math.exp on complex, imaginary part

Percentage Accurate: 100.0% → 100.0%

Time: 3.3s

Alternatives: 15

Speedup: 1.0×

• 24.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ e^{re} \cdot \sin im \end{array} \]

FPCore

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

C

double code(double re, double im) {
	return exp(re) * sin(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 = exp(re) * sin(im)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ e^{re} \cdot \sin im \end{array} \]

FPCore

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

C

double code(double re, double im) {
	return exp(re) * sin(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 = exp(re) * sin(im)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ e^{re} \cdot \sin im \end{array} \]

FPCore

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

C

double code(double re, double im) {
	return exp(re) * sin(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 = exp(re) * sin(im)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[e^{re} \cdot \sin im \]
2. Add Preprocessing

Alternative 2: 85.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{re} \cdot \sin im\\ t_1 := \left(re - -1\right) \cdot \sin im\\ t_2 := e^{re} \cdot im\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;1 \cdot \left(\mathsf{fma}\left(\sqrt{\left(im \cdot im\right) \cdot \left(im \cdot im\right)}, -0.16666666666666666, 1\right) \cdot im\right)\\ \mathbf{elif}\;t\_0 \leq -0.02:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 5 \cdot 10^{-156}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_0 \leq 1:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (exp re) (sin im)))
        (t_1 (* (- re -1.0) (sin im)))
        (t_2 (* (exp re) im)))
   (if (<= t_0 (- INFINITY))
     (*
      1.0
      (* (fma (sqrt (* (* im im) (* im im))) -0.16666666666666666 1.0) im))
     (if (<= t_0 -0.02)
       t_1
       (if (<= t_0 5e-156) t_2 (if (<= t_0 1.0) t_1 t_2))))))

C

double code(double re, double im) {
	double t_0 = exp(re) * sin(im);
	double t_1 = (re - -1.0) * sin(im);
	double t_2 = exp(re) * im;
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = 1.0 * (fma(sqrt(((im * im) * (im * im))), -0.16666666666666666, 1.0) * im);
	} else if (t_0 <= -0.02) {
		tmp = t_1;
	} else if (t_0 <= 5e-156) {
		tmp = t_2;
	} else if (t_0 <= 1.0) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(exp(re) * sin(im))
	t_1 = Float64(Float64(re - -1.0) * sin(im))
	t_2 = Float64(exp(re) * im)
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(1.0 * Float64(fma(sqrt(Float64(Float64(im * im) * Float64(im * im))), -0.16666666666666666, 1.0) * im));
	elseif (t_0 <= -0.02)
		tmp = t_1;
	elseif (t_0 <= 5e-156)
		tmp = t_2;
	elseif (t_0 <= 1.0)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(re - -1.0), $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], N[(1.0 * N[(N[(N[Sqrt[N[(N[(im * im), $MachinePrecision] * N[(im * im), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * -0.16666666666666666 + 1.0), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, -0.02], t$95$1, If[LessEqual[t$95$0, 5e-156], t$95$2, If[LessEqual[t$95$0, 1.0], t$95$1, t$95$2]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -inf.0

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 60.5

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

   4. Applied rewrites 60.5%

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

   5. Taylor expanded in re around 0

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

   7. Applied rewrites 29.7%

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

      1. lift-*.f64 N/A

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

      2. pow2 N/A

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

      3. fabs-pow2-rev N/A

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

      4. rem-sqrt-square-rev N/A

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

      5. lower-sqrt.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. pow2 N/A

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

      8. lift-*.f64 N/A

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

      9. pow2 N/A

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

      10. lift-*.f64 30.2

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

   9. Applied rewrites 30.2%

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

   if -inf.0 < (*.f64 (exp.f64 re) (sin.f64 im)) < -0.0200000000000000004 or 5.00000000000000007e-156 < (*.f64 (exp.f64 re) (sin.f64 im)) < 1

