}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= im 2.2)
   (* (* (sin re) (fma -0.16666666666666666 (* im im) -1.0)) im)
   (* (* (sin re) 0.5) (- 1.0 (exp im)))))

C

double code(double re, double im) {
	double tmp;
	if (im <= 2.2) {
		tmp = (sin(re) * fma(-0.16666666666666666, (im * im), -1.0)) * im;
	} else {
		tmp = (sin(re) * 0.5) * (1.0 - exp(im));
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (im <= 2.2)
		tmp = Float64(Float64(sin(re) * fma(-0.16666666666666666, Float64(im * im), -1.0)) * im);
	else
		tmp = Float64(Float64(sin(re) * 0.5) * Float64(1.0 - exp(im)));
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[im, 2.2], N[(N[(N[Sin[re], $MachinePrecision] * N[(-0.16666666666666666 * N[(im * im), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision] * im), $MachinePrecision], N[(N[(N[Sin[re], $MachinePrecision] * 0.5), $MachinePrecision] * N[(1.0 - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if im < 2.2000000000000002

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right) + -1 \cdot \sin re\right) \cdot im \]

      4. associate-*r* N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {im}^{2}\right) \cdot \sin re + -1 \cdot \sin re\right) \cdot im \]

      5. distribute-rgt-out N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      7. lift-sin.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      8. lower-fma.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, {im}^{2}, -1\right)\right) \cdot im \]

      9. unpow2 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      10. lower-*.f64 80.8

          \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{\left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im} \]

   if 2.2000000000000002 < im

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \sin re\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      2. lift-sin.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \color{blue}{\sin re}\right) \cdot \left(e^{-im} - e^{im}\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      5. lift-sin.f64 65.7

         \[\leadsto \left(\color{blue}{\sin re} \cdot 0.5\right) \cdot \left(e^{-im} - e^{im}\right) \]

   3. Applied rewrites 65.7%

      \[\leadsto \color{blue}{\left(\sin re \cdot 0.5\right)} \cdot \left(e^{-im} - e^{im}\right) \]

   4. Taylor expanded in im around 0

      \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\color{blue}{1} - e^{im}\right) \]
   5. Step-by-step derivation

   6. Applied rewrites 39.7%

      \[\leadsto \left(\sin re \cdot 0.5\right) \cdot \left(\color{blue}{1} - e^{im}\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 65.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right)\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;\left(\left(1 - e^{im}\right) \cdot re\right) \cdot 0.5\\ \mathbf{elif}\;t\_0 \leq 2 \cdot 10^{+40}:\\ \;\;\;\;\left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot 0.08333333333333333\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (* 0.5 (sin re)) (- (exp (- im)) (exp im)))))
   (if (<= t_0 (- INFINITY))
     (* (* (- 1.0 (exp im)) re) 0.5)
     (if (<= t_0 2e+40)
       (* (* (sin re) (fma -0.16666666666666666 (* im im) -1.0)) im)
       (* (* (* (* re re) re) (* (sinh im) 2.0)) 0.08333333333333333)))))

C

double code(double re, double im) {
	double t_0 = (0.5 * sin(re)) * (exp(-im) - exp(im));
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = ((1.0 - exp(im)) * re) * 0.5;
	} else if (t_0 <= 2e+40) {
		tmp = (sin(re) * fma(-0.16666666666666666, (im * im), -1.0)) * im;
	} else {
		tmp = (((re * re) * re) * (sinh(im) * 2.0)) * 0.08333333333333333;
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(-im)) - exp(im)))
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(Float64(Float64(1.0 - exp(im)) * re) * 0.5);
	elseif (t_0 <= 2e+40)
		tmp = Float64(Float64(sin(re) * fma(-0.16666666666666666, Float64(im * im), -1.0)) * im);
	else
		tmp = Float64(Float64(Float64(Float64(re * re) * re) * Float64(sinh(im) * 2.0)) * 0.08333333333333333);
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[(-im)], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], N[(N[(N[(1.0 - N[Exp[im], $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision] * 0.5), $MachinePrecision], If[LessEqual[t$95$0, 2e+40], N[(N[(N[Sin[re], $MachinePrecision] * N[(-0.16666666666666666 * N[(im * im), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision] * im), $MachinePrecision], N[(N[(N[(N[(re * re), $MachinePrecision] * re), $MachinePrecision] * N[(N[Sinh[im], $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision] * 0.08333333333333333), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < -inf.0

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      5. sub-negate-rev N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      6. lower-neg.f64 N/A

         \[\leadsto \left(\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      7. sinh-undef N/A

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

      8. lower-*.f64 N/A

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

      9. lower-sinh.f64 63.3

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5 \]

   4. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5} \]
   5. Step-by-step derivation

      1. lift-neg.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(2 \cdot \sinh im\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(2 \cdot \sinh im\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      3. lift-sinh.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(2 \cdot \sinh im\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      4. sinh-undef-rev N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      5. sub-negate-rev N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      6. lift-neg.f64 N/A

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      7. lift-exp.f64 N/A

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      8. lift-exp.f64 N/A

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      9. lift--.f64 52.8

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot 0.5 \]

   6. Applied rewrites 52.8%

      \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot 0.5 \]

   7. Taylor expanded in im around 0

      \[\leadsto \left(\left(1 - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]
   8. Step-by-step derivation

   9. Applied rewrites 33.6%

      \[\leadsto \left(\left(1 - e^{im}\right) \cdot re\right) \cdot 0.5 \]

   if -inf.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < 2.00000000000000006e40

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right) + -1 \cdot \sin re\right) \cdot im \]

      4. associate-*r* N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {im}^{2}\right) \cdot \sin re + -1 \cdot \sin re\right) \cdot im \]

      5. distribute-rgt-out N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      7. lift-sin.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      8. lower-fma.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, {im}^{2}, -1\right)\right) \cdot im \]

      9. unpow2 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      10. lower-*.f64 80.8

          \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{\left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im} \]

   if 2.00000000000000006e40 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im)))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in re around inf

      \[\leadsto \frac{1}{12} \cdot \color{blue}{\left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right)} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      2. lower-*.f64 N/A

         \[\leadsto \left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      3. lower-*.f64 N/A

         \[\leadsto \left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      4. unpow3 N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      5. pow2 N/A

         \[\leadsto \left(\left({re}^{2} \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\left({re}^{2} \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      7. pow2 N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      8. lift-*.f64 N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      9. rec-exp N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right) \cdot \frac{1}{12} \]

      10. sinh-undef-rev N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(2 \cdot \sinh im\right)\right) \cdot \frac{1}{12} \]

      11. *-commutative N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot \frac{1}{12} \]

      12. lower-*.f64 N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot \frac{1}{12} \]

      13. lift-sinh.f64 25.2

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot 0.08333333333333333 \]

   7. Applied rewrites 25.2%

      \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot \color{blue}{0.08333333333333333} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 64.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right)\\ \mathbf{if}\;t\_0 \leq -1 \cdot 10^{-194}:\\ \;\;\;\;\left(\left(1 - e^{im}\right) \cdot re\right) \cdot 0.5\\ \mathbf{elif}\;t\_0 \leq 2 \cdot 10^{+40}:\\ \;\;\;\;\left(-\sin re\right) \cdot im\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot 0.08333333333333333\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (* 0.5 (sin re)) (- (exp (- im)) (exp im)))))
   (if (<= t_0 -1e-194)
     (* (* (- 1.0 (exp im)) re) 0.5)
     (if (<= t_0 2e+40)
       (* (- (sin re)) im)
       (* (* (* (* re re) re) (* (sinh im) 2.0)) 0.08333333333333333)))))

C

double code(double re, double im) {
	double t_0 = (0.5 * sin(re)) * (exp(-im) - exp(im));
	double tmp;
	if (t_0 <= -1e-194) {
		tmp = ((1.0 - exp(im)) * re) * 0.5;
	} else if (t_0 <= 2e+40) {
		tmp = -sin(re) * im;
	} else {
		tmp = (((re * re) * re) * (sinh(im) * 2.0)) * 0.08333333333333333;
	}
	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(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (0.5d0 * sin(re)) * (exp(-im) - exp(im))
    if (t_0 <= (-1d-194)) then
        tmp = ((1.0d0 - exp(im)) * re) * 0.5d0
    else if (t_0 <= 2d+40) then
        tmp = -sin(re) * im
    else
        tmp = (((re * re) * re) * (sinh(im) * 2.0d0)) * 0.08333333333333333d0
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double t_0 = (0.5 * Math.sin(re)) * (Math.exp(-im) - Math.exp(im));
	double tmp;
	if (t_0 <= -1e-194) {
		tmp = ((1.0 - Math.exp(im)) * re) * 0.5;
	} else if (t_0 <= 2e+40) {
		tmp = -Math.sin(re) * im;
	} else {
		tmp = (((re * re) * re) * (Math.sinh(im) * 2.0)) * 0.08333333333333333;
	}
	return tmp;
}

Python

def code(re, im):
	t_0 = (0.5 * math.sin(re)) * (math.exp(-im) - math.exp(im))
	tmp = 0
	if t_0 <= -1e-194:
		tmp = ((1.0 - math.exp(im)) * re) * 0.5
	elif t_0 <= 2e+40:
		tmp = -math.sin(re) * im
	else:
		tmp = (((re * re) * re) * (math.sinh(im) * 2.0)) * 0.08333333333333333
	return tmp

