math.sin on complex, imaginary part

Percentage Accurate: 55.4% → 99.7%

Time: 13.3s

Alternatives: 17

Speedup: 0.9×

• 50.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (* (* 0.5 (cos re)) (- (exp (- 0.0 im)) (exp im))))

C

double code(double re, double im) {
	return (0.5 * cos(re)) * (exp((0.0 - im)) - exp(im));
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = (0.5d0 * cos(re)) * (exp((0.0d0 - im)) - exp(im))
end function

Java

public static double code(double re, double im) {
	return (0.5 * Math.cos(re)) * (Math.exp((0.0 - im)) - Math.exp(im));
}

Python

def code(re, im):
	return (0.5 * math.cos(re)) * (math.exp((0.0 - im)) - math.exp(im))

Julia

function code(re, im)
	return Float64(Float64(0.5 * cos(re)) * Float64(exp(Float64(0.0 - im)) - exp(im)))
end

MATLAB

function tmp = code(re, im)
	tmp = (0.5 * cos(re)) * (exp((0.0 - im)) - exp(im));
end

Wolfram

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

Initial Program: 55.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (* (* 0.5 (cos re)) (- (exp (- 0.0 im)) (exp im))))

C

double code(double re, double im) {
	return (0.5 * cos(re)) * (exp((0.0 - im)) - exp(im));
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = (0.5d0 * cos(re)) * (exp((0.0d0 - im)) - exp(im))
end function

Java

public static double code(double re, double im) {
	return (0.5 * Math.cos(re)) * (Math.exp((0.0 - im)) - Math.exp(im));
}

Python

def code(re, im):
	return (0.5 * math.cos(re)) * (math.exp((0.0 - im)) - math.exp(im))

Julia

function code(re, im)
	return Float64(Float64(0.5 * cos(re)) * Float64(exp(Float64(0.0 - im)) - exp(im)))
end

MATLAB

function tmp = code(re, im)
	tmp = (0.5 * cos(re)) * (exp((0.0 - im)) - exp(im));
end

Wolfram

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

Alternative 1: 99.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ \begin{array}{l} t_0 := e^{-im\_m}\\ im\_s \cdot \begin{array}{l} \mathbf{if}\;t\_0 - e^{im\_m} \leq -50000:\\ \;\;\;\;\mathsf{fma}\left(-e^{im\_m} \cdot 0.5, \cos re, \left(0.5 \cdot \cos re\right) \cdot t\_0\right)\\ \mathbf{else}:\\ \;\;\;\;\left(im\_m \cdot \cos re\right) \cdot \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m \cdot im\_m, -0.008333333333333333, -0.16666666666666666\right), -1\right)\\ \end{array} \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (let* ((t_0 (exp (- im_m))))
   (*
    im_s
    (if (<= (- t_0 (exp im_m)) -50000.0)
      (fma (- (* (exp im_m) 0.5)) (cos re) (* (* 0.5 (cos re)) t_0))
      (*
       (* im_m (cos re))
       (fma
        (* im_m im_m)
        (fma (* im_m im_m) -0.008333333333333333 -0.16666666666666666)
        -1.0))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double t_0 = exp(-im_m);
	double tmp;
	if ((t_0 - exp(im_m)) <= -50000.0) {
		tmp = fma(-(exp(im_m) * 0.5), cos(re), ((0.5 * cos(re)) * t_0));
	} else {
		tmp = (im_m * cos(re)) * fma((im_m * im_m), fma((im_m * im_m), -0.008333333333333333, -0.16666666666666666), -1.0);
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	t_0 = exp(Float64(-im_m))
	tmp = 0.0
	if (Float64(t_0 - exp(im_m)) <= -50000.0)
		tmp = fma(Float64(-Float64(exp(im_m) * 0.5)), cos(re), Float64(Float64(0.5 * cos(re)) * t_0));
	else
		tmp = Float64(Float64(im_m * cos(re)) * fma(Float64(im_m * im_m), fma(Float64(im_m * im_m), -0.008333333333333333, -0.16666666666666666), -1.0));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := Block[{t$95$0 = N[Exp[(-im$95$m)], $MachinePrecision]}, N[(im$95$s * If[LessEqual[N[(t$95$0 - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision], -50000.0], N[((-N[(N[Exp[im$95$m], $MachinePrecision] * 0.5), $MachinePrecision]) * N[Cos[re], $MachinePrecision] + N[(N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision], N[(N[(im$95$m * N[Cos[re], $MachinePrecision]), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * -0.008333333333333333 + -0.16666666666666666), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)) < -5e4

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right)} \]

      2. lift--.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \cos re\right) \cdot \color{blue}{\left(e^{0 - im} - e^{im}\right)} \]

      3. sub-neg N/A

         \[\leadsto \left(\frac{1}{2} \cdot \cos re\right) \cdot \color{blue}{\left(e^{0 - im} + \left(\mathsf{neg}\left(e^{im}\right)\right)\right)} \]

      4. +-commutative N/A

         \[\leadsto \left(\frac{1}{2} \cdot \cos re\right) \cdot \color{blue}{\left(\left(\mathsf{neg}\left(e^{im}\right)\right) + e^{0 - im}\right)} \]

      5. distribute-rgt-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(e^{im}\right)\right) \cdot \left(\frac{1}{2} \cdot \cos re\right) + e^{0 - im} \cdot \left(\frac{1}{2} \cdot \cos re\right)} \]

      6. lift-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(e^{im}\right)\right) \cdot \color{blue}{\left(\frac{1}{2} \cdot \cos re\right)} + e^{0 - im} \cdot \left(\frac{1}{2} \cdot \cos re\right) \]

      7. associate-*r* N/A

         \[\leadsto \color{blue}{\left(\left(\mathsf{neg}\left(e^{im}\right)\right) \cdot \frac{1}{2}\right) \cdot \cos re} + e^{0 - im} \cdot \left(\frac{1}{2} \cdot \cos re\right) \]

      8. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\mathsf{neg}\left(e^{im}\right)\right) \cdot \frac{1}{2}, \cos re, e^{0 - im} \cdot \left(\frac{1}{2} \cdot \cos re\right)\right)} \]

      9. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\mathsf{neg}\left(e^{im}\right)\right) \cdot \frac{1}{2}}, \cos re, e^{0 - im} \cdot \left(\frac{1}{2} \cdot \cos re\right)\right) \]

      10. lower-neg.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\mathsf{neg}\left(e^{im}\right)\right)} \cdot \frac{1}{2}, \cos re, e^{0 - im} \cdot \left(\frac{1}{2} \cdot \cos re\right)\right) \]

      11. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(\left(\mathsf{neg}\left(e^{im}\right)\right) \cdot \frac{1}{2}, \cos re, \color{blue}{\left(\frac{1}{2} \cdot \cos re\right) \cdot e^{0 - im}}\right) \]