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in re around 0

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

      1. +-commutative N/A

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

      2. add-flip N/A

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

      3. lower--.f64 N/A

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

      4. metadata-eval 51.6

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

   4. Applied rewrites 51.6%

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

   if -0.0200000000000000004 < (*.f64 (exp.f64 re) (sin.f64 im)) < 5.00000000000000007e-156 or 1 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 85.8% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{re} \cdot \sin im\\ t_1 := e^{re} \cdot im\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;1 \cdot \left(\mathsf{fma}\left(\sqrt{\left(im \cdot im\right) \cdot \left(im \cdot im\right)}, -0.16666666666666666, 1\right) \cdot im\right)\\ \mathbf{elif}\;t\_0 \leq -0.02:\\ \;\;\;\;\sin im\\ \mathbf{elif}\;t\_0 \leq 5 \cdot 10^{-18}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 1:\\ \;\;\;\;\sin im\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (exp re) (sin im))) (t_1 (* (exp re) im)))
   (if (<= t_0 (- INFINITY))
     (*
      1.0
      (* (fma (sqrt (* (* im im) (* im im))) -0.16666666666666666 1.0) im))
     (if (<= t_0 -0.02)
       (sin im)
       (if (<= t_0 5e-18) t_1 (if (<= t_0 1.0) (sin im) t_1))))))

C

double code(double re, double im) {
	double t_0 = exp(re) * sin(im);
	double t_1 = exp(re) * im;
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = 1.0 * (fma(sqrt(((im * im) * (im * im))), -0.16666666666666666, 1.0) * im);
	} else if (t_0 <= -0.02) {
		tmp = sin(im);
	} else if (t_0 <= 5e-18) {
		tmp = t_1;
	} else if (t_0 <= 1.0) {
		tmp = sin(im);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(exp(re) * sin(im))
	t_1 = Float64(exp(re) * im)
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(1.0 * Float64(fma(sqrt(Float64(Float64(im * im) * Float64(im * im))), -0.16666666666666666, 1.0) * im));
	elseif (t_0 <= -0.02)
		tmp = sin(im);
	elseif (t_0 <= 5e-18)
		tmp = t_1;
	elseif (t_0 <= 1.0)
		tmp = sin(im);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], N[(1.0 * N[(N[(N[Sqrt[N[(N[(im * im), $MachinePrecision] * N[(im * im), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * -0.16666666666666666 + 1.0), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, -0.02], N[Sin[im], $MachinePrecision], If[LessEqual[t$95$0, 5e-18], t$95$1, If[LessEqual[t$95$0, 1.0], N[Sin[im], $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -inf.0

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 60.5

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

   4. Applied rewrites 60.5%

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

   5. Taylor expanded in re around 0

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

   7. Applied rewrites 29.7%

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

      1. lift-*.f64 N/A

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

      2. pow2 N/A

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

      3. fabs-pow2-rev N/A

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

      4. rem-sqrt-square-rev N/A

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

      5. lower-sqrt.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. pow2 N/A

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

      8. lift-*.f64 N/A

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

      9. pow2 N/A

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

      10. lift-*.f64 30.2

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

   9. Applied rewrites 30.2%

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

   if -inf.0 < (*.f64 (exp.f64 re) (sin.f64 im)) < -0.0200000000000000004 or 5.00000000000000036e-18 < (*.f64 (exp.f64 re) (sin.f64 im)) < 1

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\sin im} \]
   3. Step-by-step derivation

      1. lift-sin.f64 51.0

         \[\leadsto \sin im \]

   4. Applied rewrites 51.0%

      \[\leadsto \color{blue}{\sin im} \]

   if -0.0200000000000000004 < (*.f64 (exp.f64 re) (sin.f64 im)) < 5.00000000000000036e-18 or 1 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 68.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \sin im \leq -0.02:\\ \;\;\;\;e^{re} \cdot \left(\mathsf{fma}\left(im, im \cdot -0.16666666666666666, 1\right) \cdot im\right)\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (sin im)) -0.02)
   (* (exp re) (* (fma im (* im -0.16666666666666666) 1.0) im))
   (* (exp re) im)))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * sin(im)) <= -0.02) {
		tmp = exp(re) * (fma(im, (im * -0.16666666666666666), 1.0) * im);
	} else {
		tmp = exp(re) * im;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * sin(im)) <= -0.02)
		tmp = Float64(exp(re) * Float64(fma(im, Float64(im * -0.16666666666666666), 1.0) * im));
	else
		tmp = Float64(exp(re) * im);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], -0.02], N[(N[Exp[re], $MachinePrecision] * N[(N[(im * N[(im * -0.16666666666666666), $MachinePrecision] + 1.0), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.0200000000000000004

