math.exp on complex, real part

Percentage Accurate: 100.0% → 100.0%

Time: 3.2s

Alternatives: 16

Speedup: 1.0×

• 24.4% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

Alternative 2: 92.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{re} \cdot \cos im\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;e^{re} \cdot \left(\left(im \cdot im\right) \cdot -0.5\right)\\ \mathbf{elif}\;t\_0 \leq -0.004:\\ \;\;\;\;\cos im \cdot \left(re - -1\right)\\ \mathbf{elif}\;t\_0 \leq 0.004:\\ \;\;\;\;e^{re} \cdot \mathsf{fma}\left(im \cdot im, -0.5, 1\right)\\ \mathbf{elif}\;t\_0 \leq 0.9999:\\ \;\;\;\;\cos im\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (exp re) (cos im))))
   (if (<= t_0 (- INFINITY))
     (* (exp re) (* (* im im) -0.5))
     (if (<= t_0 -0.004)
       (* (cos im) (- re -1.0))
       (if (<= t_0 0.004)
         (* (exp re) (fma (* im im) -0.5 1.0))
         (if (<= t_0 0.9999)
           (cos im)
           (*
            (exp re)
            (fma
             (- (* 0.041666666666666664 (* im im)) 0.5)
             (* im im)
             1.0))))))))

C

double code(double re, double im) {
	double t_0 = exp(re) * cos(im);
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = exp(re) * ((im * im) * -0.5);
	} else if (t_0 <= -0.004) {
		tmp = cos(im) * (re - -1.0);
	} else if (t_0 <= 0.004) {
		tmp = exp(re) * fma((im * im), -0.5, 1.0);
	} else if (t_0 <= 0.9999) {
		tmp = cos(im);
	} else {
		tmp = exp(re) * fma(((0.041666666666666664 * (im * im)) - 0.5), (im * im), 1.0);
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(exp(re) * cos(im))
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(exp(re) * Float64(Float64(im * im) * -0.5));
	elseif (t_0 <= -0.004)
		tmp = Float64(cos(im) * Float64(re - -1.0));
	elseif (t_0 <= 0.004)
		tmp = Float64(exp(re) * fma(Float64(im * im), -0.5, 1.0));
	elseif (t_0 <= 0.9999)
		tmp = cos(im);
	else
		tmp = Float64(exp(re) * fma(Float64(Float64(0.041666666666666664 * Float64(im * im)) - 0.5), Float64(im * im), 1.0));
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[Exp[re], $MachinePrecision] * N[Cos[im], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], N[(N[Exp[re], $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, -0.004], N[(N[Cos[im], $MachinePrecision] * N[(re - -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 0.004], N[(N[Exp[re], $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5 + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 0.9999], N[Cos[im], $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * N[(N[(N[(0.041666666666666664 * N[(im * im), $MachinePrecision]), $MachinePrecision] - 0.5), $MachinePrecision] * N[(im * im), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 5 regimes

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in im around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. pow2 N/A

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

      4. lift-*.f64 25.8

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

   7. Applied rewrites 25.8%

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

   if -inf.0 < (*.f64 (exp.f64 re) (cos.f64 im)) < -0.0040000000000000001

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. distribute-rgt1-in N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. lift-cos.f64 N/A

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

      6. +-commutative N/A

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

      7. metadata-eval N/A

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

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

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

      9. metadata-eval N/A

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

      10. metadata-eval N/A

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

      11. metadata-eval N/A

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

      12. lower--.f64 N/A

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

      13. metadata-eval 51.6

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

   4. Applied rewrites 51.6%

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

   if -0.0040000000000000001 < (*.f64 (exp.f64 re) (cos.f64 im)) < 0.0040000000000000001

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   if 0.0040000000000000001 < (*.f64 (exp.f64 re) (cos.f64 im)) < 0.99990000000000001

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. lift-cos.f64 50.7

         \[\leadsto \cos im \]

   4. Applied rewrites 50.7%

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

   if 0.99990000000000001 < (*.f64 (exp.f64 re) (cos.f64 im))

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{\left(1 + {im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right)\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto e^{re} \cdot \left({im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) + \color{blue}{1}\right) \]

      2. *-commutative N/A

         \[\leadsto e^{re} \cdot \left(\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) \cdot {im}^{2} + 1\right) \]

      3. lower-fma.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, \color{blue}{{im}^{2}}, 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {\color{blue}{im}}^{2}, 1\right) \]

      5. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {im}^{2}, 1\right) \]

      6. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      7. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      8. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot \color{blue}{im}, 1\right) \]

      9. lower-*.f64 59.9

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot \color{blue}{im}, 1\right) \]

   4. Applied rewrites 59.9%

      \[\leadsto e^{re} \cdot \color{blue}{\mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)} \]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 3: 92.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{re} \cdot \cos im\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;e^{re} \cdot \left(\left(im \cdot im\right) \cdot -0.5\right)\\ \mathbf{elif}\;t\_0 \leq -0.004:\\ \;\;\;\;\cos im\\ \mathbf{elif}\;t\_0 \leq 0.004:\\ \;\;\;\;e^{re} \cdot \mathsf{fma}\left(im \cdot im, -0.5, 1\right)\\ \mathbf{elif}\;t\_0 \leq 0.9999:\\ \;\;\;\;\cos im\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (exp re) (cos im))))
   (if (<= t_0 (- INFINITY))
     (* (exp re) (* (* im im) -0.5))
     (if (<= t_0 -0.004)
       (cos im)
       (if (<= t_0 0.004)
         (* (exp re) (fma (* im im) -0.5 1.0))
         (if (<= t_0 0.9999)
           (cos im)
           (*
            (exp re)
            (fma
             (- (* 0.041666666666666664 (* im im)) 0.5)
             (* im im)
             1.0))))))))