      12. lower-*.f64 100.0

          \[\leadsto \mathsf{fma}\left(\left(-e^{im}\right) \cdot 0.5, \cos re, \color{blue}{\left(0.5 \cdot \cos re\right) \cdot e^{0 - im}}\right) \]

      13. lift--.f64 N/A

          \[\leadsto \mathsf{fma}\left(\left(\mathsf{neg}\left(e^{im}\right)\right) \cdot \frac{1}{2}, \cos re, \left(\frac{1}{2} \cdot \cos re\right) \cdot e^{\color{blue}{0 - im}}\right) \]

      14. sub0-neg N/A

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

      15. lower-neg.f64 100.0

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

   4. Applied rewrites 100.0%

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

   if -5e4 < (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))

   1. Initial program 39.4%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

      \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)\right)} \]
   4. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)\right)} \]

      2. +-commutative N/A

         \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) + -1 \cdot \cos re\right)} \]

      3. +-commutative N/A

         \[\leadsto im \cdot \left({im}^{2} \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right) + \frac{-1}{6} \cdot \cos re\right)} + -1 \cdot \cos re\right) \]

      4. distribute-rgt-in N/A

         \[\leadsto im \cdot \left(\color{blue}{\left(\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) \cdot {im}^{2} + \left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2}\right)} + -1 \cdot \cos re\right) \]

      5. *-commutative N/A

         \[\leadsto im \cdot \left(\left(\color{blue}{{im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)} + \left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2}\right) + -1 \cdot \cos re\right) \]

      6. associate-+l+ N/A

         \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) + \left(\left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2} + -1 \cdot \cos re\right)\right)} \]

   5. Applied rewrites 95.1%

      \[\leadsto \color{blue}{im \cdot \left(\cos re \cdot \mathsf{fma}\left(\mathsf{fma}\left(im \cdot im, -0.0001984126984126984, -0.008333333333333333\right), im \cdot \left(im \cdot \left(im \cdot im\right)\right), \mathsf{fma}\left(im, im \cdot -0.16666666666666666, -1\right)\right)\right)} \]

   6. Taylor expanded in im around 0

      \[\leadsto im \cdot \left(\cos re \cdot \left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{5040} \cdot {im}^{2} - \frac{1}{120}\right) - \frac{1}{6}\right) - \color{blue}{1}\right)\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 95.1%

      \[\leadsto im \cdot \left(\cos re \cdot \mathsf{fma}\left(im \cdot im, \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right)}, -1\right)\right) \]
   9. Step-by-step derivation

   10. Applied rewrites 95.1%

       \[\leadsto \left(im \cdot \cos re\right) \cdot \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)} \]

   11. Taylor expanded in im around 0

       \[\leadsto \left(im \cdot \cos re\right) \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \frac{-1}{120}, \frac{-1}{6}\right), -1\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 94.6%

       \[\leadsto \left(im \cdot \cos re\right) \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, -0.008333333333333333, -0.16666666666666666\right), -1\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 95.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;e^{-im} - e^{im} \leq -50000:\\ \;\;\;\;\mathsf{fma}\left(-e^{im} \cdot 0.5, \cos re, \left(0.5 \cdot \cos re\right) \cdot e^{-im}\right)\\ \mathbf{else}:\\ \;\;\;\;\left(im \cdot \cos re\right) \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, -0.008333333333333333, -0.16666666666666666\right), -1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 98.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ \begin{array}{l} t_0 := \left(im\_m \cdot im\_m\right) \cdot -0.3333333333333333\\ t_1 := 0.5 \cdot \cos re\\ t_2 := \left(e^{-im\_m} - e^{im\_m}\right) \cdot t\_1\\ im\_s \cdot \begin{array}{l} \mathbf{if}\;t\_2 \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im\_m}\right)\\ \mathbf{elif}\;t\_2 \leq 50000:\\ \;\;\;\;t\_1 \cdot \mathsf{fma}\left(t\_0, im\_m, im\_m \cdot -2\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im\_m \cdot t\_0\right)\\ \end{array} \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (let* ((t_0 (* (* im_m im_m) -0.3333333333333333))
        (t_1 (* 0.5 (cos re)))
        (t_2 (* (- (exp (- im_m)) (exp im_m)) t_1)))
   (*
    im_s
    (if (<= t_2 (- INFINITY))
      (* 0.5 (- 1.0 (exp im_m)))
      (if (<= t_2 50000.0)
        (* t_1 (fma t_0 im_m (* im_m -2.0)))
        (* (fma -0.25 (* re re) 0.5) (* im_m t_0)))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double t_0 = (im_m * im_m) * -0.3333333333333333;
	double t_1 = 0.5 * cos(re);
	double t_2 = (exp(-im_m) - exp(im_m)) * t_1;
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = 0.5 * (1.0 - exp(im_m));
	} else if (t_2 <= 50000.0) {
		tmp = t_1 * fma(t_0, im_m, (im_m * -2.0));
	} else {
		tmp = fma(-0.25, (re * re), 0.5) * (im_m * t_0);
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	t_0 = Float64(Float64(im_m * im_m) * -0.3333333333333333)
	t_1 = Float64(0.5 * cos(re))
	t_2 = Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * t_1)
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = Float64(0.5 * Float64(1.0 - exp(im_m)));
	elseif (t_2 <= 50000.0)
		tmp = Float64(t_1 * fma(t_0, im_m, Float64(im_m * -2.0)));
	else
		tmp = Float64(fma(-0.25, Float64(re * re), 0.5) * Float64(im_m * t_0));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := Block[{t$95$0 = N[(N[(im$95$m * im$95$m), $MachinePrecision] * -0.3333333333333333), $MachinePrecision]}, Block[{t$95$1 = N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision]}, N[(im$95$s * If[LessEqual[t$95$2, (-Infinity)], N[(0.5 * N[(1.0 - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 50000.0], N[(t$95$1 * N[(t$95$0 * im$95$m + N[(im$95$m * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(-0.25 * N[(re * re), $MachinePrecision] + 0.5), $MachinePrecision] * N[(im$95$m * t$95$0), $MachinePrecision]), $MachinePrecision]]]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

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

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 77.2

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

   5. Applied rewrites 77.2%

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

   6. Taylor expanded in im around 0

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

   8. Applied rewrites 77.3%

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

   if -inf.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < 5e4

   1. Initial program 8.6%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. sub-neg N/A

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

      3. *-commutative N/A

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

      4. metadata-eval N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 99.0

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

   5. Applied rewrites 99.0%

      \[\leadsto \left(0.5 \cdot \cos re\right) \cdot \color{blue}{\left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 99.0%