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 60.5

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

   4. Applied rewrites 60.5%

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

      1. lift-*.f64 N/A

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

      2. lift-fma.f64 N/A

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

      3. associate-*l* N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-*.f64 60.5

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

   6. Applied rewrites 60.5%

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

   if -0.0200000000000000004 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 62.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \sin im \leq -0.02:\\ \;\;\;\;e^{re} \cdot \left(\left(\left(im \cdot im\right) \cdot im\right) \cdot -0.16666666666666666\right)\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (sin im)) -0.02)
   (* (exp re) (* (* (* im im) im) -0.16666666666666666))
   (* (exp re) im)))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * sin(im)) <= -0.02) {
		tmp = exp(re) * (((im * im) * im) * -0.16666666666666666);
	} else {
		tmp = exp(re) * im;
	}
	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 ((exp(re) * sin(im)) <= (-0.02d0)) then
        tmp = exp(re) * (((im * im) * im) * (-0.16666666666666666d0))
    else
        tmp = exp(re) * im
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if ((Math.exp(re) * Math.sin(im)) <= -0.02) {
		tmp = Math.exp(re) * (((im * im) * im) * -0.16666666666666666);
	} else {
		tmp = Math.exp(re) * im;
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if (math.exp(re) * math.sin(im)) <= -0.02:
		tmp = math.exp(re) * (((im * im) * im) * -0.16666666666666666)
	else:
		tmp = math.exp(re) * im
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * sin(im)) <= -0.02)
		tmp = Float64(exp(re) * Float64(Float64(Float64(im * im) * im) * -0.16666666666666666));
	else
		tmp = Float64(exp(re) * im);
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if ((exp(re) * sin(im)) <= -0.02)
		tmp = exp(re) * (((im * im) * im) * -0.16666666666666666);
	else
		tmp = exp(re) * im;
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], -0.02], N[(N[Exp[re], $MachinePrecision] * N[(N[(N[(im * im), $MachinePrecision] * im), $MachinePrecision] * -0.16666666666666666), $MachinePrecision]), $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.0200000000000000004

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 60.5

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

   4. Applied rewrites 60.5%

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

   5. Taylor expanded in im around inf

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

      1. *-commutative N/A

         \[\leadsto e^{re} \cdot \left({im}^{3} \cdot \frac{-1}{6}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \left({im}^{3} \cdot \frac{-1}{6}\right) \]

      3. unpow3 N/A

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

      4. pow2 N/A

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

      5. lower-*.f64 N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 24.4

         \[\leadsto e^{re} \cdot \left(\left(\left(im \cdot im\right) \cdot im\right) \cdot -0.16666666666666666\right) \]

   7. Applied rewrites 24.4%

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

   if -0.0200000000000000004 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 61.9% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \sin im \leq -0.02:\\ \;\;\;\;1 \cdot \left(\mathsf{fma}\left(\sqrt{\left(im \cdot im\right) \cdot \left(im \cdot im\right)}, -0.16666666666666666, 1\right) \cdot im\right)\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (sin im)) -0.02)
   (* 1.0 (* (fma (sqrt (* (* im im) (* im im))) -0.16666666666666666 1.0) im))
   (* (exp re) im)))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * sin(im)) <= -0.02) {
		tmp = 1.0 * (fma(sqrt(((im * im) * (im * im))), -0.16666666666666666, 1.0) * im);
	} else {
		tmp = exp(re) * im;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * sin(im)) <= -0.02)
		tmp = Float64(1.0 * Float64(fma(sqrt(Float64(Float64(im * im) * Float64(im * im))), -0.16666666666666666, 1.0) * im));
	else
		tmp = Float64(exp(re) * im);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], -0.02], N[(1.0 * N[(N[(N[Sqrt[N[(N[(im * im), $MachinePrecision] * N[(im * im), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * -0.16666666666666666 + 1.0), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.0200000000000000004