C

double code(double re, double im) {
	double t_0 = exp(re) * cos(im);
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = exp(re) * ((im * im) * -0.5);
	} else if (t_0 <= -0.004) {
		tmp = cos(im);
	} else if (t_0 <= 0.004) {
		tmp = exp(re) * fma((im * im), -0.5, 1.0);
	} else if (t_0 <= 0.9999) {
		tmp = cos(im);
	} else {
		tmp = exp(re) * fma(((0.041666666666666664 * (im * im)) - 0.5), (im * im), 1.0);
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(exp(re) * cos(im))
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(exp(re) * Float64(Float64(im * im) * -0.5));
	elseif (t_0 <= -0.004)
		tmp = cos(im);
	elseif (t_0 <= 0.004)
		tmp = Float64(exp(re) * fma(Float64(im * im), -0.5, 1.0));
	elseif (t_0 <= 0.9999)
		tmp = cos(im);
	else
		tmp = Float64(exp(re) * fma(Float64(Float64(0.041666666666666664 * Float64(im * im)) - 0.5), Float64(im * im), 1.0));
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[Exp[re], $MachinePrecision] * N[Cos[im], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], N[(N[Exp[re], $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, -0.004], N[Cos[im], $MachinePrecision], If[LessEqual[t$95$0, 0.004], N[(N[Exp[re], $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5 + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 0.9999], N[Cos[im], $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * N[(N[(N[(0.041666666666666664 * N[(im * im), $MachinePrecision]), $MachinePrecision] - 0.5), $MachinePrecision] * N[(im * im), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in im around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. pow2 N/A

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

      4. lift-*.f64 25.8

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

   7. Applied rewrites 25.8%

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

   if -inf.0 < (*.f64 (exp.f64 re) (cos.f64 im)) < -0.0040000000000000001 or 0.0040000000000000001 < (*.f64 (exp.f64 re) (cos.f64 im)) < 0.99990000000000001

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. lift-cos.f64 50.7

         \[\leadsto \cos im \]

   4. Applied rewrites 50.7%

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

   if -0.0040000000000000001 < (*.f64 (exp.f64 re) (cos.f64 im)) < 0.0040000000000000001

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   if 0.99990000000000001 < (*.f64 (exp.f64 re) (cos.f64 im))

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{\left(1 + {im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right)\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto e^{re} \cdot \left({im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) + \color{blue}{1}\right) \]

      2. *-commutative N/A

         \[\leadsto e^{re} \cdot \left(\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) \cdot {im}^{2} + 1\right) \]

      3. lower-fma.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, \color{blue}{{im}^{2}}, 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {\color{blue}{im}}^{2}, 1\right) \]

      5. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {im}^{2}, 1\right) \]

      6. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      7. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      8. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot \color{blue}{im}, 1\right) \]

      9. lower-*.f64 59.9

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot \color{blue}{im}, 1\right) \]

   4. Applied rewrites 59.9%

      \[\leadsto e^{re} \cdot \color{blue}{\mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 4: 89.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(\mathsf{fma}\left(0.5, re, 1\right), re, 1\right) \cdot \cos im\\ t_1 := e^{re} \cdot \mathsf{fma}\left(im \cdot im, -0.5, 1\right)\\ \mathbf{if}\;re \leq -0.029:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;re \leq 135000000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;re \leq 1.9 \cdot 10^{+154}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (fma (fma 0.5 re 1.0) re 1.0) (cos im)))
        (t_1 (* (exp re) (fma (* im im) -0.5 1.0))))
   (if (<= re -0.029)
     t_1
     (if (<= re 135000000.0) t_0 (if (<= re 1.9e+154) t_1 t_0)))))

C

double code(double re, double im) {
	double t_0 = fma(fma(0.5, re, 1.0), re, 1.0) * cos(im);
	double t_1 = exp(re) * fma((im * im), -0.5, 1.0);
	double tmp;
	if (re <= -0.029) {
		tmp = t_1;
	} else if (re <= 135000000.0) {
		tmp = t_0;
	} else if (re <= 1.9e+154) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(fma(fma(0.5, re, 1.0), re, 1.0) * cos(im))
	t_1 = Float64(exp(re) * fma(Float64(im * im), -0.5, 1.0))
	tmp = 0.0
	if (re <= -0.029)
		tmp = t_1;
	elseif (re <= 135000000.0)
		tmp = t_0;
	elseif (re <= 1.9e+154)
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[(N[(0.5 * re + 1.0), $MachinePrecision] * re + 1.0), $MachinePrecision] * N[Cos[im], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[Exp[re], $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5 + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[re, -0.029], t$95$1, If[LessEqual[re, 135000000.0], t$95$0, If[LessEqual[re, 1.9e+154], t$95$1, t$95$0]]]]]

Derivation

1. Split input into 2 regimes

2. if re < -0.0290000000000000015 or 1.35e8 < re < 1.8999999999999999e154

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   if -0.0290000000000000015 < re < 1.35e8 or 1.8999999999999999e154 < re

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. lower-fma.f64 63.6

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

   4. Applied rewrites 63.6%

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

Alternative 5: 69.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \cos im \leq 0:\\ \;\;\;\;e^{re} \cdot \mathsf{fma}\left(im \cdot im, -0.5, 1\right)\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (cos im)) 0.0)
   (* (exp re) (fma (* im im) -0.5 1.0))
   (*
    (exp re)
    (fma (- (* 0.041666666666666664 (* im im)) 0.5) (* im im) 1.0))))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * cos(im)) <= 0.0) {
		tmp = exp(re) * fma((im * im), -0.5, 1.0);
	} else {
		tmp = exp(re) * fma(((0.041666666666666664 * (im * im)) - 0.5), (im * im), 1.0);
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * cos(im)) <= 0.0)
		tmp = Float64(exp(re) * fma(Float64(im * im), -0.5, 1.0));
	else
		tmp = Float64(exp(re) * fma(Float64(Float64(0.041666666666666664 * Float64(im * im)) - 0.5), Float64(im * im), 1.0));
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Cos[im], $MachinePrecision]), $MachinePrecision], 0.0], N[(N[Exp[re], $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5 + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * N[(N[(N[(0.041666666666666664 * N[(im * im), $MachinePrecision]), $MachinePrecision] - 0.5), $MachinePrecision] * N[(im * im), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{\left(1 + {im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right)\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto e^{re} \cdot \left({im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) + \color{blue}{1}\right) \]