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

   if 5e4 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. sub-neg N/A

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

      3. *-commutative N/A

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

      4. metadata-eval N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 79.4

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

   5. Applied rewrites 79.4%

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

   6. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\left(\frac{1}{2} + \frac{-1}{4} \cdot {re}^{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]
   7. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{-1}{4} \cdot {re}^{2} + \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      2. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{4}, {re}^{2}, \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, \color{blue}{re \cdot re}, \frac{1}{2}\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      4. lower-*.f64 73.1

         \[\leadsto \mathsf{fma}\left(-0.25, \color{blue}{re \cdot re}, 0.5\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   8. Applied rewrites 73.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   9. Taylor expanded in im around inf

      \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, re \cdot re, \frac{1}{2}\right) \cdot \left(im \cdot \left(\frac{-1}{3} \cdot \color{blue}{{im}^{2}}\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 73.1%

       \[\leadsto \mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot \color{blue}{-0.3333333333333333}\right)\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 87.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im}\right)\\ \mathbf{elif}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 50000:\\ \;\;\;\;\left(0.5 \cdot \cos re\right) \cdot \mathsf{fma}\left(\left(im \cdot im\right) \cdot -0.3333333333333333, im, im \cdot -2\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot -0.3333333333333333\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 98.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ \begin{array}{l} t_0 := \left(e^{-im\_m} - e^{im\_m}\right) \cdot \left(0.5 \cdot \cos re\right)\\ im\_s \cdot \begin{array}{l} \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im\_m}\right)\\ \mathbf{elif}\;t\_0 \leq 50000:\\ \;\;\;\;im\_m \cdot \left(\cos re \cdot \mathsf{fma}\left(im\_m, im\_m \cdot -0.16666666666666666, -1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im\_m \cdot \left(\left(im\_m \cdot im\_m\right) \cdot -0.3333333333333333\right)\right)\\ \end{array} \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (let* ((t_0 (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re)))))
   (*
    im_s
    (if (<= t_0 (- INFINITY))
      (* 0.5 (- 1.0 (exp im_m)))
      (if (<= t_0 50000.0)
        (* im_m (* (cos re) (fma im_m (* im_m -0.16666666666666666) -1.0)))
        (*
         (fma -0.25 (* re re) 0.5)
         (* im_m (* (* im_m im_m) -0.3333333333333333))))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double t_0 = (exp(-im_m) - exp(im_m)) * (0.5 * cos(re));
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = 0.5 * (1.0 - exp(im_m));
	} else if (t_0 <= 50000.0) {
		tmp = im_m * (cos(re) * fma(im_m, (im_m * -0.16666666666666666), -1.0));
	} else {
		tmp = fma(-0.25, (re * re), 0.5) * (im_m * ((im_m * im_m) * -0.3333333333333333));
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	t_0 = Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re)))
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(0.5 * Float64(1.0 - exp(im_m)));
	elseif (t_0 <= 50000.0)
		tmp = Float64(im_m * Float64(cos(re) * fma(im_m, Float64(im_m * -0.16666666666666666), -1.0)));
	else
		tmp = Float64(fma(-0.25, Float64(re * re), 0.5) * Float64(im_m * Float64(Float64(im_m * im_m) * -0.3333333333333333)));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := Block[{t$95$0 = N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(im$95$s * If[LessEqual[t$95$0, (-Infinity)], N[(0.5 * N[(1.0 - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 50000.0], N[(im$95$m * N[(N[Cos[re], $MachinePrecision] * N[(im$95$m * N[(im$95$m * -0.16666666666666666), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(-0.25 * N[(re * re), $MachinePrecision] + 0.5), $MachinePrecision] * N[(im$95$m * N[(N[(im$95$m * im$95$m), $MachinePrecision] * -0.3333333333333333), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]), $MachinePrecision]]

Derivation

1. Split input into 3 regimes

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

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 77.2

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

   5. Applied rewrites 77.2%

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

   6. Taylor expanded in im around 0

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

   8. Applied rewrites 77.3%

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

   if -inf.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < 5e4

   1. Initial program 8.6%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. *-commutative N/A

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

      2. associate-*r* N/A

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

      3. lower-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. *-commutative N/A

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

      6. associate-*r* N/A

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

      7. distribute-rgt-out N/A

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

      8. lower-*.f64 N/A

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

      9. lower-cos.f64 N/A

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

      10. +-commutative N/A

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

      11. unpow2 N/A

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

      12. associate-*r* N/A

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

      13. *-commutative N/A

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

      14. lower-fma.f64 N/A

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

      15. *-commutative N/A

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

      16. lower-*.f64 99.0

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

   5. Applied rewrites 99.0%

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

   if 5e4 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. sub-neg N/A

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

      3. *-commutative N/A

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

      4. metadata-eval N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 79.4

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

   5. Applied rewrites 79.4%

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

   6. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\left(\frac{1}{2} + \frac{-1}{4} \cdot {re}^{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]
   7. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{-1}{4} \cdot {re}^{2} + \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      2. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{4}, {re}^{2}, \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, \color{blue}{re \cdot re}, \frac{1}{2}\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      4. lower-*.f64 73.1

         \[\leadsto \mathsf{fma}\left(-0.25, \color{blue}{re \cdot re}, 0.5\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   8. Applied rewrites 73.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   9. Taylor expanded in im around inf

      \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, re \cdot re, \frac{1}{2}\right) \cdot \left(im \cdot \left(\frac{-1}{3} \cdot \color{blue}{{im}^{2}}\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 73.1%

       \[\leadsto \mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot \color{blue}{-0.3333333333333333}\right)\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 87.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im}\right)\\ \mathbf{elif}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 50000:\\ \;\;\;\;im \cdot \left(\cos re \cdot \mathsf{fma}\left(im, im \cdot -0.16666666666666666, -1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot -0.3333333333333333\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 98.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ \begin{array}{l} t_0 := \left(e^{-im\_m} - e^{im\_m}\right) \cdot \left(0.5 \cdot \cos re\right)\\ im\_s \cdot \begin{array}{l} \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im\_m}\right)\\ \mathbf{elif}\;t\_0 \leq 50000:\\ \;\;\;\;\cos re \cdot \left(-im\_m\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im\_m \cdot \left(\left(im\_m \cdot im\_m\right) \cdot -0.3333333333333333\right)\right)\\ \end{array} \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (let* ((t_0 (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re)))))
   (*
    im_s
    (if (<= t_0 (- INFINITY))
      (* 0.5 (- 1.0 (exp im_m)))
      (if (<= t_0 50000.0)
        (* (cos re) (- im_m))
        (*
         (fma -0.25 (* re re) 0.5)
         (* im_m (* (* im_m im_m) -0.3333333333333333))))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double t_0 = (exp(-im_m) - exp(im_m)) * (0.5 * cos(re));
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = 0.5 * (1.0 - exp(im_m));
	} else if (t_0 <= 50000.0) {
		tmp = cos(re) * -im_m;
	} else {
		tmp = fma(-0.25, (re * re), 0.5) * (im_m * ((im_m * im_m) * -0.3333333333333333));
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	t_0 = Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re)))
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(0.5 * Float64(1.0 - exp(im_m)));
	elseif (t_0 <= 50000.0)
		tmp = Float64(cos(re) * Float64(-im_m));
	else
		tmp = Float64(fma(-0.25, Float64(re * re), 0.5) * Float64(im_m * Float64(Float64(im_m * im_m) * -0.3333333333333333)));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := Block[{t$95$0 = N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(im$95$s * If[LessEqual[t$95$0, (-Infinity)], N[(0.5 * N[(1.0 - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 50000.0], N[(N[Cos[re], $MachinePrecision] * (-im$95$m)), $MachinePrecision], N[(N[(-0.25 * N[(re * re), $MachinePrecision] + 0.5), $MachinePrecision] * N[(im$95$m * N[(N[(im$95$m * im$95$m), $MachinePrecision] * -0.3333333333333333), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]), $MachinePrecision]]