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 60.5

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

   4. Applied rewrites 60.5%

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

   5. Taylor expanded in re around 0

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

   7. Applied rewrites 29.7%

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

      1. lift-*.f64 N/A

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

      2. pow2 N/A

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

      3. fabs-pow2-rev N/A

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

      4. rem-sqrt-square-rev N/A

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

      5. lower-sqrt.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. pow2 N/A

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

      8. lift-*.f64 N/A

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

      9. pow2 N/A

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

      10. lift-*.f64 30.2

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

   9. Applied rewrites 30.2%

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

   if -0.0200000000000000004 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 61.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \sin im \leq -0.02:\\ \;\;\;\;\left(re - -1\right) \cdot \left(\mathsf{fma}\left(im, im \cdot -0.16666666666666666, 1\right) \cdot im\right)\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (sin im)) -0.02)
   (* (- re -1.0) (* (fma im (* im -0.16666666666666666) 1.0) im))
   (* (exp re) im)))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * sin(im)) <= -0.02) {
		tmp = (re - -1.0) * (fma(im, (im * -0.16666666666666666), 1.0) * im);
	} else {
		tmp = exp(re) * im;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * sin(im)) <= -0.02)
		tmp = Float64(Float64(re - -1.0) * Float64(fma(im, Float64(im * -0.16666666666666666), 1.0) * im));
	else
		tmp = Float64(exp(re) * im);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], -0.02], N[(N[(re - -1.0), $MachinePrecision] * N[(N[(im * N[(im * -0.16666666666666666), $MachinePrecision] + 1.0), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.0200000000000000004

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 60.5

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

   4. Applied rewrites 60.5%

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

      1. lift-*.f64 N/A

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

      2. lift-fma.f64 N/A

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

      3. associate-*l* N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-*.f64 60.5

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

   6. Applied rewrites 60.5%

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

   7. Taylor expanded in re around 0

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

      1. +-commutative N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 30.9

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

   9. Applied rewrites 30.9%

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

   if -0.0200000000000000004 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 61.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \sin im \leq -0.02:\\ \;\;\;\;\left(re - -1\right) \cdot \left(\left(\left(im \cdot im\right) \cdot im\right) \cdot -0.16666666666666666\right)\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (sin im)) -0.02)
   (* (- re -1.0) (* (* (* im im) im) -0.16666666666666666))
   (* (exp re) im)))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * sin(im)) <= -0.02) {
		tmp = (re - -1.0) * (((im * im) * im) * -0.16666666666666666);
	} else {
		tmp = exp(re) * im;
	}
	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 ((exp(re) * sin(im)) <= (-0.02d0)) then
        tmp = (re - (-1.0d0)) * (((im * im) * im) * (-0.16666666666666666d0))
    else
        tmp = exp(re) * im
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if ((Math.exp(re) * Math.sin(im)) <= -0.02) {
		tmp = (re - -1.0) * (((im * im) * im) * -0.16666666666666666);
	} else {
		tmp = Math.exp(re) * im;
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if (math.exp(re) * math.sin(im)) <= -0.02:
		tmp = (re - -1.0) * (((im * im) * im) * -0.16666666666666666)
	else:
		tmp = math.exp(re) * im
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * sin(im)) <= -0.02)
		tmp = Float64(Float64(re - -1.0) * Float64(Float64(Float64(im * im) * im) * -0.16666666666666666));
	else
		tmp = Float64(exp(re) * im);
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if ((exp(re) * sin(im)) <= -0.02)
		tmp = (re - -1.0) * (((im * im) * im) * -0.16666666666666666);
	else
		tmp = exp(re) * im;
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], -0.02], N[(N[(re - -1.0), $MachinePrecision] * N[(N[(N[(im * im), $MachinePrecision] * im), $MachinePrecision] * -0.16666666666666666), $MachinePrecision]), $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.0200000000000000004

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 60.5

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

   4. Applied rewrites 60.5%

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

   5. Taylor expanded in im around inf

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

      1. *-commutative N/A

         \[\leadsto e^{re} \cdot \left({im}^{3} \cdot \frac{-1}{6}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \left({im}^{3} \cdot \frac{-1}{6}\right) \]

      3. unpow3 N/A

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

      4. pow2 N/A

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

      5. lower-*.f64 N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 24.4

         \[\leadsto e^{re} \cdot \left(\left(\left(im \cdot im\right) \cdot im\right) \cdot -0.16666666666666666\right) \]