      2. *-commutative N/A

         \[\leadsto e^{re} \cdot \left(\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) \cdot {im}^{2} + 1\right) \]

      3. lower-fma.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, \color{blue}{{im}^{2}}, 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {\color{blue}{im}}^{2}, 1\right) \]

      5. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {im}^{2}, 1\right) \]

      6. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      7. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      8. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot \color{blue}{im}, 1\right) \]

      9. lower-*.f64 59.9

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot \color{blue}{im}, 1\right) \]

   4. Applied rewrites 59.9%

      \[\leadsto e^{re} \cdot \color{blue}{\mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 67.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{re} \cdot \mathsf{fma}\left(im \cdot im, -0.5, 1\right)\\ \mathbf{if}\;\cos im \leq -0.004:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;\cos im \leq 0.998:\\ \;\;\;\;e^{re} \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot 0.041666666666666664\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (exp re) (fma (* im im) -0.5 1.0))))
   (if (<= (cos im) -0.004)
     t_0
     (if (<= (cos im) 0.998)
       (* (exp re) (* (* (* im im) (* im im)) 0.041666666666666664))
       t_0))))

C

double code(double re, double im) {
	double t_0 = exp(re) * fma((im * im), -0.5, 1.0);
	double tmp;
	if (cos(im) <= -0.004) {
		tmp = t_0;
	} else if (cos(im) <= 0.998) {
		tmp = exp(re) * (((im * im) * (im * im)) * 0.041666666666666664);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(exp(re) * fma(Float64(im * im), -0.5, 1.0))
	tmp = 0.0
	if (cos(im) <= -0.004)
		tmp = t_0;
	elseif (cos(im) <= 0.998)
		tmp = Float64(exp(re) * Float64(Float64(Float64(im * im) * Float64(im * im)) * 0.041666666666666664));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[Exp[re], $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5 + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Cos[im], $MachinePrecision], -0.004], t$95$0, If[LessEqual[N[Cos[im], $MachinePrecision], 0.998], N[(N[Exp[re], $MachinePrecision] * N[(N[(N[(im * im), $MachinePrecision] * N[(im * im), $MachinePrecision]), $MachinePrecision] * 0.041666666666666664), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if (cos.f64 im) < -0.0040000000000000001 or 0.998 < (cos.f64 im)

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   if -0.0040000000000000001 < (cos.f64 im) < 0.998

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{\left(1 + {im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right)\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto e^{re} \cdot \left({im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) + \color{blue}{1}\right) \]

      2. *-commutative N/A

         \[\leadsto e^{re} \cdot \left(\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) \cdot {im}^{2} + 1\right) \]

      3. lower-fma.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, \color{blue}{{im}^{2}}, 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {\color{blue}{im}}^{2}, 1\right) \]

      5. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {im}^{2}, 1\right) \]

      6. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      7. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      8. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot \color{blue}{im}, 1\right) \]

      9. lower-*.f64 59.9

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot \color{blue}{im}, 1\right) \]

   4. Applied rewrites 59.9%

      \[\leadsto e^{re} \cdot \color{blue}{\mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)} \]

   5. Taylor expanded in im around inf

      \[\leadsto e^{re} \cdot \left(\frac{1}{24} \cdot \color{blue}{{im}^{4}}\right) \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto e^{re} \cdot \left({im}^{4} \cdot \frac{1}{24}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \left({im}^{4} \cdot \frac{1}{24}\right) \]

      3. metadata-eval N/A

         \[\leadsto e^{re} \cdot \left({im}^{\left(2 + 2\right)} \cdot \frac{1}{24}\right) \]

      4. pow-prod-up N/A

         \[\leadsto e^{re} \cdot \left(\left({im}^{2} \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      5. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \left(\left({im}^{2} \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      6. pow2 N/A

         \[\leadsto e^{re} \cdot \left(\left(\left(im \cdot im\right) \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      7. lift-*.f64 N/A

         \[\leadsto e^{re} \cdot \left(\left(\left(im \cdot im\right) \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      8. pow2 N/A

         \[\leadsto e^{re} \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot \frac{1}{24}\right) \]

      9. lift-*.f64 25.9

         \[\leadsto e^{re} \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot 0.041666666666666664\right) \]

   7. Applied rewrites 25.9%

      \[\leadsto e^{re} \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot \color{blue}{0.041666666666666664}\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 63.2% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ e^{re} \cdot \mathsf{fma}\left(im \cdot im, -0.5, 1\right) \end{array} \]

FPCore

(FPCore (re im) :precision binary64 (* (exp re) (fma (* im im) -0.5 1.0)))

C

double code(double re, double im) {
	return exp(re) * fma((im * im), -0.5, 1.0);
}

Julia

function code(re, im)
	return Float64(exp(re) * fma(Float64(im * im), -0.5, 1.0))
end

Wolfram

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

Derivation

1. Initial program 100.0%

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

2. Taylor expanded in im around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

   4. unpow2 N/A

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

   5. lower-*.f64 63.2

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

4. Applied rewrites 63.2%

   \[\leadsto e^{re} \cdot \color{blue}{\mathsf{fma}\left(im \cdot im, -0.5, 1\right)} \]
5. Add Preprocessing

Alternative 8: 56.9% accurate, 0.7× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Split input into 2 regimes

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in im around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. pow2 N/A

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

      4. lift-*.f64 25.8

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

   7. Applied rewrites 25.8%

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

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in re around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. lower-fma.f64 38.2