Julia

function code(re, im)
	t_0 = Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(-im)) - exp(im)))
	tmp = 0.0
	if (t_0 <= -1e-194)
		tmp = Float64(Float64(Float64(1.0 - exp(im)) * re) * 0.5);
	elseif (t_0 <= 2e+40)
		tmp = Float64(Float64(-sin(re)) * im);
	else
		tmp = Float64(Float64(Float64(Float64(re * re) * re) * Float64(sinh(im) * 2.0)) * 0.08333333333333333);
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	t_0 = (0.5 * sin(re)) * (exp(-im) - exp(im));
	tmp = 0.0;
	if (t_0 <= -1e-194)
		tmp = ((1.0 - exp(im)) * re) * 0.5;
	elseif (t_0 <= 2e+40)
		tmp = -sin(re) * im;
	else
		tmp = (((re * re) * re) * (sinh(im) * 2.0)) * 0.08333333333333333;
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[(-im)], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -1e-194], N[(N[(N[(1.0 - N[Exp[im], $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision] * 0.5), $MachinePrecision], If[LessEqual[t$95$0, 2e+40], N[((-N[Sin[re], $MachinePrecision]) * im), $MachinePrecision], N[(N[(N[(N[(re * re), $MachinePrecision] * re), $MachinePrecision] * N[(N[Sinh[im], $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision] * 0.08333333333333333), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < -1.00000000000000002e-194

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      5. sub-negate-rev N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      6. lower-neg.f64 N/A

         \[\leadsto \left(\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      7. sinh-undef N/A

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

      8. lower-*.f64 N/A

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

      9. lower-sinh.f64 63.3

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5 \]

   4. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5} \]
   5. Step-by-step derivation

      1. lift-neg.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(2 \cdot \sinh im\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(2 \cdot \sinh im\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      3. lift-sinh.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(2 \cdot \sinh im\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      4. sinh-undef-rev N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      5. sub-negate-rev N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      6. lift-neg.f64 N/A

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      7. lift-exp.f64 N/A

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      8. lift-exp.f64 N/A

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      9. lift--.f64 52.8

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot 0.5 \]

   6. Applied rewrites 52.8%

      \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot 0.5 \]

   7. Taylor expanded in im around 0

      \[\leadsto \left(\left(1 - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]
   8. Step-by-step derivation

   9. Applied rewrites 33.6%

      \[\leadsto \left(\left(1 - e^{im}\right) \cdot re\right) \cdot 0.5 \]

   if -1.00000000000000002e-194 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < 2.00000000000000006e40

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in im around 0

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

      1. mul-1-neg N/A

         \[\leadsto \mathsf{neg}\left(im \cdot \sin re\right) \]

      2. *-commutative N/A

         \[\leadsto \mathsf{neg}\left(\sin re \cdot im\right) \]

      3. distribute-lft-neg-out N/A

         \[\leadsto \left(\mathsf{neg}\left(\sin re\right)\right) \cdot \color{blue}{im} \]

      4. mul-1-neg N/A

         \[\leadsto \left(-1 \cdot \sin re\right) \cdot im \]

      5. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot \sin re\right) \cdot \color{blue}{im} \]

      6. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\sin re\right)\right) \cdot im \]

      7. lower-neg.f64 N/A

         \[\leadsto \left(-\sin re\right) \cdot im \]

      8. lift-sin.f64 51.7

         \[\leadsto \left(-\sin re\right) \cdot im \]

   4. Applied rewrites 51.7%

      \[\leadsto \color{blue}{\left(-\sin re\right) \cdot im} \]

   if 2.00000000000000006e40 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im)))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in re around inf

      \[\leadsto \frac{1}{12} \cdot \color{blue}{\left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right)} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      2. lower-*.f64 N/A

         \[\leadsto \left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      3. lower-*.f64 N/A

         \[\leadsto \left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      4. unpow3 N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      5. pow2 N/A

         \[\leadsto \left(\left({re}^{2} \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\left({re}^{2} \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      7. pow2 N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      8. lift-*.f64 N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      9. rec-exp N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right) \cdot \frac{1}{12} \]

      10. sinh-undef-rev N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(2 \cdot \sinh im\right)\right) \cdot \frac{1}{12} \]

      11. *-commutative N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot \frac{1}{12} \]

      12. lower-*.f64 N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot \frac{1}{12} \]

      13. lift-sinh.f64 25.2

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot 0.08333333333333333 \]

   7. Applied rewrites 25.2%

      \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot \color{blue}{0.08333333333333333} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 62.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin re \leq 4 \cdot 10^{-9}:\\ \;\;\;\;\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re\\ \mathbf{else}:\\ \;\;\;\;\left(re \cdot \left(\left(-0.16666666666666666 \cdot im\right) \cdot im\right)\right) \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* 0.5 (sin re)) 4e-9)
   (* (* (- (* 2.0 (sinh im))) (fma (* re re) -0.08333333333333333 0.5)) re)
   (* (* re (* (* -0.16666666666666666 im) im)) im)))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(re)) <= 4e-9) {
		tmp = (-(2.0 * sinh(im)) * fma((re * re), -0.08333333333333333, 0.5)) * re;
	} else {
		tmp = (re * ((-0.16666666666666666 * im) * im)) * im;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= 4e-9)
		tmp = Float64(Float64(Float64(-Float64(2.0 * sinh(im))) * fma(Float64(re * re), -0.08333333333333333, 0.5)) * re);
	else
		tmp = Float64(Float64(re * Float64(Float64(-0.16666666666666666 * im) * im)) * im);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], 4e-9], N[(N[((-N[(2.0 * N[Sinh[im], $MachinePrecision]), $MachinePrecision]) * N[(N[(re * re), $MachinePrecision] * -0.08333333333333333 + 0.5), $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision], N[(N[(re * N[(N[(-0.16666666666666666 * im), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < 4.00000000000000025e-9

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   if 4.00000000000000025e-9 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right) + -1 \cdot \sin re\right) \cdot im \]

      4. associate-*r* N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {im}^{2}\right) \cdot \sin re + -1 \cdot \sin re\right) \cdot im \]

      5. distribute-rgt-out N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      7. lift-sin.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      8. lower-fma.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, {im}^{2}, -1\right)\right) \cdot im \]

      9. unpow2 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      10. lower-*.f64 80.8

          \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{\left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im} \]

   5. Taylor expanded in im around inf

      \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2}\right)\right) \cdot im \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      3. pow2 N/A

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      4. lift-*.f64 45.6

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   7. Applied rewrites 45.6%

      \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   8. Taylor expanded in re around 0

      \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]
   9. Step-by-step derivation

   10. Applied rewrites 39.4%

       \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]
   11. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

       2. lift-*.f64 N/A

          \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

       3. pow2 N/A

          \[\leadsto \left(re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

       4. *-commutative N/A

          \[\leadsto \left(re \cdot \left(\frac{-1}{6} \cdot {im}^{2}\right)\right) \cdot im \]

       5. pow2 N/A

          \[\leadsto \left(re \cdot \left(\frac{-1}{6} \cdot \left(im \cdot im\right)\right)\right) \cdot im \]

       6. associate-*r* N/A

          \[\leadsto \left(re \cdot \left(\left(\frac{-1}{6} \cdot im\right) \cdot im\right)\right) \cdot im \]

       7. lower-*.f64 N/A

          \[\leadsto \left(re \cdot \left(\left(\frac{-1}{6} \cdot im\right) \cdot im\right)\right) \cdot im \]

       8. lower-*.f64 39.4

          \[\leadsto \left(re \cdot \left(\left(-0.16666666666666666 \cdot im\right) \cdot im\right)\right) \cdot im \]

   12. Applied rewrites 39.4%

       \[\leadsto \left(re \cdot \left(\left(-0.16666666666666666 \cdot im\right) \cdot im\right)\right) \cdot im \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 62.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin re \leq -0.01:\\ \;\;\;\;\left(\left(\left(re \cdot re\right) \cdot -0.08333333333333333\right) \cdot \left(\sinh im \cdot -2\right)\right) \cdot re\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* 0.5 (sin re)) -0.01)
   (* (* (* (* re re) -0.08333333333333333) (* (sinh im) -2.0)) re)
   (* (* (* (sinh im) 2.0) re) -0.5)))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(re)) <= -0.01) {
		tmp = (((re * re) * -0.08333333333333333) * (sinh(im) * -2.0)) * re;
	} else {
		tmp = ((sinh(im) * 2.0) * re) * -0.5;
	}
	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(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if ((0.5d0 * sin(re)) <= (-0.01d0)) then
        tmp = (((re * re) * (-0.08333333333333333d0)) * (sinh(im) * (-2.0d0))) * re
    else
        tmp = ((sinh(im) * 2.0d0) * re) * (-0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if ((0.5 * Math.sin(re)) <= -0.01) {
		tmp = (((re * re) * -0.08333333333333333) * (Math.sinh(im) * -2.0)) * re;
	} else {
		tmp = ((Math.sinh(im) * 2.0) * re) * -0.5;
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if (0.5 * math.sin(re)) <= -0.01:
		tmp = (((re * re) * -0.08333333333333333) * (math.sinh(im) * -2.0)) * re
	else:
		tmp = ((math.sinh(im) * 2.0) * re) * -0.5
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= -0.01)
		tmp = Float64(Float64(Float64(Float64(re * re) * -0.08333333333333333) * Float64(sinh(im) * -2.0)) * re);
	else
		tmp = Float64(Float64(Float64(sinh(im) * 2.0) * re) * -0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if ((0.5 * sin(re)) <= -0.01)
		tmp = (((re * re) * -0.08333333333333333) * (sinh(im) * -2.0)) * re;
	else
		tmp = ((sinh(im) * 2.0) * re) * -0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], -0.01], N[(N[(N[(N[(re * re), $MachinePrecision] * -0.08333333333333333), $MachinePrecision] * N[(N[Sinh[im], $MachinePrecision] * -2.0), $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision], N[(N[(N[(N[Sinh[im], $MachinePrecision] * 2.0), $MachinePrecision] * re), $MachinePrecision] * -0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0100000000000000002

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in im around 0

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]
   6. Step-by-step derivation

      1. sinh-undef-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      2. sub-negate-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      3. lower-*.f64 35.9

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   7. Applied rewrites 35.9%

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   8. Taylor expanded in re around inf

      \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)\right) \cdot re \]
   9. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \left(\left(\frac{-1}{12} \cdot {re}^{2}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot re \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(\frac{-1}{12} \cdot {re}^{2}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot re \]