   7. Applied rewrites 24.4%

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

   8. Taylor expanded in re around 0

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

      1. +-commutative N/A

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

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lower--.f64 15.4

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

   10. Applied rewrites 15.4%

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

   if -0.0200000000000000004 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 61.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \sin im \leq -0.02:\\ \;\;\;\;1 \cdot \left(\mathsf{fma}\left(im \cdot im, -0.16666666666666666, 1\right) \cdot im\right)\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (sin im)) -0.02)
   (* 1.0 (* (fma (* im im) -0.16666666666666666 1.0) im))
   (* (exp re) im)))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * sin(im)) <= -0.02) {
		tmp = 1.0 * (fma((im * im), -0.16666666666666666, 1.0) * im);
	} else {
		tmp = exp(re) * im;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * sin(im)) <= -0.02)
		tmp = Float64(1.0 * Float64(fma(Float64(im * im), -0.16666666666666666, 1.0) * im));
	else
		tmp = Float64(exp(re) * im);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], -0.02], N[(1.0 * N[(N[(N[(im * im), $MachinePrecision] * -0.16666666666666666 + 1.0), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.0200000000000000004

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 60.5

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

   4. Applied rewrites 60.5%

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

   5. Taylor expanded in re around 0

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

   7. Applied rewrites 29.7%

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

   if -0.0200000000000000004 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 61.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \sin im \leq -0.02:\\ \;\;\;\;\left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \cdot 1\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (sin im)) -0.02)
   (* (* (* (* im im) -0.16666666666666666) im) 1.0)
   (* (exp re) im)))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * sin(im)) <= -0.02) {
		tmp = (((im * im) * -0.16666666666666666) * im) * 1.0;
	} else {
		tmp = exp(re) * im;
	}
	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 ((exp(re) * sin(im)) <= (-0.02d0)) then
        tmp = (((im * im) * (-0.16666666666666666d0)) * im) * 1.0d0
    else
        tmp = exp(re) * im
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if ((Math.exp(re) * Math.sin(im)) <= -0.02) {
		tmp = (((im * im) * -0.16666666666666666) * im) * 1.0;
	} else {
		tmp = Math.exp(re) * im;
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if (math.exp(re) * math.sin(im)) <= -0.02:
		tmp = (((im * im) * -0.16666666666666666) * im) * 1.0
	else:
		tmp = math.exp(re) * im
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * sin(im)) <= -0.02)
		tmp = Float64(Float64(Float64(Float64(im * im) * -0.16666666666666666) * im) * 1.0);
	else
		tmp = Float64(exp(re) * im);
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if ((exp(re) * sin(im)) <= -0.02)
		tmp = (((im * im) * -0.16666666666666666) * im) * 1.0;
	else
		tmp = exp(re) * im;
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], -0.02], N[(N[(N[(N[(im * im), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * im), $MachinePrecision] * 1.0), $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.0200000000000000004

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 60.5

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

   4. Applied rewrites 60.5%

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

   5. Taylor expanded in re around 0

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

   7. Applied rewrites 29.7%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lift-*.f64 N/A

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

      6. lift-fma.f64 N/A

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

      7. +-commutative N/A

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

      8. pow2 N/A

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

      9. *-commutative N/A

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

      10. *-commutative N/A

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

      11. +-commutative N/A

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

      12. *-commutative N/A

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

      13. pow2 N/A

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

      14. associate-*l* N/A

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

      15. lift-fma.f64 N/A

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

      16. lift-*.f64 N/A

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

      17. lift-*.f64 N/A

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

   9. Applied rewrites 29.7%

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

   10. Taylor expanded in im around inf

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

       1. *-commutative N/A

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

       2. lower-*.f64 N/A

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

       3. pow2 N/A

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

       4. lift-*.f64 14.5

          \[\leadsto \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \cdot 1 \]

   12. Applied rewrites 14.5%

       \[\leadsto \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \cdot 1 \]

   if -0.0200000000000000004 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 31.4% accurate, 0.7× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], 0.0], N[(N[(N[(N[(im * im), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * im), $MachinePrecision] * 1.0), $MachinePrecision], N[(N[(N[(0.5 * re + 1.0), $MachinePrecision] * re + 1.0), $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.0