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

   7. Applied rewrites 38.2%

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

Alternative 9: 56.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \cos im \leq 0.004:\\ \;\;\;\;e^{re} \cdot \left(\left(im \cdot im\right) \cdot -0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\left(1 + re\right) \cdot \mathsf{fma}\left(\left(\left(im \cdot im\right) \cdot im\right) \cdot 0.041666666666666664, im, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (cos im)) 0.004)
   (* (exp re) (* (* im im) -0.5))
   (* (+ 1.0 re) (fma (* (* (* im im) im) 0.041666666666666664) im 1.0))))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * cos(im)) <= 0.004) {
		tmp = exp(re) * ((im * im) * -0.5);
	} else {
		tmp = (1.0 + re) * fma((((im * im) * im) * 0.041666666666666664), im, 1.0);
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * cos(im)) <= 0.004)
		tmp = Float64(exp(re) * Float64(Float64(im * im) * -0.5));
	else
		tmp = Float64(Float64(1.0 + re) * fma(Float64(Float64(Float64(im * im) * im) * 0.041666666666666664), im, 1.0));
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Cos[im], $MachinePrecision]), $MachinePrecision], 0.004], N[(N[Exp[re], $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 + re), $MachinePrecision] * N[(N[(N[(N[(im * im), $MachinePrecision] * im), $MachinePrecision] * 0.041666666666666664), $MachinePrecision] * im + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (cos.f64 im)) < 0.0040000000000000001

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in im around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. pow2 N/A

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

      4. lift-*.f64 25.8

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

   7. Applied rewrites 25.8%

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

   if 0.0040000000000000001 < (*.f64 (exp.f64 re) (cos.f64 im))

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{\left(1 + {im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right)\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto e^{re} \cdot \left({im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) + \color{blue}{1}\right) \]

      2. *-commutative N/A

         \[\leadsto e^{re} \cdot \left(\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) \cdot {im}^{2} + 1\right) \]

      3. lower-fma.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, \color{blue}{{im}^{2}}, 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {\color{blue}{im}}^{2}, 1\right) \]

      5. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {im}^{2}, 1\right) \]

      6. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      7. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      8. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot \color{blue}{im}, 1\right) \]

      9. lower-*.f64 59.9

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot \color{blue}{im}, 1\right) \]

   4. Applied rewrites 59.9%

      \[\leadsto e^{re} \cdot \color{blue}{\mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)} \]

   5. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\left(1 + re\right)} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot im, 1\right) \]
   6. Step-by-step derivation

      1. lower-+.f64 32.1

         \[\leadsto \left(1 + \color{blue}{re}\right) \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right) \]

   7. Applied rewrites 32.1%

      \[\leadsto \color{blue}{\left(1 + re\right)} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right) \]
   8. Step-by-step derivation

      1. lift-fma.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}\right) \cdot \left(im \cdot im\right) + \color{blue}{1}\right) \]

      2. lift--.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}\right) \cdot \left(im \cdot im\right) + 1\right) \]

      3. lift-*.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}\right) \cdot \left(im \cdot im\right) + 1\right) \]

      4. lift-*.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}\right) \cdot \left(im \cdot im\right) + 1\right) \]

      5. lift-*.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}\right) \cdot \left(im \cdot im\right) + 1\right) \]

      6. associate-*r* N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}\right) \cdot im\right) \cdot im + 1\right) \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}\right) \cdot im, \color{blue}{im}, 1\right) \]

      8. lower-*.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}\right) \cdot im, im, 1\right) \]

      9. pow2 N/A

         \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) \cdot im, im, 1\right) \]

      10. lower--.f64 N/A

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) \cdot im, im, 1\right) \]

      11. *-commutative N/A

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left({im}^{2} \cdot \frac{1}{24} - \frac{1}{2}\right) \cdot im, im, 1\right) \]

      12. lower-*.f64 N/A

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left({im}^{2} \cdot \frac{1}{24} - \frac{1}{2}\right) \cdot im, im, 1\right) \]

      13. pow2 N/A

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left(\left(im \cdot im\right) \cdot \frac{1}{24} - \frac{1}{2}\right) \cdot im, im, 1\right) \]

      14. lift-*.f64 32.1

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left(\left(im \cdot im\right) \cdot 0.041666666666666664 - 0.5\right) \cdot im, im, 1\right) \]

   9. Applied rewrites 32.1%

      \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left(\left(im \cdot im\right) \cdot 0.041666666666666664 - 0.5\right) \cdot im, \color{blue}{im}, 1\right) \]

   10. Taylor expanded in im around inf

       \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{3}, im, 1\right) \]
   11. Step-by-step derivation

       1. *-commutative N/A

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left({im}^{3} \cdot \frac{1}{24}, im, 1\right) \]

       2. lower-*.f64 N/A

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left({im}^{3} \cdot \frac{1}{24}, im, 1\right) \]

       3. unpow3 N/A

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left(\left(im \cdot im\right) \cdot im\right) \cdot \frac{1}{24}, im, 1\right) \]

       4. pow2 N/A

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left({im}^{2} \cdot im\right) \cdot \frac{1}{24}, im, 1\right) \]

       5. lower-*.f64 N/A

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left({im}^{2} \cdot im\right) \cdot \frac{1}{24}, im, 1\right) \]

       6. pow2 N/A

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left(\left(im \cdot im\right) \cdot im\right) \cdot \frac{1}{24}, im, 1\right) \]

       7. lift-*.f64 31.8

          \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left(\left(im \cdot im\right) \cdot im\right) \cdot 0.041666666666666664, im, 1\right) \]

   12. Applied rewrites 31.8%

       \[\leadsto \left(1 + re\right) \cdot \mathsf{fma}\left(\left(\left(im \cdot im\right) \cdot im\right) \cdot 0.041666666666666664, im, 1\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 55.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{re} \cdot \cos im\\ \mathbf{if}\;t\_0 \leq 0.004:\\ \;\;\;\;e^{re} \cdot \left(\left(im \cdot im\right) \cdot -0.5\right)\\ \mathbf{elif}\;t\_0 \leq 2:\\ \;\;\;\;\left(1 + re\right) \cdot \mathsf{fma}\left(im \cdot im, -0.5, 1\right)\\ \mathbf{else}:\\ \;\;\;\;\left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot 0.041666666666666664\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (exp re) (cos im))))
   (if (<= t_0 0.004)
     (* (exp re) (* (* im im) -0.5))
     (if (<= t_0 2.0)
       (* (+ 1.0 re) (fma (* im im) -0.5 1.0))
       (* (+ 1.0 re) (* (* (* im im) (* im im)) 0.041666666666666664))))))