Derivation

1. Split input into 3 regimes

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

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 77.2

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

   5. Applied rewrites 77.2%

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

   6. Taylor expanded in im around 0

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

   8. Applied rewrites 77.3%

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

   if -inf.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < 5e4

   1. Initial program 8.6%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-cos.f64 98.4

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

   5. Applied rewrites 98.4%

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

   if 5e4 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. sub-neg N/A

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

      3. *-commutative N/A

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

      4. metadata-eval N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 79.4

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

   5. Applied rewrites 79.4%

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

   6. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\left(\frac{1}{2} + \frac{-1}{4} \cdot {re}^{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]
   7. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{-1}{4} \cdot {re}^{2} + \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      2. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{4}, {re}^{2}, \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, \color{blue}{re \cdot re}, \frac{1}{2}\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      4. lower-*.f64 73.1

         \[\leadsto \mathsf{fma}\left(-0.25, \color{blue}{re \cdot re}, 0.5\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   8. Applied rewrites 73.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   9. Taylor expanded in im around inf

      \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, re \cdot re, \frac{1}{2}\right) \cdot \left(im \cdot \left(\frac{-1}{3} \cdot \color{blue}{{im}^{2}}\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 73.1%

       \[\leadsto \mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot \color{blue}{-0.3333333333333333}\right)\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 86.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im}\right)\\ \mathbf{elif}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 50000:\\ \;\;\;\;\cos re \cdot \left(-im\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot -0.3333333333333333\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 93.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ \begin{array}{l} t_0 := \left(e^{-im\_m} - e^{im\_m}\right) \cdot \left(0.5 \cdot \cos re\right)\\ im\_s \cdot \begin{array}{l} \mathbf{if}\;t\_0 \leq -5 \cdot 10^{-8}:\\ \;\;\;\;im\_m \cdot \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m, im\_m \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)\\ \mathbf{elif}\;t\_0 \leq 50000:\\ \;\;\;\;\cos re \cdot \left(-im\_m\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im\_m \cdot \left(\left(im\_m \cdot im\_m\right) \cdot -0.3333333333333333\right)\right)\\ \end{array} \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (let* ((t_0 (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re)))))
   (*
    im_s
    (if (<= t_0 -5e-8)
      (*
       im_m
       (fma
        (* im_m im_m)
        (fma
         (* im_m im_m)
         (fma im_m (* im_m -0.0001984126984126984) -0.008333333333333333)
         -0.16666666666666666)
        -1.0))
      (if (<= t_0 50000.0)
        (* (cos re) (- im_m))
        (*
         (fma -0.25 (* re re) 0.5)
         (* im_m (* (* im_m im_m) -0.3333333333333333))))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double t_0 = (exp(-im_m) - exp(im_m)) * (0.5 * cos(re));
	double tmp;
	if (t_0 <= -5e-8) {
		tmp = im_m * fma((im_m * im_m), fma((im_m * im_m), fma(im_m, (im_m * -0.0001984126984126984), -0.008333333333333333), -0.16666666666666666), -1.0);
	} else if (t_0 <= 50000.0) {
		tmp = cos(re) * -im_m;
	} else {
		tmp = fma(-0.25, (re * re), 0.5) * (im_m * ((im_m * im_m) * -0.3333333333333333));
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	t_0 = Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re)))
	tmp = 0.0
	if (t_0 <= -5e-8)
		tmp = Float64(im_m * fma(Float64(im_m * im_m), fma(Float64(im_m * im_m), fma(im_m, Float64(im_m * -0.0001984126984126984), -0.008333333333333333), -0.16666666666666666), -1.0));
	elseif (t_0 <= 50000.0)
		tmp = Float64(cos(re) * Float64(-im_m));
	else
		tmp = Float64(fma(-0.25, Float64(re * re), 0.5) * Float64(im_m * Float64(Float64(im_m * im_m) * -0.3333333333333333)));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := Block[{t$95$0 = N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(im$95$s * If[LessEqual[t$95$0, -5e-8], N[(im$95$m * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(im$95$m * N[(im$95$m * -0.0001984126984126984), $MachinePrecision] + -0.008333333333333333), $MachinePrecision] + -0.16666666666666666), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 50000.0], N[(N[Cos[re], $MachinePrecision] * (-im$95$m)), $MachinePrecision], N[(N[(-0.25 * N[(re * re), $MachinePrecision] + 0.5), $MachinePrecision] * N[(im$95$m * N[(N[(im$95$m * im$95$m), $MachinePrecision] * -0.3333333333333333), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]), $MachinePrecision]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < -4.9999999999999998e-8

   1. Initial program 99.2%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 75.9

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

   5. Applied rewrites 75.9%

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

   6. Taylor expanded in im around 0

      \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{5040} \cdot {im}^{2} - \frac{1}{120}\right) - \frac{1}{6}\right) - 1\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 68.7%

      \[\leadsto im \cdot \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)} \]

   if -4.9999999999999998e-8 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < 5e4

   1. Initial program 6.9%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-cos.f64 99.3

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

   5. Applied rewrites 99.3%

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

   if 5e4 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. sub-neg N/A

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

      3. *-commutative N/A

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

      4. metadata-eval N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 79.4

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

   5. Applied rewrites 79.4%

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

   6. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\left(\frac{1}{2} + \frac{-1}{4} \cdot {re}^{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]
   7. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{-1}{4} \cdot {re}^{2} + \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      2. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{4}, {re}^{2}, \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, \color{blue}{re \cdot re}, \frac{1}{2}\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      4. lower-*.f64 73.1

         \[\leadsto \mathsf{fma}\left(-0.25, \color{blue}{re \cdot re}, 0.5\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   8. Applied rewrites 73.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   9. Taylor expanded in im around inf