      3. *-commutative N/A

         \[\leadsto \left(\left({re}^{2} \cdot \frac{-1}{12}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot re \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left({re}^{2} \cdot \frac{-1}{12}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot re \]

      5. pow2 N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot \frac{-1}{12}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot re \]

      6. lift-*.f64 N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot \frac{-1}{12}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot re \]

      7. sub-negate-rev N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot \frac{-1}{12}\right) \cdot \left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right)\right) \cdot re \]

      8. sinh-undef-rev N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot \frac{-1}{12}\right) \cdot \left(\mathsf{neg}\left(2 \cdot \sinh im\right)\right)\right) \cdot re \]

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

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot \frac{-1}{12}\right) \cdot \left(\left(\mathsf{neg}\left(2\right)\right) \cdot \sinh im\right)\right) \cdot re \]

      10. metadata-eval N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot \frac{-1}{12}\right) \cdot \left(-2 \cdot \sinh im\right)\right) \cdot re \]

      11. *-commutative N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot \frac{-1}{12}\right) \cdot \left(\sinh im \cdot -2\right)\right) \cdot re \]

      12. lower-*.f64 N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot \frac{-1}{12}\right) \cdot \left(\sinh im \cdot -2\right)\right) \cdot re \]

      13. lift-sinh.f64 25.3

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot -0.08333333333333333\right) \cdot \left(\sinh im \cdot -2\right)\right) \cdot re \]

   10. Applied rewrites 25.3%

       \[\leadsto \left(\left(\left(re \cdot re\right) \cdot -0.08333333333333333\right) \cdot \left(\sinh im \cdot -2\right)\right) \cdot re \]

   if -0.0100000000000000002 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \sin re\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      2. lift-sin.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \color{blue}{\sin re}\right) \cdot \left(e^{-im} - e^{im}\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      5. lift-sin.f64 65.7

         \[\leadsto \left(\color{blue}{\sin re} \cdot 0.5\right) \cdot \left(e^{-im} - e^{im}\right) \]

      6. lift--.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(e^{-im} - e^{im}\right)} \]

      7. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\color{blue}{e^{-im}} - e^{im}\right) \]

      8. lift-neg.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\color{blue}{\mathsf{neg}\left(im\right)}} - e^{im}\right) \]

      9. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - \color{blue}{e^{im}}\right) \]

      10. sub-negate-rev N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right)} \]

      11. lower-neg.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)} \]

      12. sinh-undef N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      14. lower-sinh.f64 99.9

          \[\leadsto \left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \color{blue}{\sinh im}\right) \]

   3. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \sinh im\right)} \]

   4. Taylor expanded in re around 0

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

      1. *-commutative N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      2. sinh-undef-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      3. sub-negate-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      4. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      6. *-commutative N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      8. rec-exp N/A

         \[\leadsto \left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      9. sinh-undef-rev N/A

         \[\leadsto \left(\left(2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{-1}{2} \]

      10. *-commutative N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      11. lower-*.f64 N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      12. lift-sinh.f64 63.3

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5 \]

   6. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 62.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \sinh im \cdot 2\\ \mathbf{if}\;0.5 \cdot \sin re \leq -0.01:\\ \;\;\;\;\left(\left(\left(re \cdot re\right) \cdot re\right) \cdot t\_0\right) \cdot 0.08333333333333333\\ \mathbf{else}:\\ \;\;\;\;\left(t\_0 \cdot re\right) \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (sinh im) 2.0)))
   (if (<= (* 0.5 (sin re)) -0.01)
     (* (* (* (* re re) re) t_0) 0.08333333333333333)
     (* (* t_0 re) -0.5))))

C

double code(double re, double im) {
	double t_0 = sinh(im) * 2.0;
	double tmp;
	if ((0.5 * sin(re)) <= -0.01) {
		tmp = (((re * re) * re) * t_0) * 0.08333333333333333;
	} else {
		tmp = (t_0 * re) * -0.5;
	}
	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(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: t_0
    real(8) :: tmp
    t_0 = sinh(im) * 2.0d0
    if ((0.5d0 * sin(re)) <= (-0.01d0)) then
        tmp = (((re * re) * re) * t_0) * 0.08333333333333333d0
    else
        tmp = (t_0 * re) * (-0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double t_0 = Math.sinh(im) * 2.0;
	double tmp;
	if ((0.5 * Math.sin(re)) <= -0.01) {
		tmp = (((re * re) * re) * t_0) * 0.08333333333333333;
	} else {
		tmp = (t_0 * re) * -0.5;
	}
	return tmp;
}

Python

def code(re, im):
	t_0 = math.sinh(im) * 2.0
	tmp = 0
	if (0.5 * math.sin(re)) <= -0.01:
		tmp = (((re * re) * re) * t_0) * 0.08333333333333333
	else:
		tmp = (t_0 * re) * -0.5
	return tmp

Julia

function code(re, im)
	t_0 = Float64(sinh(im) * 2.0)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= -0.01)
		tmp = Float64(Float64(Float64(Float64(re * re) * re) * t_0) * 0.08333333333333333);
	else
		tmp = Float64(Float64(t_0 * re) * -0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	t_0 = sinh(im) * 2.0;
	tmp = 0.0;
	if ((0.5 * sin(re)) <= -0.01)
		tmp = (((re * re) * re) * t_0) * 0.08333333333333333;
	else
		tmp = (t_0 * re) * -0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[Sinh[im], $MachinePrecision] * 2.0), $MachinePrecision]}, If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], -0.01], N[(N[(N[(N[(re * re), $MachinePrecision] * re), $MachinePrecision] * t$95$0), $MachinePrecision] * 0.08333333333333333), $MachinePrecision], N[(N[(t$95$0 * re), $MachinePrecision] * -0.5), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0100000000000000002

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in re around inf

      \[\leadsto \frac{1}{12} \cdot \color{blue}{\left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right)} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      2. lower-*.f64 N/A

         \[\leadsto \left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      3. lower-*.f64 N/A

         \[\leadsto \left({re}^{3} \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      4. unpow3 N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      5. pow2 N/A

         \[\leadsto \left(\left({re}^{2} \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\left({re}^{2} \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      7. pow2 N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      8. lift-*.f64 N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \frac{1}{12} \]

      9. rec-exp N/A

         \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right) \cdot \frac{1}{12} \]

      10. sinh-undef-rev N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(2 \cdot \sinh im\right)\right) \cdot \frac{1}{12} \]

      11. *-commutative N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot \frac{1}{12} \]

      12. lower-*.f64 N/A

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot \frac{1}{12} \]

      13. lift-sinh.f64 25.2

          \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot 0.08333333333333333 \]

   7. Applied rewrites 25.2%

      \[\leadsto \left(\left(\left(re \cdot re\right) \cdot re\right) \cdot \left(\sinh im \cdot 2\right)\right) \cdot \color{blue}{0.08333333333333333} \]

   if -0.0100000000000000002 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \sin re\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      2. lift-sin.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \color{blue}{\sin re}\right) \cdot \left(e^{-im} - e^{im}\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      5. lift-sin.f64 65.7

         \[\leadsto \left(\color{blue}{\sin re} \cdot 0.5\right) \cdot \left(e^{-im} - e^{im}\right) \]

      6. lift--.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(e^{-im} - e^{im}\right)} \]

      7. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\color{blue}{e^{-im}} - e^{im}\right) \]

      8. lift-neg.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\color{blue}{\mathsf{neg}\left(im\right)}} - e^{im}\right) \]

      9. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - \color{blue}{e^{im}}\right) \]

      10. sub-negate-rev N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right)} \]

      11. lower-neg.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)} \]

      12. sinh-undef N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      14. lower-sinh.f64 99.9

          \[\leadsto \left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \color{blue}{\sinh im}\right) \]

   3. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \sinh im\right)} \]

   4. Taylor expanded in re around 0

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

      1. *-commutative N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      2. sinh-undef-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      3. sub-negate-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      4. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      6. *-commutative N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      8. rec-exp N/A

         \[\leadsto \left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      9. sinh-undef-rev N/A

         \[\leadsto \left(\left(2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{-1}{2} \]

      10. *-commutative N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      11. lower-*.f64 N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      12. lift-sinh.f64 63.3

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5 \]

   6. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 62.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin re \leq -0.01:\\ \;\;\;\;\left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* 0.5 (sin re)) -0.01)
   (*
    (*
     (* (fma -0.3333333333333333 (* im im) -2.0) im)
     (fma (* re re) -0.08333333333333333 0.5))
    re)
   (* (* (* (sinh im) 2.0) re) -0.5)))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(re)) <= -0.01) {
		tmp = ((fma(-0.3333333333333333, (im * im), -2.0) * im) * fma((re * re), -0.08333333333333333, 0.5)) * re;
	} else {
		tmp = ((sinh(im) * 2.0) * re) * -0.5;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= -0.01)
		tmp = Float64(Float64(Float64(fma(-0.3333333333333333, Float64(im * im), -2.0) * im) * fma(Float64(re * re), -0.08333333333333333, 0.5)) * re);
	else
		tmp = Float64(Float64(Float64(sinh(im) * 2.0) * re) * -0.5);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], -0.01], N[(N[(N[(N[(-0.3333333333333333 * N[(im * im), $MachinePrecision] + -2.0), $MachinePrecision] * im), $MachinePrecision] * N[(N[(re * re), $MachinePrecision] * -0.08333333333333333 + 0.5), $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision], N[(N[(N[(N[Sinh[im], $MachinePrecision] * 2.0), $MachinePrecision] * re), $MachinePrecision] * -0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0100000000000000002

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in im around 0

      \[\leadsto \left(\left(im \cdot \left(\frac{-1}{3} \cdot {im}^{2} - 2\right)\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]
   6. Step-by-step derivation