C

double code(double re, double im) {
	double t_0 = exp(re) * cos(im);
	double tmp;
	if (t_0 <= 0.004) {
		tmp = exp(re) * ((im * im) * -0.5);
	} else if (t_0 <= 2.0) {
		tmp = (1.0 + re) * fma((im * im), -0.5, 1.0);
	} else {
		tmp = (1.0 + re) * (((im * im) * (im * im)) * 0.041666666666666664);
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(exp(re) * cos(im))
	tmp = 0.0
	if (t_0 <= 0.004)
		tmp = Float64(exp(re) * Float64(Float64(im * im) * -0.5));
	elseif (t_0 <= 2.0)
		tmp = Float64(Float64(1.0 + re) * fma(Float64(im * im), -0.5, 1.0));
	else
		tmp = Float64(Float64(1.0 + re) * Float64(Float64(Float64(im * im) * Float64(im * im)) * 0.041666666666666664));
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[Exp[re], $MachinePrecision] * N[Cos[im], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, 0.004], N[(N[Exp[re], $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 2.0], N[(N[(1.0 + re), $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5 + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 + re), $MachinePrecision] * N[(N[(N[(im * im), $MachinePrecision] * N[(im * im), $MachinePrecision]), $MachinePrecision] * 0.041666666666666664), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (exp.f64 re) (cos.f64 im)) < 0.0040000000000000001

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in im around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. pow2 N/A

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

      4. lift-*.f64 25.8

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

   7. Applied rewrites 25.8%

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

   if 0.0040000000000000001 < (*.f64 (exp.f64 re) (cos.f64 im)) < 2

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in re around 0

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

      1. lower-+.f64 31.7

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

   7. Applied rewrites 31.7%

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

   if 2 < (*.f64 (exp.f64 re) (cos.f64 im))

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{\left(1 + {im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right)\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto e^{re} \cdot \left({im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) + \color{blue}{1}\right) \]

      2. *-commutative N/A

         \[\leadsto e^{re} \cdot \left(\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) \cdot {im}^{2} + 1\right) \]

      3. lower-fma.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, \color{blue}{{im}^{2}}, 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {\color{blue}{im}}^{2}, 1\right) \]

      5. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {im}^{2}, 1\right) \]

      6. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      7. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      8. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot \color{blue}{im}, 1\right) \]

      9. lower-*.f64 59.9

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot \color{blue}{im}, 1\right) \]

   4. Applied rewrites 59.9%

      \[\leadsto e^{re} \cdot \color{blue}{\mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)} \]

   5. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\left(1 + re\right)} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot im, 1\right) \]
   6. Step-by-step derivation

      1. lower-+.f64 32.1

         \[\leadsto \left(1 + \color{blue}{re}\right) \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right) \]

   7. Applied rewrites 32.1%

      \[\leadsto \color{blue}{\left(1 + re\right)} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right) \]

   8. Taylor expanded in im around inf

      \[\leadsto \left(1 + re\right) \cdot \left(\frac{1}{24} \cdot \color{blue}{{im}^{4}}\right) \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(1 + re\right) \cdot \left({im}^{4} \cdot \frac{1}{24}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \left({im}^{4} \cdot \frac{1}{24}\right) \]

      3. metadata-eval N/A

         \[\leadsto \left(1 + re\right) \cdot \left({im}^{\left(2 + 2\right)} \cdot \frac{1}{24}\right) \]

      4. pow-prod-up N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left({im}^{2} \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      5. lower-*.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left({im}^{2} \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      6. pow2 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      7. lift-*.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      8. pow2 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot \frac{1}{24}\right) \]

      9. lift-*.f64 16.0

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot 0.041666666666666664\right) \]

   10. Applied rewrites 16.0%

       \[\leadsto \left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot \color{blue}{0.041666666666666664}\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 11: 55.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \cos im \leq 0:\\ \;\;\;\;e^{re} \cdot \left(\left(im \cdot im\right) \cdot -0.5\right)\\ \mathbf{else}:\\ \;\;\;\;1 \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (cos im)) 0.0)
   (* (exp re) (* (* im im) -0.5))
   (* 1.0 (fma (- (* 0.041666666666666664 (* im im)) 0.5) (* im im) 1.0))))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * cos(im)) <= 0.0) {
		tmp = exp(re) * ((im * im) * -0.5);
	} else {
		tmp = 1.0 * fma(((0.041666666666666664 * (im * im)) - 0.5), (im * im), 1.0);
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * cos(im)) <= 0.0)
		tmp = Float64(exp(re) * Float64(Float64(im * im) * -0.5));
	else
		tmp = Float64(1.0 * fma(Float64(Float64(0.041666666666666664 * Float64(im * im)) - 0.5), Float64(im * im), 1.0));
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Cos[im], $MachinePrecision]), $MachinePrecision], 0.0], N[(N[Exp[re], $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5), $MachinePrecision]), $MachinePrecision], N[(1.0 * N[(N[(N[(0.041666666666666664 * N[(im * im), $MachinePrecision]), $MachinePrecision] - 0.5), $MachinePrecision] * N[(im * im), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in im around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. pow2 N/A

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

      4. lift-*.f64 25.8

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

   7. Applied rewrites 25.8%

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

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{\left(1 + {im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right)\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto e^{re} \cdot \left({im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) + \color{blue}{1}\right) \]

      2. *-commutative N/A

         \[\leadsto e^{re} \cdot \left(\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) \cdot {im}^{2} + 1\right) \]