      \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, re \cdot re, \frac{1}{2}\right) \cdot \left(im \cdot \left(\frac{-1}{3} \cdot \color{blue}{{im}^{2}}\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 73.1%

       \[\leadsto \mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot \color{blue}{-0.3333333333333333}\right)\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 85.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -5 \cdot 10^{-8}:\\ \;\;\;\;im \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)\\ \mathbf{elif}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 50000:\\ \;\;\;\;\cos re \cdot \left(-im\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot -0.3333333333333333\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 99.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ \begin{array}{l} t_0 := e^{-im\_m} - e^{im\_m}\\ im\_s \cdot \begin{array}{l} \mathbf{if}\;t\_0 \leq -50000:\\ \;\;\;\;t\_0 \cdot \left(0.5 \cdot \cos re\right)\\ \mathbf{else}:\\ \;\;\;\;\left(im\_m \cdot \cos re\right) \cdot \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m \cdot im\_m, -0.008333333333333333, -0.16666666666666666\right), -1\right)\\ \end{array} \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (let* ((t_0 (- (exp (- im_m)) (exp im_m))))
   (*
    im_s
    (if (<= t_0 -50000.0)
      (* t_0 (* 0.5 (cos re)))
      (*
       (* im_m (cos re))
       (fma
        (* im_m im_m)
        (fma (* im_m im_m) -0.008333333333333333 -0.16666666666666666)
        -1.0))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double t_0 = exp(-im_m) - exp(im_m);
	double tmp;
	if (t_0 <= -50000.0) {
		tmp = t_0 * (0.5 * cos(re));
	} else {
		tmp = (im_m * cos(re)) * fma((im_m * im_m), fma((im_m * im_m), -0.008333333333333333, -0.16666666666666666), -1.0);
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	t_0 = Float64(exp(Float64(-im_m)) - exp(im_m))
	tmp = 0.0
	if (t_0 <= -50000.0)
		tmp = Float64(t_0 * Float64(0.5 * cos(re)));
	else
		tmp = Float64(Float64(im_m * cos(re)) * fma(Float64(im_m * im_m), fma(Float64(im_m * im_m), -0.008333333333333333, -0.16666666666666666), -1.0));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := Block[{t$95$0 = N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision]}, N[(im$95$s * If[LessEqual[t$95$0, -50000.0], N[(t$95$0 * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(im$95$m * N[Cos[re], $MachinePrecision]), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * -0.008333333333333333 + -0.16666666666666666), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)) < -5e4

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right)} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\left(e^{0 - im} - e^{im}\right) \cdot \left(\frac{1}{2} \cdot \cos re\right)} \]

      3. lower-*.f64 100.0

         \[\leadsto \color{blue}{\left(e^{0 - im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right)} \]

      4. lift--.f64 N/A

         \[\leadsto \left(e^{\color{blue}{0 - im}} - e^{im}\right) \cdot \left(\frac{1}{2} \cdot \cos re\right) \]

      5. sub0-neg N/A

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

      6. lower-neg.f64 100.0

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

   4. Applied rewrites 100.0%

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

   if -5e4 < (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))

   1. Initial program 39.4%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

      \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)\right)} \]
   4. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)\right)} \]

      2. +-commutative N/A

         \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) + -1 \cdot \cos re\right)} \]

      3. +-commutative N/A

         \[\leadsto im \cdot \left({im}^{2} \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right) + \frac{-1}{6} \cdot \cos re\right)} + -1 \cdot \cos re\right) \]

      4. distribute-rgt-in N/A

         \[\leadsto im \cdot \left(\color{blue}{\left(\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) \cdot {im}^{2} + \left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2}\right)} + -1 \cdot \cos re\right) \]

      5. *-commutative N/A

         \[\leadsto im \cdot \left(\left(\color{blue}{{im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)} + \left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2}\right) + -1 \cdot \cos re\right) \]

      6. associate-+l+ N/A

         \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) + \left(\left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2} + -1 \cdot \cos re\right)\right)} \]

   5. Applied rewrites 95.1%

      \[\leadsto \color{blue}{im \cdot \left(\cos re \cdot \mathsf{fma}\left(\mathsf{fma}\left(im \cdot im, -0.0001984126984126984, -0.008333333333333333\right), im \cdot \left(im \cdot \left(im \cdot im\right)\right), \mathsf{fma}\left(im, im \cdot -0.16666666666666666, -1\right)\right)\right)} \]

   6. Taylor expanded in im around 0

      \[\leadsto im \cdot \left(\cos re \cdot \left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{5040} \cdot {im}^{2} - \frac{1}{120}\right) - \frac{1}{6}\right) - \color{blue}{1}\right)\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 95.1%

      \[\leadsto im \cdot \left(\cos re \cdot \mathsf{fma}\left(im \cdot im, \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right)}, -1\right)\right) \]
   9. Step-by-step derivation

   10. Applied rewrites 95.1%

       \[\leadsto \left(im \cdot \cos re\right) \cdot \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)} \]

   11. Taylor expanded in im around 0

       \[\leadsto \left(im \cdot \cos re\right) \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \frac{-1}{120}, \frac{-1}{6}\right), -1\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 94.6%

       \[\leadsto \left(im \cdot \cos re\right) \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, -0.008333333333333333, -0.16666666666666666\right), -1\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 95.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;e^{-im} - e^{im} \leq -50000:\\ \;\;\;\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right)\\ \mathbf{else}:\\ \;\;\;\;\left(im \cdot \cos re\right) \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, -0.008333333333333333, -0.16666666666666666\right), -1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 97.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ im\_s \cdot \begin{array}{l} \mathbf{if}\;\left(e^{-im\_m} - e^{im\_m}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im\_m}\right)\\ \mathbf{else}:\\ \;\;\;\;\left(im\_m \cdot \cos re\right) \cdot \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m, im\_m \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)\\ \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (*
  im_s
  (if (<= (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re))) (- INFINITY))
    (* 0.5 (- 1.0 (exp im_m)))
    (*
     (* im_m (cos re))
     (fma
      (* im_m im_m)
      (fma
       (* im_m im_m)
       (fma im_m (* im_m -0.0001984126984126984) -0.008333333333333333)
       -0.16666666666666666)
      -1.0)))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double tmp;
	if (((exp(-im_m) - exp(im_m)) * (0.5 * cos(re))) <= -((double) INFINITY)) {
		tmp = 0.5 * (1.0 - exp(im_m));
	} else {
		tmp = (im_m * cos(re)) * fma((im_m * im_m), fma((im_m * im_m), fma(im_m, (im_m * -0.0001984126984126984), -0.008333333333333333), -0.16666666666666666), -1.0);
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	tmp = 0.0
	if (Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re))) <= Float64(-Inf))
		tmp = Float64(0.5 * Float64(1.0 - exp(im_m)));
	else
		tmp = Float64(Float64(im_m * cos(re)) * fma(Float64(im_m * im_m), fma(Float64(im_m * im_m), fma(im_m, Float64(im_m * -0.0001984126984126984), -0.008333333333333333), -0.16666666666666666), -1.0));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := N[(im$95$s * If[LessEqual[N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], (-Infinity)], N[(0.5 * N[(1.0 - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(im$95$m * N[Cos[re], $MachinePrecision]), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(im$95$m * N[(im$95$m * -0.0001984126984126984), $MachinePrecision] + -0.008333333333333333), $MachinePrecision] + -0.16666666666666666), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