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 60.5

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

   4. Applied rewrites 60.5%

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

   5. Taylor expanded in re around 0

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

   7. Applied rewrites 29.7%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lift-*.f64 N/A

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

      6. lift-fma.f64 N/A

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

      7. +-commutative N/A

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

      8. pow2 N/A

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

      9. *-commutative N/A

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

      10. *-commutative N/A

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

      11. +-commutative N/A

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

      12. *-commutative N/A

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

      13. pow2 N/A

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

      14. associate-*l* N/A

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

      15. lift-fma.f64 N/A

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

      16. lift-*.f64 N/A

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

      17. lift-*.f64 N/A

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

   9. Applied rewrites 29.7%

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

   10. Taylor expanded in im around inf

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

       1. *-commutative N/A

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

       2. lower-*.f64 N/A

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

       3. pow2 N/A

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

       4. lift-*.f64 14.5

          \[\leadsto \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \cdot 1 \]

   12. Applied rewrites 14.5%

       \[\leadsto \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \cdot 1 \]

   if -0.0 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]

   5. Taylor expanded in re around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. lift-fma.f64 36.6

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.5, re, 1\right), re, 1\right) \cdot im \]

   7. Applied rewrites 36.6%

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

Alternative 12: 29.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{re} \cdot \sin im\\ \mathbf{if}\;t\_0 \leq 0:\\ \;\;\;\;\left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \cdot 1\\ \mathbf{elif}\;t\_0 \leq 0.98:\\ \;\;\;\;1 \cdot im\\ \mathbf{else}:\\ \;\;\;\;\left(\left(re \cdot re\right) \cdot 0.5\right) \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (exp re) (sin im))))
   (if (<= t_0 0.0)
     (* (* (* (* im im) -0.16666666666666666) im) 1.0)
     (if (<= t_0 0.98) (* 1.0 im) (* (* (* re re) 0.5) im)))))

C

double code(double re, double im) {
	double t_0 = exp(re) * sin(im);
	double tmp;
	if (t_0 <= 0.0) {
		tmp = (((im * im) * -0.16666666666666666) * im) * 1.0;
	} else if (t_0 <= 0.98) {
		tmp = 1.0 * im;
	} else {
		tmp = ((re * re) * 0.5) * im;
	}
	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 = exp(re) * sin(im)
    if (t_0 <= 0.0d0) then
        tmp = (((im * im) * (-0.16666666666666666d0)) * im) * 1.0d0
    else if (t_0 <= 0.98d0) then
        tmp = 1.0d0 * im
    else
        tmp = ((re * re) * 0.5d0) * im
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double t_0 = Math.exp(re) * Math.sin(im);
	double tmp;
	if (t_0 <= 0.0) {
		tmp = (((im * im) * -0.16666666666666666) * im) * 1.0;
	} else if (t_0 <= 0.98) {
		tmp = 1.0 * im;
	} else {
		tmp = ((re * re) * 0.5) * im;
	}
	return tmp;
}

Python

def code(re, im):
	t_0 = math.exp(re) * math.sin(im)
	tmp = 0
	if t_0 <= 0.0:
		tmp = (((im * im) * -0.16666666666666666) * im) * 1.0
	elif t_0 <= 0.98:
		tmp = 1.0 * im
	else:
		tmp = ((re * re) * 0.5) * im
	return tmp

Julia

function code(re, im)
	t_0 = Float64(exp(re) * sin(im))
	tmp = 0.0
	if (t_0 <= 0.0)
		tmp = Float64(Float64(Float64(Float64(im * im) * -0.16666666666666666) * im) * 1.0);
	elseif (t_0 <= 0.98)
		tmp = Float64(1.0 * im);
	else
		tmp = Float64(Float64(Float64(re * re) * 0.5) * im);
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	t_0 = exp(re) * sin(im);
	tmp = 0.0;
	if (t_0 <= 0.0)
		tmp = (((im * im) * -0.16666666666666666) * im) * 1.0;
	elseif (t_0 <= 0.98)
		tmp = 1.0 * im;
	else
		tmp = ((re * re) * 0.5) * im;
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, 0.0], N[(N[(N[(N[(im * im), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * im), $MachinePrecision] * 1.0), $MachinePrecision], If[LessEqual[t$95$0, 0.98], N[(1.0 * im), $MachinePrecision], N[(N[(N[(re * re), $MachinePrecision] * 0.5), $MachinePrecision] * im), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.0