      1. sinh-undef-rev N/A

         \[\leadsto \left(\left(im \cdot \left(\frac{-1}{3} \cdot {im}^{2} - 2\right)\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      2. sub-negate-rev N/A

         \[\leadsto \left(\left(im \cdot \left(\frac{-1}{3} \cdot {im}^{2} - 2\right)\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      3. *-commutative N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} - 2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} - 2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      5. sub-flip N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} + \left(\mathsf{neg}\left(2\right)\right)\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      6. metadata-eval N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} + -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, {im}^{2}, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      8. pow2 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, im \cdot im, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      9. lift-*.f64 54.0

         \[\leadsto \left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   7. Applied rewrites 54.0%

      \[\leadsto \left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   if -0.0100000000000000002 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \sin re\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      2. lift-sin.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \color{blue}{\sin re}\right) \cdot \left(e^{-im} - e^{im}\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      5. lift-sin.f64 65.7

         \[\leadsto \left(\color{blue}{\sin re} \cdot 0.5\right) \cdot \left(e^{-im} - e^{im}\right) \]

      6. lift--.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(e^{-im} - e^{im}\right)} \]

      7. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\color{blue}{e^{-im}} - e^{im}\right) \]

      8. lift-neg.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\color{blue}{\mathsf{neg}\left(im\right)}} - e^{im}\right) \]

      9. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - \color{blue}{e^{im}}\right) \]

      10. sub-negate-rev N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right)} \]

      11. lower-neg.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)} \]

      12. sinh-undef N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      14. lower-sinh.f64 99.9

          \[\leadsto \left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \color{blue}{\sinh im}\right) \]

   3. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \sinh im\right)} \]

   4. Taylor expanded in re around 0

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

      1. *-commutative N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      2. sinh-undef-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      3. sub-negate-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      4. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      6. *-commutative N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      8. rec-exp N/A

         \[\leadsto \left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      9. sinh-undef-rev N/A

         \[\leadsto \left(\left(2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{-1}{2} \]

      10. *-commutative N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      11. lower-*.f64 N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      12. lift-sinh.f64 63.3

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5 \]

   6. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 62.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin re \leq -0.01:\\ \;\;\;\;\left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot \left(\left(re \cdot re\right) \cdot -0.08333333333333333\right)\right) \cdot re\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* 0.5 (sin re)) -0.01)
   (*
    (*
     (* (fma -0.3333333333333333 (* im im) -2.0) im)
     (* (* re re) -0.08333333333333333))
    re)
   (* (* (* (sinh im) 2.0) re) -0.5)))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(re)) <= -0.01) {
		tmp = ((fma(-0.3333333333333333, (im * im), -2.0) * im) * ((re * re) * -0.08333333333333333)) * re;
	} else {
		tmp = ((sinh(im) * 2.0) * re) * -0.5;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= -0.01)
		tmp = Float64(Float64(Float64(fma(-0.3333333333333333, Float64(im * im), -2.0) * im) * Float64(Float64(re * re) * -0.08333333333333333)) * re);
	else
		tmp = Float64(Float64(Float64(sinh(im) * 2.0) * re) * -0.5);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], -0.01], N[(N[(N[(N[(-0.3333333333333333 * N[(im * im), $MachinePrecision] + -2.0), $MachinePrecision] * im), $MachinePrecision] * N[(N[(re * re), $MachinePrecision] * -0.08333333333333333), $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision], N[(N[(N[(N[Sinh[im], $MachinePrecision] * 2.0), $MachinePrecision] * re), $MachinePrecision] * -0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0100000000000000002

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in im around 0

      \[\leadsto \left(\left(im \cdot \left(\frac{-1}{3} \cdot {im}^{2} - 2\right)\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]
   6. Step-by-step derivation

      1. sinh-undef-rev N/A

         \[\leadsto \left(\left(im \cdot \left(\frac{-1}{3} \cdot {im}^{2} - 2\right)\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      2. sub-negate-rev N/A

         \[\leadsto \left(\left(im \cdot \left(\frac{-1}{3} \cdot {im}^{2} - 2\right)\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      3. *-commutative N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} - 2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} - 2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      5. sub-flip N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} + \left(\mathsf{neg}\left(2\right)\right)\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      6. metadata-eval N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} + -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, {im}^{2}, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      8. pow2 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, im \cdot im, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      9. lift-*.f64 54.0

         \[\leadsto \left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   7. Applied rewrites 54.0%

      \[\leadsto \left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   8. Taylor expanded in re around inf

      \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, im \cdot im, -2\right) \cdot im\right) \cdot \left(\frac{-1}{12} \cdot {re}^{2}\right)\right) \cdot re \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, im \cdot im, -2\right) \cdot im\right) \cdot \left({re}^{2} \cdot \frac{-1}{12}\right)\right) \cdot re \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, im \cdot im, -2\right) \cdot im\right) \cdot \left({re}^{2} \cdot \frac{-1}{12}\right)\right) \cdot re \]

      3. pow2 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, im \cdot im, -2\right) \cdot im\right) \cdot \left(\left(re \cdot re\right) \cdot \frac{-1}{12}\right)\right) \cdot re \]

      4. lift-*.f64 24.7

         \[\leadsto \left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot \left(\left(re \cdot re\right) \cdot -0.08333333333333333\right)\right) \cdot re \]

   10. Applied rewrites 24.7%

       \[\leadsto \left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot \left(\left(re \cdot re\right) \cdot -0.08333333333333333\right)\right) \cdot re \]

   if -0.0100000000000000002 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \sin re\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      2. lift-sin.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \color{blue}{\sin re}\right) \cdot \left(e^{-im} - e^{im}\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      5. lift-sin.f64 65.7

         \[\leadsto \left(\color{blue}{\sin re} \cdot 0.5\right) \cdot \left(e^{-im} - e^{im}\right) \]

      6. lift--.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(e^{-im} - e^{im}\right)} \]

      7. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\color{blue}{e^{-im}} - e^{im}\right) \]

      8. lift-neg.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\color{blue}{\mathsf{neg}\left(im\right)}} - e^{im}\right) \]

      9. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - \color{blue}{e^{im}}\right) \]

      10. sub-negate-rev N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right)} \]

      11. lower-neg.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)} \]

      12. sinh-undef N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      14. lower-sinh.f64 99.9

          \[\leadsto \left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \color{blue}{\sinh im}\right) \]

   3. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \sinh im\right)} \]

   4. Taylor expanded in re around 0

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

      1. *-commutative N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      2. sinh-undef-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      3. sub-negate-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      4. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      6. *-commutative N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      8. rec-exp N/A

         \[\leadsto \left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      9. sinh-undef-rev N/A

         \[\leadsto \left(\left(2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{-1}{2} \]

      10. *-commutative N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      11. lower-*.f64 N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      12. lift-sinh.f64 63.3

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5 \]

   6. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 62.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin re \leq -0.01:\\ \;\;\;\;\left(\left(\left(\left(im \cdot im\right) \cdot -0.3333333333333333\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* 0.5 (sin re)) -0.01)
   (*
    (*
     (* (* (* im im) -0.3333333333333333) im)
     (fma (* re re) -0.08333333333333333 0.5))
    re)
   (* (* (* (sinh im) 2.0) re) -0.5)))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(re)) <= -0.01) {
		tmp = ((((im * im) * -0.3333333333333333) * im) * fma((re * re), -0.08333333333333333, 0.5)) * re;
	} else {
		tmp = ((sinh(im) * 2.0) * re) * -0.5;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= -0.01)
		tmp = Float64(Float64(Float64(Float64(Float64(im * im) * -0.3333333333333333) * im) * fma(Float64(re * re), -0.08333333333333333, 0.5)) * re);
	else
		tmp = Float64(Float64(Float64(sinh(im) * 2.0) * re) * -0.5);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], -0.01], N[(N[(N[(N[(N[(im * im), $MachinePrecision] * -0.3333333333333333), $MachinePrecision] * im), $MachinePrecision] * N[(N[(re * re), $MachinePrecision] * -0.08333333333333333 + 0.5), $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision], N[(N[(N[(N[Sinh[im], $MachinePrecision] * 2.0), $MachinePrecision] * re), $MachinePrecision] * -0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0100000000000000002

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in im around 0

      \[\leadsto \left(\left(im \cdot \left(\frac{-1}{3} \cdot {im}^{2} - 2\right)\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]
   6. Step-by-step derivation

      1. sinh-undef-rev N/A

         \[\leadsto \left(\left(im \cdot \left(\frac{-1}{3} \cdot {im}^{2} - 2\right)\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      2. sub-negate-rev N/A

         \[\leadsto \left(\left(im \cdot \left(\frac{-1}{3} \cdot {im}^{2} - 2\right)\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      3. *-commutative N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} - 2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} - 2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      5. sub-flip N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} + \left(\mathsf{neg}\left(2\right)\right)\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      6. metadata-eval N/A

         \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2} + -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, {im}^{2}, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      8. pow2 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, im \cdot im, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      9. lift-*.f64 54.0

         \[\leadsto \left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   7. Applied rewrites 54.0%

      \[\leadsto \left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   8. Taylor expanded in im around inf

      \[\leadsto \left(\left(\left(\frac{-1}{3} \cdot {im}^{2}\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(\left({im}^{2} \cdot \frac{-1}{3}\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(\left({im}^{2} \cdot \frac{-1}{3}\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      3. pow2 N/A

         \[\leadsto \left(\left(\left(\left(im \cdot im\right) \cdot \frac{-1}{3}\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      4. lift-*.f64 42.6

         \[\leadsto \left(\left(\left(\left(im \cdot im\right) \cdot -0.3333333333333333\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   10. Applied rewrites 42.6%

       \[\leadsto \left(\left(\left(\left(im \cdot im\right) \cdot -0.3333333333333333\right) \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   if -0.0100000000000000002 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \sin re\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      2. lift-sin.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \color{blue}{\sin re}\right) \cdot \left(e^{-im} - e^{im}\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      5. lift-sin.f64 65.7

         \[\leadsto \left(\color{blue}{\sin re} \cdot 0.5\right) \cdot \left(e^{-im} - e^{im}\right) \]