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

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 77.2

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

   5. Applied rewrites 77.2%

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

   6. Taylor expanded in im around 0

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

   8. Applied rewrites 77.3%

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

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

   1. Initial program 40.3%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

      \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)\right)} \]
   4. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)\right)} \]

      2. +-commutative N/A

         \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) + -1 \cdot \cos re\right)} \]

      3. +-commutative N/A

         \[\leadsto im \cdot \left({im}^{2} \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right) + \frac{-1}{6} \cdot \cos re\right)} + -1 \cdot \cos re\right) \]

      4. distribute-rgt-in N/A

         \[\leadsto im \cdot \left(\color{blue}{\left(\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) \cdot {im}^{2} + \left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2}\right)} + -1 \cdot \cos re\right) \]

      5. *-commutative N/A

         \[\leadsto im \cdot \left(\left(\color{blue}{{im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)} + \left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2}\right) + -1 \cdot \cos re\right) \]

      6. associate-+l+ N/A

         \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) + \left(\left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2} + -1 \cdot \cos re\right)\right)} \]

   5. Applied rewrites 95.6%

      \[\leadsto \color{blue}{im \cdot \left(\cos re \cdot \mathsf{fma}\left(\mathsf{fma}\left(im \cdot im, -0.0001984126984126984, -0.008333333333333333\right), im \cdot \left(im \cdot \left(im \cdot im\right)\right), \mathsf{fma}\left(im, im \cdot -0.16666666666666666, -1\right)\right)\right)} \]

   6. Taylor expanded in im around 0

      \[\leadsto im \cdot \left(\cos re \cdot \left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{5040} \cdot {im}^{2} - \frac{1}{120}\right) - \frac{1}{6}\right) - \color{blue}{1}\right)\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 95.6%

      \[\leadsto im \cdot \left(\cos re \cdot \mathsf{fma}\left(im \cdot im, \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right)}, -1\right)\right) \]
   9. Step-by-step derivation

   10. Applied rewrites 95.6%

       \[\leadsto \left(im \cdot \cos re\right) \cdot \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 91.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im}\right)\\ \mathbf{else}:\\ \;\;\;\;\left(im \cdot \cos re\right) \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 97.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ im\_s \cdot \begin{array}{l} \mathbf{if}\;\left(e^{-im\_m} - e^{im\_m}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im\_m}\right)\\ \mathbf{else}:\\ \;\;\;\;im\_m \cdot \left(\cos re \cdot \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m, im\_m \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)\right)\\ \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (*
  im_s
  (if (<= (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re))) (- INFINITY))
    (* 0.5 (- 1.0 (exp im_m)))
    (*
     im_m
     (*
      (cos re)
      (fma
       (* im_m im_m)
       (fma
        (* im_m im_m)
        (fma im_m (* im_m -0.0001984126984126984) -0.008333333333333333)
        -0.16666666666666666)
       -1.0))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double tmp;
	if (((exp(-im_m) - exp(im_m)) * (0.5 * cos(re))) <= -((double) INFINITY)) {
		tmp = 0.5 * (1.0 - exp(im_m));
	} else {
		tmp = im_m * (cos(re) * fma((im_m * im_m), fma((im_m * im_m), fma(im_m, (im_m * -0.0001984126984126984), -0.008333333333333333), -0.16666666666666666), -1.0));
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	tmp = 0.0
	if (Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re))) <= Float64(-Inf))
		tmp = Float64(0.5 * Float64(1.0 - exp(im_m)));
	else
		tmp = Float64(im_m * Float64(cos(re) * fma(Float64(im_m * im_m), fma(Float64(im_m * im_m), fma(im_m, Float64(im_m * -0.0001984126984126984), -0.008333333333333333), -0.16666666666666666), -1.0)));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := N[(im$95$s * If[LessEqual[N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], (-Infinity)], N[(0.5 * N[(1.0 - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(im$95$m * N[(N[Cos[re], $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(im$95$m * N[(im$95$m * -0.0001984126984126984), $MachinePrecision] + -0.008333333333333333), $MachinePrecision] + -0.16666666666666666), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

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

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 77.2

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

   5. Applied rewrites 77.2%

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

   6. Taylor expanded in im around 0

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

   8. Applied rewrites 77.3%

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

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

   1. Initial program 40.3%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

      \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)\right)} \]
   4. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)\right)} \]

      2. +-commutative N/A

         \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) + -1 \cdot \cos re\right)} \]

      3. +-commutative N/A

         \[\leadsto im \cdot \left({im}^{2} \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right) + \frac{-1}{6} \cdot \cos re\right)} + -1 \cdot \cos re\right) \]

      4. distribute-rgt-in N/A

         \[\leadsto im \cdot \left(\color{blue}{\left(\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) \cdot {im}^{2} + \left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2}\right)} + -1 \cdot \cos re\right) \]

      5. *-commutative N/A

         \[\leadsto im \cdot \left(\left(\color{blue}{{im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)} + \left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2}\right) + -1 \cdot \cos re\right) \]

      6. associate-+l+ N/A

         \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) + \left(\left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2} + -1 \cdot \cos re\right)\right)} \]

   5. Applied rewrites 95.6%

      \[\leadsto \color{blue}{im \cdot \left(\cos re \cdot \mathsf{fma}\left(\mathsf{fma}\left(im \cdot im, -0.0001984126984126984, -0.008333333333333333\right), im \cdot \left(im \cdot \left(im \cdot im\right)\right), \mathsf{fma}\left(im, im \cdot -0.16666666666666666, -1\right)\right)\right)} \]