      3. lower-fma.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, \color{blue}{{im}^{2}}, 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {\color{blue}{im}}^{2}, 1\right) \]

      5. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {im}^{2}, 1\right) \]

      6. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      7. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      8. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot \color{blue}{im}, 1\right) \]

      9. lower-*.f64 59.9

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot \color{blue}{im}, 1\right) \]

   4. Applied rewrites 59.9%

      \[\leadsto e^{re} \cdot \color{blue}{\mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)} \]

   5. Taylor expanded in re around 0

      \[\leadsto \color{blue}{1} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot im, 1\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 30.9%

      \[\leadsto \color{blue}{1} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 42.6% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;re \leq -1:\\ \;\;\;\;\left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot 0.041666666666666664\right)\\ \mathbf{elif}\;re \leq 2.15 \cdot 10^{+105}:\\ \;\;\;\;\left(1 + re\right) \cdot \mathsf{fma}\left(im \cdot im, -0.5, 1\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.5, re, \frac{re}{im \cdot im}\right) \cdot \left(im \cdot im\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= re -1.0)
   (* (+ 1.0 re) (* (* (* im im) (* im im)) 0.041666666666666664))
   (if (<= re 2.15e+105)
     (* (+ 1.0 re) (fma (* im im) -0.5 1.0))
     (* (fma -0.5 re (/ re (* im im))) (* im im)))))

C

double code(double re, double im) {
	double tmp;
	if (re <= -1.0) {
		tmp = (1.0 + re) * (((im * im) * (im * im)) * 0.041666666666666664);
	} else if (re <= 2.15e+105) {
		tmp = (1.0 + re) * fma((im * im), -0.5, 1.0);
	} else {
		tmp = fma(-0.5, re, (re / (im * im))) * (im * im);
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (re <= -1.0)
		tmp = Float64(Float64(1.0 + re) * Float64(Float64(Float64(im * im) * Float64(im * im)) * 0.041666666666666664));
	elseif (re <= 2.15e+105)
		tmp = Float64(Float64(1.0 + re) * fma(Float64(im * im), -0.5, 1.0));
	else
		tmp = Float64(fma(-0.5, re, Float64(re / Float64(im * im))) * Float64(im * im));
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[re, -1.0], N[(N[(1.0 + re), $MachinePrecision] * N[(N[(N[(im * im), $MachinePrecision] * N[(im * im), $MachinePrecision]), $MachinePrecision] * 0.041666666666666664), $MachinePrecision]), $MachinePrecision], If[LessEqual[re, 2.15e+105], N[(N[(1.0 + re), $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5 + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(-0.5 * re + N[(re / N[(im * im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(im * im), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if re < -1

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

      \[\leadsto e^{re} \cdot \color{blue}{\left(1 + {im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right)\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto e^{re} \cdot \left({im}^{2} \cdot \left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) + \color{blue}{1}\right) \]

      2. *-commutative N/A

         \[\leadsto e^{re} \cdot \left(\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}\right) \cdot {im}^{2} + 1\right) \]

      3. lower-fma.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, \color{blue}{{im}^{2}}, 1\right) \]

      4. lower--.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {\color{blue}{im}}^{2}, 1\right) \]

      5. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot {im}^{2} - \frac{1}{2}, {im}^{2}, 1\right) \]

      6. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      7. lower-*.f64 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, {im}^{2}, 1\right) \]

      8. unpow2 N/A

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot \color{blue}{im}, 1\right) \]

      9. lower-*.f64 59.9

         \[\leadsto e^{re} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot \color{blue}{im}, 1\right) \]

   4. Applied rewrites 59.9%

      \[\leadsto e^{re} \cdot \color{blue}{\mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right)} \]

   5. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\left(1 + re\right)} \cdot \mathsf{fma}\left(\frac{1}{24} \cdot \left(im \cdot im\right) - \frac{1}{2}, im \cdot im, 1\right) \]
   6. Step-by-step derivation

      1. lower-+.f64 32.1

         \[\leadsto \left(1 + \color{blue}{re}\right) \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right) \]

   7. Applied rewrites 32.1%

      \[\leadsto \color{blue}{\left(1 + re\right)} \cdot \mathsf{fma}\left(0.041666666666666664 \cdot \left(im \cdot im\right) - 0.5, im \cdot im, 1\right) \]

   8. Taylor expanded in im around inf

      \[\leadsto \left(1 + re\right) \cdot \left(\frac{1}{24} \cdot \color{blue}{{im}^{4}}\right) \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(1 + re\right) \cdot \left({im}^{4} \cdot \frac{1}{24}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \left({im}^{4} \cdot \frac{1}{24}\right) \]

      3. metadata-eval N/A

         \[\leadsto \left(1 + re\right) \cdot \left({im}^{\left(2 + 2\right)} \cdot \frac{1}{24}\right) \]

      4. pow-prod-up N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left({im}^{2} \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      5. lower-*.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left({im}^{2} \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      6. pow2 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      7. lift-*.f64 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot {im}^{2}\right) \cdot \frac{1}{24}\right) \]

      8. pow2 N/A

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot \frac{1}{24}\right) \]

      9. lift-*.f64 16.0

         \[\leadsto \left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot 0.041666666666666664\right) \]

   10. Applied rewrites 16.0%

       \[\leadsto \left(1 + re\right) \cdot \left(\left(\left(im \cdot im\right) \cdot \left(im \cdot im\right)\right) \cdot \color{blue}{0.041666666666666664}\right) \]

   if -1 < re < 2.1500000000000001e105

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in re around 0

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

      1. lower-+.f64 31.7

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

   7. Applied rewrites 31.7%

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

   if 2.1500000000000001e105 < re

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. distribute-rgt1-in N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. lift-cos.f64 N/A