      6. lift--.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(e^{-im} - e^{im}\right)} \]

      7. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\color{blue}{e^{-im}} - e^{im}\right) \]

      8. lift-neg.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\color{blue}{\mathsf{neg}\left(im\right)}} - e^{im}\right) \]

      9. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - \color{blue}{e^{im}}\right) \]

      10. sub-negate-rev N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right)} \]

      11. lower-neg.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)} \]

      12. sinh-undef N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      14. lower-sinh.f64 99.9

          \[\leadsto \left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \color{blue}{\sinh im}\right) \]

   3. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \sinh im\right)} \]

   4. Taylor expanded in re around 0

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

      1. *-commutative N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      2. sinh-undef-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      3. sub-negate-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      4. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      6. *-commutative N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      8. rec-exp N/A

         \[\leadsto \left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      9. sinh-undef-rev N/A

         \[\leadsto \left(\left(2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{-1}{2} \]

      10. *-commutative N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      11. lower-*.f64 N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      12. lift-sinh.f64 63.3

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5 \]

   6. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 61.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin re \leq -0.01:\\ \;\;\;\;\left(\left(\mathsf{fma}\left(re \cdot re, -0.16666666666666666, 1\right) \cdot re\right) \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* 0.5 (sin re)) -0.01)
   (*
    (*
     (* (fma (* re re) -0.16666666666666666 1.0) re)
     (* (* im im) -0.16666666666666666))
    im)
   (* (* (* (sinh im) 2.0) re) -0.5)))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(re)) <= -0.01) {
		tmp = ((fma((re * re), -0.16666666666666666, 1.0) * re) * ((im * im) * -0.16666666666666666)) * im;
	} else {
		tmp = ((sinh(im) * 2.0) * re) * -0.5;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= -0.01)
		tmp = Float64(Float64(Float64(fma(Float64(re * re), -0.16666666666666666, 1.0) * re) * Float64(Float64(im * im) * -0.16666666666666666)) * im);
	else
		tmp = Float64(Float64(Float64(sinh(im) * 2.0) * re) * -0.5);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], -0.01], N[(N[(N[(N[(N[(re * re), $MachinePrecision] * -0.16666666666666666 + 1.0), $MachinePrecision] * re), $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.16666666666666666), $MachinePrecision]), $MachinePrecision] * im), $MachinePrecision], N[(N[(N[(N[Sinh[im], $MachinePrecision] * 2.0), $MachinePrecision] * re), $MachinePrecision] * -0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0100000000000000002

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right) + -1 \cdot \sin re\right) \cdot im \]

      4. associate-*r* N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {im}^{2}\right) \cdot \sin re + -1 \cdot \sin re\right) \cdot im \]

      5. distribute-rgt-out N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      7. lift-sin.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      8. lower-fma.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, {im}^{2}, -1\right)\right) \cdot im \]

      9. unpow2 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      10. lower-*.f64 80.8

          \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{\left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im} \]

   5. Taylor expanded in im around inf

      \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2}\right)\right) \cdot im \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      3. pow2 N/A

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      4. lift-*.f64 45.6

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   7. Applied rewrites 45.6%

      \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   8. Taylor expanded in re around 0

      \[\leadsto \left(\left(re \cdot \left(1 + \frac{-1}{6} \cdot {re}^{2}\right)\right) \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(\left(1 + \frac{-1}{6} \cdot {re}^{2}\right) \cdot re\right) \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(\left(1 + \frac{-1}{6} \cdot {re}^{2}\right) \cdot re\right) \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      3. +-commutative N/A

         \[\leadsto \left(\left(\left(\frac{-1}{6} \cdot {re}^{2} + 1\right) \cdot re\right) \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      4. *-commutative N/A

         \[\leadsto \left(\left(\left({re}^{2} \cdot \frac{-1}{6} + 1\right) \cdot re\right) \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      5. lower-fma.f64 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left({re}^{2}, \frac{-1}{6}, 1\right) \cdot re\right) \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      6. pow2 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(re \cdot re, \frac{-1}{6}, 1\right) \cdot re\right) \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      7. lift-*.f64 39.4

         \[\leadsto \left(\left(\mathsf{fma}\left(re \cdot re, -0.16666666666666666, 1\right) \cdot re\right) \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   10. Applied rewrites 39.4%

       \[\leadsto \left(\left(\mathsf{fma}\left(re \cdot re, -0.16666666666666666, 1\right) \cdot re\right) \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   if -0.0100000000000000002 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \sin re\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      2. lift-sin.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \color{blue}{\sin re}\right) \cdot \left(e^{-im} - e^{im}\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      5. lift-sin.f64 65.7

         \[\leadsto \left(\color{blue}{\sin re} \cdot 0.5\right) \cdot \left(e^{-im} - e^{im}\right) \]

      6. lift--.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(e^{-im} - e^{im}\right)} \]

      7. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\color{blue}{e^{-im}} - e^{im}\right) \]

      8. lift-neg.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\color{blue}{\mathsf{neg}\left(im\right)}} - e^{im}\right) \]

      9. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - \color{blue}{e^{im}}\right) \]

      10. sub-negate-rev N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right)} \]

      11. lower-neg.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)} \]

      12. sinh-undef N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      14. lower-sinh.f64 99.9

          \[\leadsto \left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \color{blue}{\sinh im}\right) \]

   3. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \sinh im\right)} \]

   4. Taylor expanded in re around 0

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

      1. *-commutative N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      2. sinh-undef-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      3. sub-negate-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      4. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      6. *-commutative N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      8. rec-exp N/A

         \[\leadsto \left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      9. sinh-undef-rev N/A

         \[\leadsto \left(\left(2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{-1}{2} \]

      10. *-commutative N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      11. lower-*.f64 N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      12. lift-sinh.f64 63.3

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5 \]

   6. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 61.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin re \leq -0.01:\\ \;\;\;\;\left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* 0.5 (sin re)) -0.01)
   (* (* (* -2.0 im) (fma (* re re) -0.08333333333333333 0.5)) re)
   (* (* (* (sinh im) 2.0) re) -0.5)))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(re)) <= -0.01) {
		tmp = ((-2.0 * im) * fma((re * re), -0.08333333333333333, 0.5)) * re;
	} else {
		tmp = ((sinh(im) * 2.0) * re) * -0.5;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= -0.01)
		tmp = Float64(Float64(Float64(-2.0 * im) * fma(Float64(re * re), -0.08333333333333333, 0.5)) * re);
	else
		tmp = Float64(Float64(Float64(sinh(im) * 2.0) * re) * -0.5);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], -0.01], N[(N[(N[(-2.0 * im), $MachinePrecision] * N[(N[(re * re), $MachinePrecision] * -0.08333333333333333 + 0.5), $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision], N[(N[(N[(N[Sinh[im], $MachinePrecision] * 2.0), $MachinePrecision] * re), $MachinePrecision] * -0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0100000000000000002

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in im around 0

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]
   6. Step-by-step derivation

      1. sinh-undef-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      2. sub-negate-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      3. lower-*.f64 35.9

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   7. Applied rewrites 35.9%

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   if -0.0100000000000000002 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \sin re\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      2. lift-sin.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \color{blue}{\sin re}\right) \cdot \left(e^{-im} - e^{im}\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\sin re \cdot \frac{1}{2}\right)} \cdot \left(e^{-im} - e^{im}\right) \]

      5. lift-sin.f64 65.7

         \[\leadsto \left(\color{blue}{\sin re} \cdot 0.5\right) \cdot \left(e^{-im} - e^{im}\right) \]

      6. lift--.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(e^{-im} - e^{im}\right)} \]

      7. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(\color{blue}{e^{-im}} - e^{im}\right) \]

      8. lift-neg.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\color{blue}{\mathsf{neg}\left(im\right)}} - e^{im}\right) \]

      9. lift-exp.f64 N/A

         \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(e^{\mathsf{neg}\left(im\right)} - \color{blue}{e^{im}}\right) \]

      10. sub-negate-rev N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right)} \]

      11. lower-neg.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)} \]

      12. sinh-undef N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \left(\sin re \cdot \frac{1}{2}\right) \cdot \left(-\color{blue}{2 \cdot \sinh im}\right) \]

      14. lower-sinh.f64 99.9

          \[\leadsto \left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \color{blue}{\sinh im}\right) \]

   3. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left(\sin re \cdot 0.5\right) \cdot \left(-2 \cdot \sinh im\right)} \]

   4. Taylor expanded in re around 0

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

      1. *-commutative N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      2. sinh-undef-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      3. sub-negate-rev N/A

         \[\leadsto \frac{-1}{2} \cdot \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \]

      4. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{im} - \frac{1}{e^{im}}\right)\right) \cdot \color{blue}{\frac{-1}{2}} \]

      6. *-commutative N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{im} - \frac{1}{e^{im}}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      8. rec-exp N/A

         \[\leadsto \left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right) \cdot re\right) \cdot \frac{-1}{2} \]

      9. sinh-undef-rev N/A

         \[\leadsto \left(\left(2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{-1}{2} \]

      10. *-commutative N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      11. lower-*.f64 N/A

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot \frac{-1}{2} \]

      12. lift-sinh.f64 63.3

          \[\leadsto \left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5 \]

   6. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(\sinh im \cdot 2\right) \cdot re\right) \cdot -0.5} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 13: 52.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \leq -1 \cdot 10^{-194}:\\ \;\;\;\;\left(\left(1 - e^{im}\right) \cdot re\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (* 0.5 (sin re)) (- (exp (- im)) (exp im))) -1e-194)
   (* (* (- 1.0 (exp im)) re) 0.5)
   (* (* (* -2.0 im) (fma (* re re) -0.08333333333333333 0.5)) re)))