   6. Taylor expanded in im around 0

      \[\leadsto im \cdot \left(\cos re \cdot \left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{5040} \cdot {im}^{2} - \frac{1}{120}\right) - \frac{1}{6}\right) - \color{blue}{1}\right)\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 95.6%

      \[\leadsto im \cdot \left(\cos re \cdot \mathsf{fma}\left(im \cdot im, \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right)}, -1\right)\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 91.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im}\right)\\ \mathbf{else}:\\ \;\;\;\;im \cdot \left(\cos re \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 96.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ im\_s \cdot \begin{array}{l} \mathbf{if}\;\left(e^{-im\_m} - e^{im\_m}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im\_m}\right)\\ \mathbf{else}:\\ \;\;\;\;\left(im\_m \cdot \cos re\right) \cdot \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m \cdot im\_m, -0.008333333333333333, -0.16666666666666666\right), -1\right)\\ \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (*
  im_s
  (if (<= (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re))) (- INFINITY))
    (* 0.5 (- 1.0 (exp im_m)))
    (*
     (* im_m (cos re))
     (fma
      (* im_m im_m)
      (fma (* im_m im_m) -0.008333333333333333 -0.16666666666666666)
      -1.0)))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double tmp;
	if (((exp(-im_m) - exp(im_m)) * (0.5 * cos(re))) <= -((double) INFINITY)) {
		tmp = 0.5 * (1.0 - exp(im_m));
	} else {
		tmp = (im_m * cos(re)) * fma((im_m * im_m), fma((im_m * im_m), -0.008333333333333333, -0.16666666666666666), -1.0);
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	tmp = 0.0
	if (Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re))) <= Float64(-Inf))
		tmp = Float64(0.5 * Float64(1.0 - exp(im_m)));
	else
		tmp = Float64(Float64(im_m * cos(re)) * fma(Float64(im_m * im_m), fma(Float64(im_m * im_m), -0.008333333333333333, -0.16666666666666666), -1.0));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := N[(im$95$s * If[LessEqual[N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], (-Infinity)], N[(0.5 * N[(1.0 - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(im$95$m * N[Cos[re], $MachinePrecision]), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * -0.008333333333333333 + -0.16666666666666666), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

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

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 77.2

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

   5. Applied rewrites 77.2%

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

   6. Taylor expanded in im around 0

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

   8. Applied rewrites 77.3%

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

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

   1. Initial program 40.3%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

      \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)\right)} \]
   4. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)\right)} \]

      2. +-commutative N/A

         \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) + -1 \cdot \cos re\right)} \]

      3. +-commutative N/A

         \[\leadsto im \cdot \left({im}^{2} \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right) + \frac{-1}{6} \cdot \cos re\right)} + -1 \cdot \cos re\right) \]

      4. distribute-rgt-in N/A

         \[\leadsto im \cdot \left(\color{blue}{\left(\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) \cdot {im}^{2} + \left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2}\right)} + -1 \cdot \cos re\right) \]

      5. *-commutative N/A

         \[\leadsto im \cdot \left(\left(\color{blue}{{im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)} + \left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2}\right) + -1 \cdot \cos re\right) \]

      6. associate-+l+ N/A

         \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \cos re + \frac{-1}{5040} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right) + \left(\left(\frac{-1}{6} \cdot \cos re\right) \cdot {im}^{2} + -1 \cdot \cos re\right)\right)} \]

   5. Applied rewrites 95.6%

      \[\leadsto \color{blue}{im \cdot \left(\cos re \cdot \mathsf{fma}\left(\mathsf{fma}\left(im \cdot im, -0.0001984126984126984, -0.008333333333333333\right), im \cdot \left(im \cdot \left(im \cdot im\right)\right), \mathsf{fma}\left(im, im \cdot -0.16666666666666666, -1\right)\right)\right)} \]

   6. Taylor expanded in im around 0

      \[\leadsto im \cdot \left(\cos re \cdot \left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{5040} \cdot {im}^{2} - \frac{1}{120}\right) - \frac{1}{6}\right) - \color{blue}{1}\right)\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 95.6%

      \[\leadsto im \cdot \left(\cos re \cdot \mathsf{fma}\left(im \cdot im, \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right)}, -1\right)\right) \]
   9. Step-by-step derivation

   10. Applied rewrites 95.6%

       \[\leadsto \left(im \cdot \cos re\right) \cdot \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)} \]

   11. Taylor expanded in im around 0

       \[\leadsto \left(im \cdot \cos re\right) \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \frac{-1}{120}, \frac{-1}{6}\right), -1\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 95.1%

       \[\leadsto \left(im \cdot \cos re\right) \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, -0.008333333333333333, -0.16666666666666666\right), -1\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 91.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im}\right)\\ \mathbf{else}:\\ \;\;\;\;\left(im \cdot \cos re\right) \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, -0.008333333333333333, -0.16666666666666666\right), -1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 96.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ im\_s \cdot \begin{array}{l} \mathbf{if}\;\left(e^{-im\_m} - e^{im\_m}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im\_m}\right)\\ \mathbf{else}:\\ \;\;\;\;im\_m \cdot \left(\cos re \cdot \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m \cdot im\_m, -0.008333333333333333, -0.16666666666666666\right), -1\right)\right)\\ \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (*
  im_s
  (if (<= (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re))) (- INFINITY))
    (* 0.5 (- 1.0 (exp im_m)))
    (*
     im_m
     (*
      (cos re)
      (fma
       (* im_m im_m)
       (fma (* im_m im_m) -0.008333333333333333 -0.16666666666666666)
       -1.0))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double tmp;
	if (((exp(-im_m) - exp(im_m)) * (0.5 * cos(re))) <= -((double) INFINITY)) {
		tmp = 0.5 * (1.0 - exp(im_m));
	} else {
		tmp = im_m * (cos(re) * fma((im_m * im_m), fma((im_m * im_m), -0.008333333333333333, -0.16666666666666666), -1.0));
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	tmp = 0.0
	if (Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re))) <= Float64(-Inf))
		tmp = Float64(0.5 * Float64(1.0 - exp(im_m)));
	else
		tmp = Float64(im_m * Float64(cos(re) * fma(Float64(im_m * im_m), fma(Float64(im_m * im_m), -0.008333333333333333, -0.16666666666666666), -1.0)));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := N[(im$95$s * If[LessEqual[N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], (-Infinity)], N[(0.5 * N[(1.0 - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(im$95$m * N[(N[Cos[re], $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * -0.008333333333333333 + -0.16666666666666666), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

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

   1. Initial program 100.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 77.2

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

   5. Applied rewrites 77.2%

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

   6. Taylor expanded in im around 0

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

   8. Applied rewrites 77.3%

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

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

   1. Initial program 40.3%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

      \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + \frac{-1}{120} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)} \]
   4. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \color{blue}{im \cdot \left(-1 \cdot \cos re + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + \frac{-1}{120} \cdot \left({im}^{2} \cdot \cos re\right)\right)\right)} \]

      2. +-commutative N/A

         \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re + \frac{-1}{120} \cdot \left({im}^{2} \cdot \cos re\right)\right) + -1 \cdot \cos re\right)} \]

      3. +-commutative N/A

         \[\leadsto im \cdot \left({im}^{2} \cdot \color{blue}{\left(\frac{-1}{120} \cdot \left({im}^{2} \cdot \cos re\right) + \frac{-1}{6} \cdot \cos re\right)} + -1 \cdot \cos re\right) \]