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

      6. +-commutative N/A

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

      7. metadata-eval N/A

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

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

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

      9. metadata-eval N/A

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

      10. metadata-eval N/A

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

      11. metadata-eval N/A

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

      12. lower--.f64 N/A

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

      13. metadata-eval 51.6

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

   4. Applied rewrites 51.6%

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

   5. Taylor expanded in re around inf

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

      1. *-commutative N/A

         \[\leadsto \cos im \cdot re \]

      2. lower-*.f64 N/A

         \[\leadsto \cos im \cdot re \]

      3. lift-cos.f64 3.9

         \[\leadsto \cos im \cdot re \]

   7. Applied rewrites 3.9%

      \[\leadsto \cos im \cdot \color{blue}{re} \]

   8. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. pow2 N/A

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

      6. lift-*.f64 7.7

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

   10. Applied rewrites 7.7%

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

   11. Taylor expanded in im around inf

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

       1. *-commutative N/A

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

       2. lower-*.f64 N/A

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

       3. lower-fma.f64 N/A

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

       4. lower-/.f64 N/A

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

       5. pow2 N/A

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

       6. lift-*.f64 N/A

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

       7. pow2 N/A

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

       8. lift-*.f64 9.9

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

   13. Applied rewrites 9.9%

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

Alternative 13: 40.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{re} \cdot \cos im\\ \mathbf{if}\;t\_0 \leq 0.98:\\ \;\;\;\;\left(\left(im \cdot im\right) \cdot re\right) \cdot -0.5\\ \mathbf{elif}\;t\_0 \leq 2:\\ \;\;\;\;\left(1 + re\right) \cdot \mathsf{fma}\left(im \cdot im, -0.5, 1\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.5, re, \frac{re}{im \cdot im}\right) \cdot \left(im \cdot im\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (exp re) (cos im))))
   (if (<= t_0 0.98)
     (* (* (* im im) re) -0.5)
     (if (<= t_0 2.0)
       (* (+ 1.0 re) (fma (* im im) -0.5 1.0))
       (* (fma -0.5 re (/ re (* im im))) (* im im))))))

C

double code(double re, double im) {
	double t_0 = exp(re) * cos(im);
	double tmp;
	if (t_0 <= 0.98) {
		tmp = ((im * im) * re) * -0.5;
	} else if (t_0 <= 2.0) {
		tmp = (1.0 + re) * fma((im * im), -0.5, 1.0);
	} else {
		tmp = fma(-0.5, re, (re / (im * im))) * (im * im);
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(exp(re) * cos(im))
	tmp = 0.0
	if (t_0 <= 0.98)
		tmp = Float64(Float64(Float64(im * im) * re) * -0.5);
	elseif (t_0 <= 2.0)
		tmp = Float64(Float64(1.0 + re) * fma(Float64(im * im), -0.5, 1.0));
	else
		tmp = Float64(fma(-0.5, re, Float64(re / Float64(im * im))) * Float64(im * im));
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[Exp[re], $MachinePrecision] * N[Cos[im], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, 0.98], N[(N[(N[(im * im), $MachinePrecision] * re), $MachinePrecision] * -0.5), $MachinePrecision], If[LessEqual[t$95$0, 2.0], N[(N[(1.0 + re), $MachinePrecision] * N[(N[(im * im), $MachinePrecision] * -0.5 + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(-0.5 * re + N[(re / N[(im * im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(im * im), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

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

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. distribute-rgt1-in N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. lift-cos.f64 N/A

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

      6. +-commutative N/A

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

      7. metadata-eval N/A

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

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

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

      9. metadata-eval N/A

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

      10. metadata-eval N/A

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

      11. metadata-eval N/A

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

      12. lower--.f64 N/A

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

      13. metadata-eval 51.6

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

   4. Applied rewrites 51.6%

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

   5. Taylor expanded in re around inf

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

      1. *-commutative N/A

         \[\leadsto \cos im \cdot re \]

      2. lower-*.f64 N/A

         \[\leadsto \cos im \cdot re \]

      3. lift-cos.f64 3.9

         \[\leadsto \cos im \cdot re \]

   7. Applied rewrites 3.9%

      \[\leadsto \cos im \cdot \color{blue}{re} \]

   8. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. pow2 N/A

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

      6. lift-*.f64 7.7

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

   10. Applied rewrites 7.7%

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

   11. Taylor expanded in im around inf

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

       1. *-commutative N/A

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

       2. lower-*.f64 N/A

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

       3. pow2 N/A

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

       4. lift-*.f64 N/A

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

       5. lift-*.f64 12.6

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

   13. Applied rewrites 12.6%

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

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in re around 0

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

      1. lower-+.f64 31.7

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

   7. Applied rewrites 31.7%

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

   if 2 < (*.f64 (exp.f64 re) (cos.f64 im))

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. distribute-rgt1-in N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. lift-cos.f64 N/A

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

      6. +-commutative N/A

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

      7. metadata-eval N/A

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

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

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

      9. metadata-eval N/A

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

      10. metadata-eval N/A

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

      11. metadata-eval N/A

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

      12. lower--.f64 N/A

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

      13. metadata-eval 51.6

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

   4. Applied rewrites 51.6%

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

   5. Taylor expanded in re around inf

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

      1. *-commutative N/A

         \[\leadsto \cos im \cdot re \]

      2. lower-*.f64 N/A

         \[\leadsto \cos im \cdot re \]

      3. lift-cos.f64 3.9

         \[\leadsto \cos im \cdot re \]

   7. Applied rewrites 3.9%

      \[\leadsto \cos im \cdot \color{blue}{re} \]