C

double code(double re, double im) {
	double tmp;
	if (((0.5 * sin(re)) * (exp(-im) - exp(im))) <= -1e-194) {
		tmp = ((1.0 - exp(im)) * re) * 0.5;
	} else {
		tmp = ((-2.0 * im) * fma((re * re), -0.08333333333333333, 0.5)) * re;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(-im)) - exp(im))) <= -1e-194)
		tmp = Float64(Float64(Float64(1.0 - exp(im)) * re) * 0.5);
	else
		tmp = Float64(Float64(Float64(-2.0 * im) * fma(Float64(re * re), -0.08333333333333333, 0.5)) * re);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[(-im)], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -1e-194], N[(N[(N[(1.0 - N[Exp[im], $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision] * 0.5), $MachinePrecision], N[(N[(N[(-2.0 * im), $MachinePrecision] * N[(N[(re * re), $MachinePrecision] * -0.08333333333333333 + 0.5), $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < -1.00000000000000002e-194

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      5. sub-negate-rev N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      6. lower-neg.f64 N/A

         \[\leadsto \left(\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      7. sinh-undef N/A

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

      8. lower-*.f64 N/A

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

      9. lower-sinh.f64 63.3

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5 \]

   4. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5} \]
   5. Step-by-step derivation

      1. lift-neg.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(2 \cdot \sinh im\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(2 \cdot \sinh im\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      3. lift-sinh.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(2 \cdot \sinh im\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      4. sinh-undef-rev N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      5. sub-negate-rev N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      6. lift-neg.f64 N/A

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      7. lift-exp.f64 N/A

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      8. lift-exp.f64 N/A

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      9. lift--.f64 52.8

         \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot 0.5 \]

   6. Applied rewrites 52.8%

      \[\leadsto \left(\left(e^{-im} - e^{im}\right) \cdot re\right) \cdot 0.5 \]

   7. Taylor expanded in im around 0

      \[\leadsto \left(\left(1 - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]
   8. Step-by-step derivation

   9. Applied rewrites 33.6%

      \[\leadsto \left(\left(1 - e^{im}\right) \cdot re\right) \cdot 0.5 \]

   if -1.00000000000000002e-194 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im)))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in im around 0

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]
   6. Step-by-step derivation

      1. sinh-undef-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      2. sub-negate-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      3. lower-*.f64 35.9

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   7. Applied rewrites 35.9%

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 44.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin re \leq -0.01:\\ \;\;\;\;\left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot re\right) \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* 0.5 (sin re)) -0.01)
   (* (* (* -2.0 im) (fma (* re re) -0.08333333333333333 0.5)) re)
   (* (* (* (fma -0.3333333333333333 (* im im) -2.0) im) re) 0.5)))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(re)) <= -0.01) {
		tmp = ((-2.0 * im) * fma((re * re), -0.08333333333333333, 0.5)) * re;
	} else {
		tmp = ((fma(-0.3333333333333333, (im * im), -2.0) * im) * re) * 0.5;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= -0.01)
		tmp = Float64(Float64(Float64(-2.0 * im) * fma(Float64(re * re), -0.08333333333333333, 0.5)) * re);
	else
		tmp = Float64(Float64(Float64(fma(-0.3333333333333333, Float64(im * im), -2.0) * im) * re) * 0.5);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], -0.01], N[(N[(N[(-2.0 * im), $MachinePrecision] * N[(N[(re * re), $MachinePrecision] * -0.08333333333333333 + 0.5), $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision], N[(N[(N[(N[(-0.3333333333333333 * N[(im * im), $MachinePrecision] + -2.0), $MachinePrecision] * im), $MachinePrecision] * re), $MachinePrecision] * 0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0100000000000000002

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in im around 0

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]
   6. Step-by-step derivation

      1. sinh-undef-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      2. sub-negate-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      3. lower-*.f64 35.9

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   7. Applied rewrites 35.9%

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   if -0.0100000000000000002 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      5. sub-negate-rev N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      6. lower-neg.f64 N/A

         \[\leadsto \left(\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      7. sinh-undef N/A

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

      8. lower-*.f64 N/A

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

      9. lower-sinh.f64 63.3

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5 \]

   4. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5} \]

   5. Taylor expanded in im around 0

      \[\leadsto \left(\left(im \cdot \left(\frac{-1}{3} \cdot {im}^{2} - 2\right)\right) \cdot re\right) \cdot \frac{1}{2} \]
   6. Step-by-step derivation

      1. sinh-undef-rev N/A

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

      2. sub-negate-rev N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. sub-flip N/A

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

      6. metadata-eval N/A

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

      7. lower-fma.f64 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, {im}^{2}, -2\right) \cdot im\right) \cdot re\right) \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \left(\left(\mathsf{fma}\left(\frac{-1}{3}, im \cdot im, -2\right) \cdot im\right) \cdot re\right) \cdot \frac{1}{2} \]

      9. lift-*.f64 53.4

         \[\leadsto \left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot re\right) \cdot 0.5 \]

   7. Applied rewrites 53.4%

      \[\leadsto \left(\left(\mathsf{fma}\left(-0.3333333333333333, im \cdot im, -2\right) \cdot im\right) \cdot re\right) \cdot 0.5 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 15: 44.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right)\\ \mathbf{if}\;t\_0 \leq -0.005:\\ \;\;\;\;re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right)\\ \mathbf{elif}\;t\_0 \leq 0:\\ \;\;\;\;\left(im \cdot re\right) \cdot \mathsf{fma}\left(im \cdot im, -0.16666666666666666, -1\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-2 \cdot im\right) \cdot \left(\left(re \cdot re\right) \cdot -0.08333333333333333\right)\right) \cdot re\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (* 0.5 (sin re)) (- (exp (- im)) (exp im)))))
   (if (<= t_0 -0.005)
     (* re (* (* (* im im) -0.16666666666666666) im))
     (if (<= t_0 0.0)
       (* (* im re) (fma (* im im) -0.16666666666666666 -1.0))
       (* (* (* -2.0 im) (* (* re re) -0.08333333333333333)) re)))))

C

double code(double re, double im) {
	double t_0 = (0.5 * sin(re)) * (exp(-im) - exp(im));
	double tmp;
	if (t_0 <= -0.005) {
		tmp = re * (((im * im) * -0.16666666666666666) * im);
	} else if (t_0 <= 0.0) {
		tmp = (im * re) * fma((im * im), -0.16666666666666666, -1.0);
	} else {
		tmp = ((-2.0 * im) * ((re * re) * -0.08333333333333333)) * re;
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(-im)) - exp(im)))
	tmp = 0.0
	if (t_0 <= -0.005)
		tmp = Float64(re * Float64(Float64(Float64(im * im) * -0.16666666666666666) * im));
	elseif (t_0 <= 0.0)
		tmp = Float64(Float64(im * re) * fma(Float64(im * im), -0.16666666666666666, -1.0));
	else
		tmp = Float64(Float64(Float64(-2.0 * im) * Float64(Float64(re * re) * -0.08333333333333333)) * re);
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[(-im)], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -0.005], N[(re * N[(N[(N[(im * im), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 0.0], N[(N[(im * re), $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.16666666666666666 + -1.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(-2.0 * im), $MachinePrecision] * N[(N[(re * re), $MachinePrecision] * -0.08333333333333333), $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < -0.0050000000000000001

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right) + -1 \cdot \sin re\right) \cdot im \]

      4. associate-*r* N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {im}^{2}\right) \cdot \sin re + -1 \cdot \sin re\right) \cdot im \]

      5. distribute-rgt-out N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      7. lift-sin.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      8. lower-fma.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, {im}^{2}, -1\right)\right) \cdot im \]

      9. unpow2 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      10. lower-*.f64 80.8

          \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{\left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im} \]

   5. Taylor expanded in im around inf

      \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2}\right)\right) \cdot im \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      3. pow2 N/A

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      4. lift-*.f64 45.6

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   7. Applied rewrites 45.6%

      \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   8. Taylor expanded in re around 0

      \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]
   9. Step-by-step derivation

   10. Applied rewrites 39.4%

       \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]
   11. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot \color{blue}{im} \]

       2. lift-*.f64 N/A

          \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

       3. associate-*l* N/A

          \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right) \cdot im\right)} \]

       4. lower-*.f64 N/A

          \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right) \cdot im\right)} \]

       5. lower-*.f64 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot \color{blue}{im}\right) \]

       6. pow2 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

       7. *-commutative 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

       8. pow2 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

   12. Applied rewrites 42.4%

       \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right)} \]

   if -0.0050000000000000001 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < 0.0

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right) + -1 \cdot \sin re\right) \cdot im \]

      4. associate-*r* N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {im}^{2}\right) \cdot \sin re + -1 \cdot \sin re\right) \cdot im \]

      5. distribute-rgt-out N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      7. lift-sin.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      8. lower-fma.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, {im}^{2}, -1\right)\right) \cdot im \]

      9. unpow2 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      10. lower-*.f64 80.8

          \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{\left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot \color{blue}{im} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      3. lift-sin.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      4. lift-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      5. lift-fma.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot \left(im \cdot im\right) + -1\right)\right) \cdot im \]

      6. *-commutative N/A

         \[\leadsto im \cdot \color{blue}{\left(\sin re \cdot \left(\frac{-1}{6} \cdot \left(im \cdot im\right) + -1\right)\right)} \]

      7. add-flip N/A

         \[\leadsto im \cdot \left(\sin re \cdot \left(\frac{-1}{6} \cdot \left(im \cdot im\right) - \color{blue}{\left(\mathsf{neg}\left(-1\right)\right)}\right)\right) \]

      8. pow2 N/A

         \[\leadsto im \cdot \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} - \left(\mathsf{neg}\left(-1\right)\right)\right)\right) \]

      9. metadata-eval N/A

         \[\leadsto im \cdot \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} - 1\right)\right) \]

      10. associate-*r* N/A

          \[\leadsto \left(im \cdot \sin re\right) \cdot \color{blue}{\left(\frac{-1}{6} \cdot {im}^{2} - 1\right)} \]