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 60.5

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

   4. Applied rewrites 60.5%

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

   5. Taylor expanded in re around 0

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

   7. Applied rewrites 29.7%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lift-*.f64 N/A

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

      6. lift-fma.f64 N/A

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

      7. +-commutative N/A

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

      8. pow2 N/A

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

      9. *-commutative N/A

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

      10. *-commutative N/A

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

      11. +-commutative N/A

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

      12. *-commutative N/A

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

      13. pow2 N/A

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

      14. associate-*l* N/A

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

      15. lift-fma.f64 N/A

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

      16. lift-*.f64 N/A

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

      17. lift-*.f64 N/A

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

   9. Applied rewrites 29.7%

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

   10. Taylor expanded in im around inf

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

       1. *-commutative N/A

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

       2. lower-*.f64 N/A

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

       3. pow2 N/A

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

       4. lift-*.f64 14.5

          \[\leadsto \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \cdot 1 \]

   12. Applied rewrites 14.5%

       \[\leadsto \left(\left(\left(im \cdot im\right) \cdot -0.16666666666666666\right) \cdot im\right) \cdot 1 \]

   if -0.0 < (*.f64 (exp.f64 re) (sin.f64 im)) < 0.97999999999999998

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]

   5. Taylor expanded in re around 0

      \[\leadsto \color{blue}{1} \cdot im \]
   6. Step-by-step derivation

   7. Applied rewrites 26.4%

      \[\leadsto \color{blue}{1} \cdot im \]

   if 0.97999999999999998 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]

   5. Taylor expanded in re around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. lift-fma.f64 36.6

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.5, re, 1\right), re, 1\right) \cdot im \]

   7. Applied rewrites 36.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, re, 1\right), re, 1\right)} \cdot im \]

   8. Taylor expanded in re around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. unpow2 N/A

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

      4. lower-*.f64 13.8

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

   10. Applied rewrites 13.8%

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

Alternative 13: 29.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \sin im \leq 0.98:\\ \;\;\;\;1 \cdot im\\ \mathbf{else}:\\ \;\;\;\;\left(\left(re \cdot re\right) \cdot 0.5\right) \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (sin im)) 0.98) (* 1.0 im) (* (* (* re re) 0.5) im)))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * sin(im)) <= 0.98) {
		tmp = 1.0 * im;
	} else {
		tmp = ((re * re) * 0.5) * im;
	}
	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 ((exp(re) * sin(im)) <= 0.98d0) then
        tmp = 1.0d0 * im
    else
        tmp = ((re * re) * 0.5d0) * im
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if ((Math.exp(re) * Math.sin(im)) <= 0.98) {
		tmp = 1.0 * im;
	} else {
		tmp = ((re * re) * 0.5) * im;
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if (math.exp(re) * math.sin(im)) <= 0.98:
		tmp = 1.0 * im
	else:
		tmp = ((re * re) * 0.5) * im
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * sin(im)) <= 0.98)
		tmp = Float64(1.0 * im);
	else
		tmp = Float64(Float64(Float64(re * re) * 0.5) * im);
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if ((exp(re) * sin(im)) <= 0.98)
		tmp = 1.0 * im;
	else
		tmp = ((re * re) * 0.5) * im;
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], 0.98], N[(1.0 * im), $MachinePrecision], N[(N[(N[(re * re), $MachinePrecision] * 0.5), $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < 0.97999999999999998

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]

   5. Taylor expanded in re around 0

      \[\leadsto \color{blue}{1} \cdot im \]
   6. Step-by-step derivation

   7. Applied rewrites 26.4%

      \[\leadsto \color{blue}{1} \cdot im \]

   if 0.97999999999999998 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]

   5. Taylor expanded in re around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. lift-fma.f64 36.6

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.5, re, 1\right), re, 1\right) \cdot im \]