   8. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. pow2 N/A

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

      6. lift-*.f64 7.7

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

   10. Applied rewrites 7.7%

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

   11. Taylor expanded in im around inf

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

       1. *-commutative N/A

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

       2. lower-*.f64 N/A

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

       3. lower-fma.f64 N/A

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

       4. lower-/.f64 N/A

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

       5. pow2 N/A

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

       6. lift-*.f64 N/A

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

       7. pow2 N/A

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

       8. lift-*.f64 9.9

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

   13. Applied rewrites 9.9%

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

Alternative 14: 37.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \cos im \leq 0.98:\\ \;\;\;\;\left(\left(im \cdot im\right) \cdot re\right) \cdot -0.5\\ \mathbf{else}:\\ \;\;\;\;\left(1 + re\right) \cdot \mathsf{fma}\left(im \cdot im, -0.5, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (cos im)) 0.98)
   (* (* (* im im) re) -0.5)
   (* (+ 1.0 re) (fma (* im im) -0.5 1.0))))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * cos(im)) <= 0.98) {
		tmp = ((im * im) * re) * -0.5;
	} else {
		tmp = (1.0 + re) * fma((im * im), -0.5, 1.0);
	}
	return tmp;
}

Julia

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

Wolfram

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

Derivation

1. Split input into 2 regimes

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

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. distribute-rgt1-in N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. lift-cos.f64 N/A

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

      6. +-commutative N/A

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

      7. metadata-eval N/A

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

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

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

      9. metadata-eval N/A

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

      10. metadata-eval N/A

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

      11. metadata-eval N/A

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

      12. lower--.f64 N/A

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

      13. metadata-eval 51.6

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

   4. Applied rewrites 51.6%

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

   5. Taylor expanded in re around inf

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

      1. *-commutative N/A

         \[\leadsto \cos im \cdot re \]

      2. lower-*.f64 N/A

         \[\leadsto \cos im \cdot re \]

      3. lift-cos.f64 3.9

         \[\leadsto \cos im \cdot re \]

   7. Applied rewrites 3.9%

      \[\leadsto \cos im \cdot \color{blue}{re} \]

   8. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. pow2 N/A

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

      6. lift-*.f64 7.7

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

   10. Applied rewrites 7.7%

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

   11. Taylor expanded in im around inf

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

       1. *-commutative N/A

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

       2. lower-*.f64 N/A

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

       3. pow2 N/A

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

       4. lift-*.f64 N/A

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

       5. lift-*.f64 12.6

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

   13. Applied rewrites 12.6%

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

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in re around 0

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

      1. lower-+.f64 31.7

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

   7. Applied rewrites 31.7%

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

Alternative 15: 36.7% accurate, 0.8× speedup

Math

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

FPCore

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

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * cos(im)) <= 0.98) {
		tmp = ((im * im) * re) * -0.5;
	} else {
		tmp = 1.0 * fma((im * im), -0.5, 1.0);
	}
	return tmp;
}

Julia

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

Wolfram

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

Derivation

1. Split input into 2 regimes

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

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. distribute-rgt1-in N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. lift-cos.f64 N/A

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

      6. +-commutative N/A

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

      7. metadata-eval N/A

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

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

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

      9. metadata-eval N/A

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

      10. metadata-eval N/A

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

      11. metadata-eval N/A

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

      12. lower--.f64 N/A

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

      13. metadata-eval 51.6

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

   4. Applied rewrites 51.6%

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

   5. Taylor expanded in re around inf

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

      1. *-commutative N/A

         \[\leadsto \cos im \cdot re \]

      2. lower-*.f64 N/A

         \[\leadsto \cos im \cdot re \]

      3. lift-cos.f64 3.9

         \[\leadsto \cos im \cdot re \]

   7. Applied rewrites 3.9%

      \[\leadsto \cos im \cdot \color{blue}{re} \]

   8. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. pow2 N/A

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

      6. lift-*.f64 7.7

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

   10. Applied rewrites 7.7%

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

   11. Taylor expanded in im around inf

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

       1. *-commutative N/A

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

       2. lower-*.f64 N/A

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

       3. pow2 N/A

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

       4. lift-*.f64 N/A

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

       5. lift-*.f64 12.6

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

   13. Applied rewrites 12.6%

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

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. unpow2 N/A

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

      5. lower-*.f64 63.2

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

   4. Applied rewrites 63.2%

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

   5. Taylor expanded in re around 0

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

   7. Applied rewrites 29.7%

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

Alternative 16: 12.6% accurate, 4.7× speedup

Math

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

FPCore

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

C

double code(double re, double im) {
	return ((im * im) * re) * -0.5;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

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

Java

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

Python

def code(re, im):
	return ((im * im) * re) * -0.5

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

2. Taylor expanded in re around 0

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

   1. distribute-rgt1-in N/A

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

   2. +-commutative N/A

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

   3. *-commutative N/A

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

   4. lower-*.f64 N/A

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

   5. lift-cos.f64 N/A

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

   6. +-commutative N/A

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

   7. metadata-eval N/A

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

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

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

   9. metadata-eval N/A

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

   10. metadata-eval N/A

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

   11. metadata-eval N/A

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

   12. lower--.f64 N/A

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

   13. metadata-eval 51.6

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

4. Applied rewrites 51.6%

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

5. Taylor expanded in re around inf

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

   1. *-commutative N/A

      \[\leadsto \cos im \cdot re \]

   2. lower-*.f64 N/A

      \[\leadsto \cos im \cdot re \]

   3. lift-cos.f64 3.9

      \[\leadsto \cos im \cdot re \]

7. Applied rewrites 3.9%

   \[\leadsto \cos im \cdot \color{blue}{re} \]

8. Taylor expanded in im around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

   4. lower-*.f64 N/A

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

   5. pow2 N/A

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

   6. lift-*.f64 7.7

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

10. Applied rewrites 7.7%

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

11. Taylor expanded in im around inf

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

    1. *-commutative N/A

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

    2. lower-*.f64 N/A

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

    3. pow2 N/A

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

    4. lift-*.f64 N/A

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

    5. lift-*.f64 12.6

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

13. Applied rewrites 12.6%

    \[\leadsto \left(\left(im \cdot im\right) \cdot re\right) \cdot -0.5 \]
14. Add Preprocessing

Reproduce

herbie shell --seed 2025139 
(FPCore (re im)
  :name "math.exp on complex, real part"
  :precision binary64
  (* (exp re) (cos im)))