      11. lower-*.f64 N/A

          \[\leadsto \left(im \cdot \sin re\right) \cdot \color{blue}{\left(\frac{-1}{6} \cdot {im}^{2} - 1\right)} \]

      12. *-commutative N/A

          \[\leadsto \left(\sin re \cdot im\right) \cdot \left(\color{blue}{\frac{-1}{6} \cdot {im}^{2}} - 1\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \left(\sin re \cdot im\right) \cdot \left(\color{blue}{\frac{-1}{6} \cdot {im}^{2}} - 1\right) \]

      14. lift-sin.f64 N/A

          \[\leadsto \left(\sin re \cdot im\right) \cdot \left(\color{blue}{\frac{-1}{6}} \cdot {im}^{2} - 1\right) \]

      15. sub-flip N/A

          \[\leadsto \left(\sin re \cdot im\right) \cdot \left(\frac{-1}{6} \cdot {im}^{2} + \color{blue}{\left(\mathsf{neg}\left(1\right)\right)}\right) \]

      16. *-commutative N/A

          \[\leadsto \left(\sin re \cdot im\right) \cdot \left({im}^{2} \cdot \frac{-1}{6} + \left(\mathsf{neg}\left(\color{blue}{1}\right)\right)\right) \]

      17. metadata-eval N/A

          \[\leadsto \left(\sin re \cdot im\right) \cdot \left({im}^{2} \cdot \frac{-1}{6} + -1\right) \]

      18. lower-fma.f64 N/A

          \[\leadsto \left(\sin re \cdot im\right) \cdot \mathsf{fma}\left({im}^{2}, \color{blue}{\frac{-1}{6}}, -1\right) \]

      19. pow2 N/A

          \[\leadsto \left(\sin re \cdot im\right) \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{6}, -1\right) \]

      20. lift-*.f64 80.8

          \[\leadsto \left(\sin re \cdot im\right) \cdot \mathsf{fma}\left(im \cdot im, -0.16666666666666666, -1\right) \]

   6. Applied rewrites 80.8%

      \[\leadsto \left(\sin re \cdot im\right) \cdot \color{blue}{\mathsf{fma}\left(im \cdot im, -0.16666666666666666, -1\right)} \]

   7. Taylor expanded in re around 0

      \[\leadsto \left(im \cdot re\right) \cdot \mathsf{fma}\left(\color{blue}{im \cdot im}, \frac{-1}{6}, -1\right) \]
   8. Step-by-step derivation

      1. lower-*.f64 50.4

         \[\leadsto \left(im \cdot re\right) \cdot \mathsf{fma}\left(im \cdot \color{blue}{im}, -0.16666666666666666, -1\right) \]

   9. Applied rewrites 50.4%

      \[\leadsto \left(im \cdot re\right) \cdot \mathsf{fma}\left(\color{blue}{im \cdot im}, -0.16666666666666666, -1\right) \]

   if 0.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im)))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in im around 0

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]
   6. Step-by-step derivation

      1. sinh-undef-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      2. sub-negate-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      3. lower-*.f64 35.9

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   7. Applied rewrites 35.9%

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   8. Taylor expanded in re around inf

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \left(\frac{-1}{12} \cdot {re}^{2}\right)\right) \cdot re \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \left({re}^{2} \cdot \frac{-1}{12}\right)\right) \cdot re \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \left({re}^{2} \cdot \frac{-1}{12}\right)\right) \cdot re \]

      3. pow2 N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \left(\left(re \cdot re\right) \cdot \frac{-1}{12}\right)\right) \cdot re \]

      4. lift-*.f64 23.6

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \left(\left(re \cdot re\right) \cdot -0.08333333333333333\right)\right) \cdot re \]

   10. Applied rewrites 23.6%

       \[\leadsto \left(\left(-2 \cdot im\right) \cdot \left(\left(re \cdot re\right) \cdot -0.08333333333333333\right)\right) \cdot re \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 16: 43.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \leq -1 \cdot 10^{-194}:\\ \;\;\;\;re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (* 0.5 (sin re)) (- (exp (- im)) (exp im))) -1e-194)
   (* re (* (* (* im im) -0.16666666666666666) im))
   (* (* (* -2.0 im) (fma (* re re) -0.08333333333333333 0.5)) re)))

C

double code(double re, double im) {
	double tmp;
	if (((0.5 * sin(re)) * (exp(-im) - exp(im))) <= -1e-194) {
		tmp = re * (((im * im) * -0.16666666666666666) * im);
	} else {
		tmp = ((-2.0 * im) * fma((re * re), -0.08333333333333333, 0.5)) * re;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(-im)) - exp(im))) <= -1e-194)
		tmp = Float64(re * Float64(Float64(Float64(im * im) * -0.16666666666666666) * im));
	else
		tmp = Float64(Float64(Float64(-2.0 * im) * fma(Float64(re * re), -0.08333333333333333, 0.5)) * re);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[(-im)], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -1e-194], N[(re * N[(N[(N[(im * im), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision], N[(N[(N[(-2.0 * im), $MachinePrecision] * N[(N[(re * re), $MachinePrecision] * -0.08333333333333333 + 0.5), $MachinePrecision]), $MachinePrecision] * re), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < -1.00000000000000002e-194

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right) + -1 \cdot \sin re\right) \cdot im \]

      4. associate-*r* N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {im}^{2}\right) \cdot \sin re + -1 \cdot \sin re\right) \cdot im \]

      5. distribute-rgt-out N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      7. lift-sin.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      8. lower-fma.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, {im}^{2}, -1\right)\right) \cdot im \]

      9. unpow2 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      10. lower-*.f64 80.8

          \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{\left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im} \]

   5. Taylor expanded in im around inf

      \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2}\right)\right) \cdot im \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      3. pow2 N/A

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      4. lift-*.f64 45.6

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   7. Applied rewrites 45.6%

      \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   8. Taylor expanded in re around 0

      \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]
   9. Step-by-step derivation

   10. Applied rewrites 39.4%

       \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]
   11. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot \color{blue}{im} \]

       2. lift-*.f64 N/A

          \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

       3. associate-*l* N/A

          \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right) \cdot im\right)} \]

       4. lower-*.f64 N/A

          \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right) \cdot im\right)} \]

       5. lower-*.f64 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot \color{blue}{im}\right) \]

       6. pow2 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

       7. *-commutative 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

       8. pow2 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

   12. Applied rewrites 42.4%

       \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right)} \]

   if -1.00000000000000002e-194 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im)))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in im around 0

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]
   6. Step-by-step derivation

      1. sinh-undef-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      2. sub-negate-rev N/A

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right)\right) \cdot re \]

      3. lower-*.f64 35.9

         \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]

   7. Applied rewrites 35.9%

      \[\leadsto \left(\left(-2 \cdot im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 17: 42.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \leq -1 \cdot 10^{-194}:\\ \;\;\;\;re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right) \cdot re\right) \cdot im\right) \cdot -2\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (* 0.5 (sin re)) (- (exp (- im)) (exp im))) -1e-194)
   (* re (* (* (* im im) -0.16666666666666666) im))
   (* (* (* (fma (* re re) -0.08333333333333333 0.5) re) im) -2.0)))

C

double code(double re, double im) {
	double tmp;
	if (((0.5 * sin(re)) * (exp(-im) - exp(im))) <= -1e-194) {
		tmp = re * (((im * im) * -0.16666666666666666) * im);
	} else {
		tmp = ((fma((re * re), -0.08333333333333333, 0.5) * re) * im) * -2.0;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(-im)) - exp(im))) <= -1e-194)
		tmp = Float64(re * Float64(Float64(Float64(im * im) * -0.16666666666666666) * im));
	else
		tmp = Float64(Float64(Float64(fma(Float64(re * re), -0.08333333333333333, 0.5) * re) * im) * -2.0);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[(-im)], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -1e-194], N[(re * N[(N[(N[(im * im), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(N[(re * re), $MachinePrecision] * -0.08333333333333333 + 0.5), $MachinePrecision] * re), $MachinePrecision] * im), $MachinePrecision] * -2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < -1.00000000000000002e-194

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right) + -1 \cdot \sin re\right) \cdot im \]

      4. associate-*r* N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {im}^{2}\right) \cdot \sin re + -1 \cdot \sin re\right) \cdot im \]

      5. distribute-rgt-out N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      7. lift-sin.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      8. lower-fma.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, {im}^{2}, -1\right)\right) \cdot im \]

      9. unpow2 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      10. lower-*.f64 80.8

          \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{\left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im} \]

   5. Taylor expanded in im around inf

      \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2}\right)\right) \cdot im \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      3. pow2 N/A

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      4. lift-*.f64 45.6

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   7. Applied rewrites 45.6%

      \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   8. Taylor expanded in re around 0

      \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]
   9. Step-by-step derivation

   10. Applied rewrites 39.4%

       \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]
   11. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot \color{blue}{im} \]

       2. lift-*.f64 N/A

          \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

       3. associate-*l* N/A

          \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right) \cdot im\right)} \]

       4. lower-*.f64 N/A

          \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right) \cdot im\right)} \]

       5. lower-*.f64 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot \color{blue}{im}\right) \]

       6. pow2 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

       7. *-commutative 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

       8. pow2 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

   12. Applied rewrites 42.4%

       \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right)} \]

   if -1.00000000000000002e-194 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im)))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{re \cdot \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{12} \cdot \left({re}^{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) + \frac{1}{2} \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{re} \]

   4. Applied rewrites 62.7%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot \mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right)\right) \cdot re} \]

   5. Taylor expanded in im around 0

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

      1. *-commutative N/A

         \[\leadsto \left(im \cdot \left(re \cdot \left(\frac{1}{2} + \frac{-1}{12} \cdot {re}^{2}\right)\right)\right) \cdot -2 \]