   7. Applied rewrites 36.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, re, 1\right), re, 1\right)} \cdot im \]

   8. Taylor expanded in re around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. unpow2 N/A

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

      4. lower-*.f64 13.8

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

   10. Applied rewrites 13.8%

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

Alternative 14: 27.9% accurate, 4.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;im \leq 14000000000000:\\ \;\;\;\;1 \cdot im\\ \mathbf{else}:\\ \;\;\;\;\left(1 \cdot re\right) \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= im 14000000000000.0) (* 1.0 im) (* (* 1.0 re) im)))

C

double code(double re, double im) {
	double tmp;
	if (im <= 14000000000000.0) {
		tmp = 1.0 * im;
	} else {
		tmp = (1.0 * re) * im;
	}
	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 (im <= 14000000000000.0d0) then
        tmp = 1.0d0 * im
    else
        tmp = (1.0d0 * re) * im
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if (im <= 14000000000000.0) {
		tmp = 1.0 * im;
	} else {
		tmp = (1.0 * re) * im;
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if im <= 14000000000000.0:
		tmp = 1.0 * im
	else:
		tmp = (1.0 * re) * im
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (im <= 14000000000000.0)
		tmp = Float64(1.0 * im);
	else
		tmp = Float64(Float64(1.0 * re) * im);
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if (im <= 14000000000000.0)
		tmp = 1.0 * im;
	else
		tmp = (1.0 * re) * im;
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[im, 14000000000000.0], N[(1.0 * im), $MachinePrecision], N[(N[(1.0 * re), $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if im < 1.4e13

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]

   5. Taylor expanded in re around 0

      \[\leadsto \color{blue}{1} \cdot im \]
   6. Step-by-step derivation

   7. Applied rewrites 26.4%

      \[\leadsto \color{blue}{1} \cdot im \]

   if 1.4e13 < im

   1. Initial program 100.0%

      \[e^{re} \cdot \sin im \]

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{im} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.9%

      \[\leadsto e^{re} \cdot \color{blue}{im} \]

   5. Taylor expanded in re around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. lift-fma.f64 36.6

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.5, re, 1\right), re, 1\right) \cdot im \]

   7. Applied rewrites 36.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, re, 1\right), re, 1\right)} \cdot im \]

   8. Taylor expanded in re around inf

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

      1. unpow2 N/A

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

      2. associate-*l* N/A

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

      3. distribute-lft-in N/A

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

      4. *-commutative N/A

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

      5. rgt-mult-inverse N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 N/A

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

      8. lift-fma.f64 13.9

         \[\leadsto \left(\mathsf{fma}\left(0.5, re, 1\right) \cdot re\right) \cdot im \]

   10. Applied rewrites 13.9%

       \[\leadsto \left(\mathsf{fma}\left(0.5, re, 1\right) \cdot \color{blue}{re}\right) \cdot im \]

   11. Taylor expanded in re around 0

       \[\leadsto \left(1 \cdot re\right) \cdot im \]
   12. Step-by-step derivation

   13. Applied rewrites 6.6%

       \[\leadsto \left(1 \cdot re\right) \cdot im \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 15: 26.4% accurate, 11.6× speedup

Math

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

FPCore

(FPCore (re im) :precision binary64 (* 1.0 im))

C

double code(double re, double im) {
	return 1.0 * 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 = 1.0d0 * im
end function

Java

public static double code(double re, double im) {
	return 1.0 * im;
}

Python

def code(re, im):
	return 1.0 * im

Julia

function code(re, im)
	return Float64(1.0 * im)
end

MATLAB

function tmp = code(re, im)
	tmp = 1.0 * im;
end

Wolfram

code[re_, im_] := N[(1.0 * im), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[e^{re} \cdot \sin im \]

2. Taylor expanded in im around 0

   \[\leadsto e^{re} \cdot \color{blue}{im} \]
3. Step-by-step derivation

4. Applied rewrites 68.9%

   \[\leadsto e^{re} \cdot \color{blue}{im} \]

5. Taylor expanded in re around 0

   \[\leadsto \color{blue}{1} \cdot im \]
6. Step-by-step derivation

7. Applied rewrites 26.4%

   \[\leadsto \color{blue}{1} \cdot im \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025134 
(FPCore (re im)
  :name "math.exp on complex, imaginary part"
  :precision binary64
  (* (exp re) (sin im)))