      2. lower-*.f64 N/A

         \[\leadsto \left(im \cdot \left(re \cdot \left(\frac{1}{2} + \frac{-1}{12} \cdot {re}^{2}\right)\right)\right) \cdot -2 \]

      3. *-commutative N/A

         \[\leadsto \left(\left(re \cdot \left(\frac{1}{2} + \frac{-1}{12} \cdot {re}^{2}\right)\right) \cdot im\right) \cdot -2 \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(re \cdot \left(\frac{1}{2} + \frac{-1}{12} \cdot {re}^{2}\right)\right) \cdot im\right) \cdot -2 \]

      5. *-commutative N/A

         \[\leadsto \left(\left(\left(\frac{1}{2} + \frac{-1}{12} \cdot {re}^{2}\right) \cdot re\right) \cdot im\right) \cdot -2 \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\left(\left(\frac{1}{2} + \frac{-1}{12} \cdot {re}^{2}\right) \cdot re\right) \cdot im\right) \cdot -2 \]

      7. *-commutative N/A

         \[\leadsto \left(\left(\left(\frac{1}{2} + {re}^{2} \cdot \frac{-1}{12}\right) \cdot re\right) \cdot im\right) \cdot -2 \]

      8. pow2 N/A

         \[\leadsto \left(\left(\left(\frac{1}{2} + \left(re \cdot re\right) \cdot \frac{-1}{12}\right) \cdot re\right) \cdot im\right) \cdot -2 \]

      9. +-commutative N/A

         \[\leadsto \left(\left(\left(\left(re \cdot re\right) \cdot \frac{-1}{12} + \frac{1}{2}\right) \cdot re\right) \cdot im\right) \cdot -2 \]

      10. lift-fma.f64 N/A

          \[\leadsto \left(\left(\mathsf{fma}\left(re \cdot re, \frac{-1}{12}, \frac{1}{2}\right) \cdot re\right) \cdot im\right) \cdot -2 \]

      11. lift-*.f64 35.8

          \[\leadsto \left(\left(\mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right) \cdot re\right) \cdot im\right) \cdot -2 \]

   7. Applied rewrites 35.8%

      \[\leadsto \left(\left(\mathsf{fma}\left(re \cdot re, -0.08333333333333333, 0.5\right) \cdot re\right) \cdot im\right) \cdot \color{blue}{-2} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 18: 39.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \leq -1 \cdot 10^{-194}:\\ \;\;\;\;re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-im\right) \cdot re\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (* 0.5 (sin re)) (- (exp (- im)) (exp im))) -1e-194)
   (* re (* (* (* im im) -0.16666666666666666) im))
   (* (- im) re)))

C

double code(double re, double im) {
	double tmp;
	if (((0.5 * sin(re)) * (exp(-im) - exp(im))) <= -1e-194) {
		tmp = re * (((im * im) * -0.16666666666666666) * im);
	} else {
		tmp = -im * 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(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if (((0.5d0 * sin(re)) * (exp(-im) - exp(im))) <= (-1d-194)) then
        tmp = re * (((im * im) * (-0.16666666666666666d0)) * im)
    else
        tmp = -im * re
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if (((0.5 * Math.sin(re)) * (Math.exp(-im) - Math.exp(im))) <= -1e-194) {
		tmp = re * (((im * im) * -0.16666666666666666) * im);
	} else {
		tmp = -im * re;
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if ((0.5 * math.sin(re)) * (math.exp(-im) - math.exp(im))) <= -1e-194:
		tmp = re * (((im * im) * -0.16666666666666666) * im)
	else:
		tmp = -im * re
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(-im)) - exp(im))) <= -1e-194)
		tmp = Float64(re * Float64(Float64(Float64(im * im) * -0.16666666666666666) * im));
	else
		tmp = Float64(Float64(-im) * re);
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if (((0.5 * sin(re)) * (exp(-im) - exp(im))) <= -1e-194)
		tmp = re * (((im * im) * -0.16666666666666666) * im);
	else
		tmp = -im * re;
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[(-im)], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -1e-194], N[(re * N[(N[(N[(im * im), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision], N[((-im) * re), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < -1.00000000000000002e-194

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot \sin re + \frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right)\right) \cdot \color{blue}{im} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{-1}{6} \cdot \left({im}^{2} \cdot \sin re\right) + -1 \cdot \sin re\right) \cdot im \]

      4. associate-*r* N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {im}^{2}\right) \cdot \sin re + -1 \cdot \sin re\right) \cdot im \]

      5. distribute-rgt-out N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      7. lift-sin.f64 N/A

         \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2} + -1\right)\right) \cdot im \]

      8. lower-fma.f64 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, {im}^{2}, -1\right)\right) \cdot im \]

      9. unpow2 N/A

         \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(\frac{-1}{6}, im \cdot im, -1\right)\right) \cdot im \]

      10. lower-*.f64 80.8

          \[\leadsto \left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im \]

   4. Applied rewrites 80.8%

      \[\leadsto \color{blue}{\left(\sin re \cdot \mathsf{fma}\left(-0.16666666666666666, im \cdot im, -1\right)\right) \cdot im} \]

   5. Taylor expanded in im around inf

      \[\leadsto \left(\sin re \cdot \left(\frac{-1}{6} \cdot {im}^{2}\right)\right) \cdot im \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\sin re \cdot \left({im}^{2} \cdot \frac{-1}{6}\right)\right) \cdot im \]

      3. pow2 N/A

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

      4. lift-*.f64 45.6

         \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   7. Applied rewrites 45.6%

      \[\leadsto \left(\sin re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]

   8. Taylor expanded in re around 0

      \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]
   9. Step-by-step derivation

   10. Applied rewrites 39.4%

       \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot -0.16666666666666666\right)\right) \cdot im \]
   11. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot \color{blue}{im} \]

       2. lift-*.f64 N/A

          \[\leadsto \left(re \cdot \left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right)\right) \cdot im \]

       3. associate-*l* N/A

          \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right) \cdot im\right)} \]

       4. lower-*.f64 N/A

          \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot \frac{-1}{6}\right) \cdot im\right)} \]

       5. lower-*.f64 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot \color{blue}{im}\right) \]

       6. pow2 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

       7. *-commutative 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

       8. pow2 42.4

          \[\leadsto re \cdot \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \]

   12. Applied rewrites 42.4%

       \[\leadsto re \cdot \color{blue}{\left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right)} \]

   if -1.00000000000000002e-194 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im)))

   1. Initial program 65.7%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

      5. sub-negate-rev N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      6. lower-neg.f64 N/A

         \[\leadsto \left(\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

      7. sinh-undef N/A

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

      8. lower-*.f64 N/A

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

      9. lower-sinh.f64 63.3

         \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5 \]

   4. Applied rewrites 63.3%

      \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5} \]

   5. Taylor expanded in im around 0

      \[\leadsto -1 \cdot \color{blue}{\left(im \cdot re\right)} \]
   6. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \left(-1 \cdot im\right) \cdot re \]

      2. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(im\right)\right) \cdot re \]

      3. lift-neg.f64 N/A

         \[\leadsto \left(-im\right) \cdot re \]

      4. lower-*.f64 32.4

         \[\leadsto \left(-im\right) \cdot re \]

   7. Applied rewrites 32.4%

      \[\leadsto \left(-im\right) \cdot \color{blue}{re} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 19: 32.4% accurate, 12.7× speedup

Math

\[\begin{array}{l} \\ \left(-im\right) \cdot re \end{array} \]

FPCore

(FPCore (re im) :precision binary64 (* (- im) re))

C

double code(double re, double im) {
	return -im * 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(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = -im * re
end function

Java

public static double code(double re, double im) {
	return -im * re;
}

Python

def code(re, im):
	return -im * re

Julia

function code(re, im)
	return Float64(Float64(-im) * re)
end

MATLAB

function tmp = code(re, im)
	tmp = -im * re;
end

Wolfram

code[re_, im_] := N[((-im) * re), $MachinePrecision]

Derivation

1. Initial program 65.7%

   \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{-im} - e^{im}\right) \]

2. Taylor expanded in re around 0

   \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right)} \]
3. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

   2. lower-*.f64 N/A

      \[\leadsto \left(re \cdot \left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

   3. *-commutative N/A

      \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

   4. lower-*.f64 N/A

      \[\leadsto \left(\left(e^{\mathsf{neg}\left(im\right)} - e^{im}\right) \cdot re\right) \cdot \frac{1}{2} \]

   5. sub-negate-rev N/A

      \[\leadsto \left(\left(\mathsf{neg}\left(\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

   6. lower-neg.f64 N/A

      \[\leadsto \left(\left(-\left(e^{im} - e^{\mathsf{neg}\left(im\right)}\right)\right) \cdot re\right) \cdot \frac{1}{2} \]

   7. sinh-undef N/A

      \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

   8. lower-*.f64 N/A

      \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot \frac{1}{2} \]

   9. lower-sinh.f64 63.3

      \[\leadsto \left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5 \]

4. Applied rewrites 63.3%

   \[\leadsto \color{blue}{\left(\left(-2 \cdot \sinh im\right) \cdot re\right) \cdot 0.5} \]

5. Taylor expanded in im around 0

   \[\leadsto -1 \cdot \color{blue}{\left(im \cdot re\right)} \]
6. Step-by-step derivation

   1. associate-*r* N/A

      \[\leadsto \left(-1 \cdot im\right) \cdot re \]

   2. mul-1-neg N/A

      \[\leadsto \left(\mathsf{neg}\left(im\right)\right) \cdot re \]

   3. lift-neg.f64 N/A

      \[\leadsto \left(-im\right) \cdot re \]

   4. lower-*.f64 32.4

      \[\leadsto \left(-im\right) \cdot re \]

7. Applied rewrites 32.4%

   \[\leadsto \left(-im\right) \cdot \color{blue}{re} \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025134 
(FPCore (re im)
  :name "math.cos on complex, imaginary part"
  :precision binary64
  (* (* 0.5 (sin re)) (- (exp (- im)) (exp im))))