      4. distribute-lft-in N/A

         \[\leadsto im \cdot \left(\color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot \left({im}^{2} \cdot \cos re\right)\right) + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re\right)\right)} + -1 \cdot \cos re\right) \]

      5. associate-*r* N/A

         \[\leadsto im \cdot \left(\left({im}^{2} \cdot \color{blue}{\left(\left(\frac{-1}{120} \cdot {im}^{2}\right) \cdot \cos re\right)} + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re\right)\right) + -1 \cdot \cos re\right) \]

      6. associate-*r* N/A

         \[\leadsto im \cdot \left(\left(\color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot {im}^{2}\right)\right) \cdot \cos re} + {im}^{2} \cdot \left(\frac{-1}{6} \cdot \cos re\right)\right) + -1 \cdot \cos re\right) \]

      7. associate-*r* N/A

         \[\leadsto im \cdot \left(\left(\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot {im}^{2}\right)\right) \cdot \cos re + \color{blue}{\left({im}^{2} \cdot \frac{-1}{6}\right) \cdot \cos re}\right) + -1 \cdot \cos re\right) \]

      8. *-commutative N/A

         \[\leadsto im \cdot \left(\left(\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot {im}^{2}\right)\right) \cdot \cos re + \color{blue}{\left(\frac{-1}{6} \cdot {im}^{2}\right)} \cdot \cos re\right) + -1 \cdot \cos re\right) \]

      9. distribute-rgt-out N/A

         \[\leadsto im \cdot \left(\color{blue}{\cos re \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot {im}^{2}\right) + \frac{-1}{6} \cdot {im}^{2}\right)} + -1 \cdot \cos re\right) \]

      10. *-commutative N/A

          \[\leadsto im \cdot \left(\cos re \cdot \left({im}^{2} \cdot \left(\frac{-1}{120} \cdot {im}^{2}\right) + \frac{-1}{6} \cdot {im}^{2}\right) + \color{blue}{\cos re \cdot -1}\right) \]

      11. distribute-lft-out N/A

          \[\leadsto im \cdot \color{blue}{\left(\cos re \cdot \left(\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot {im}^{2}\right) + \frac{-1}{6} \cdot {im}^{2}\right) + -1\right)\right)} \]

   5. Applied rewrites 95.1%

      \[\leadsto \color{blue}{im \cdot \left(\cos re \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, -0.008333333333333333, -0.16666666666666666\right), -1\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 91.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq -\infty:\\ \;\;\;\;0.5 \cdot \left(1 - e^{im}\right)\\ \mathbf{else}:\\ \;\;\;\;im \cdot \left(\cos re \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, -0.008333333333333333, -0.16666666666666666\right), -1\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 71.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ im\_s \cdot \begin{array}{l} \mathbf{if}\;\left(e^{-im\_m} - e^{im\_m}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 0:\\ \;\;\;\;im\_m \cdot \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m, im\_m \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im\_m \cdot \left(\left(im\_m \cdot im\_m\right) \cdot -0.3333333333333333\right)\right)\\ \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (*
  im_s
  (if (<= (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re))) 0.0)
    (*
     im_m
     (fma
      (* im_m im_m)
      (fma
       (* im_m im_m)
       (fma im_m (* im_m -0.0001984126984126984) -0.008333333333333333)
       -0.16666666666666666)
      -1.0))
    (*
     (fma -0.25 (* re re) 0.5)
     (* im_m (* (* im_m im_m) -0.3333333333333333))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double tmp;
	if (((exp(-im_m) - exp(im_m)) * (0.5 * cos(re))) <= 0.0) {
		tmp = im_m * fma((im_m * im_m), fma((im_m * im_m), fma(im_m, (im_m * -0.0001984126984126984), -0.008333333333333333), -0.16666666666666666), -1.0);
	} else {
		tmp = fma(-0.25, (re * re), 0.5) * (im_m * ((im_m * im_m) * -0.3333333333333333));
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	tmp = 0.0
	if (Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re))) <= 0.0)
		tmp = Float64(im_m * fma(Float64(im_m * im_m), fma(Float64(im_m * im_m), fma(im_m, Float64(im_m * -0.0001984126984126984), -0.008333333333333333), -0.16666666666666666), -1.0));
	else
		tmp = Float64(fma(-0.25, Float64(re * re), 0.5) * Float64(im_m * Float64(Float64(im_m * im_m) * -0.3333333333333333)));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := N[(im$95$s * If[LessEqual[N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0], N[(im$95$m * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(im$95$m * N[(im$95$m * -0.0001984126984126984), $MachinePrecision] + -0.008333333333333333), $MachinePrecision] + -0.16666666666666666), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision], N[(N[(-0.25 * N[(re * re), $MachinePrecision] + 0.5), $MachinePrecision] * N[(im$95$m * N[(N[(im$95$m * im$95$m), $MachinePrecision] * -0.3333333333333333), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < 0.0

   1. Initial program 36.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 28.5

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

   5. Applied rewrites 28.5%

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

   6. Taylor expanded in im around 0

      \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left({im}^{2} \cdot \left(\frac{-1}{5040} \cdot {im}^{2} - \frac{1}{120}\right) - \frac{1}{6}\right) - 1\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 60.1%

      \[\leadsto im \cdot \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)} \]

   if 0.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 99.4%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. sub-neg N/A

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

      3. *-commutative N/A

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

      4. metadata-eval N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 79.7

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

   5. Applied rewrites 79.7%

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

   6. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\left(\frac{1}{2} + \frac{-1}{4} \cdot {re}^{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]
   7. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{-1}{4} \cdot {re}^{2} + \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      2. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{4}, {re}^{2}, \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, \color{blue}{re \cdot re}, \frac{1}{2}\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      4. lower-*.f64 72.5

         \[\leadsto \mathsf{fma}\left(-0.25, \color{blue}{re \cdot re}, 0.5\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   8. Applied rewrites 72.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   9. Taylor expanded in im around inf

      \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, re \cdot re, \frac{1}{2}\right) \cdot \left(im \cdot \left(\frac{-1}{3} \cdot \color{blue}{{im}^{2}}\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 71.2%

       \[\leadsto \mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot \color{blue}{-0.3333333333333333}\right)\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 63.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 0:\\ \;\;\;\;im \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.0001984126984126984, -0.008333333333333333\right), -0.16666666666666666\right), -1\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot -0.3333333333333333\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 69.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ im\_s \cdot \begin{array}{l} \mathbf{if}\;\left(e^{-im\_m} - e^{im\_m}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 0:\\ \;\;\;\;im\_m \cdot \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m, im\_m \cdot -0.008333333333333333, -0.16666666666666666\right), -1\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im\_m \cdot \left(\left(im\_m \cdot im\_m\right) \cdot -0.3333333333333333\right)\right)\\ \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (*
  im_s
  (if (<= (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re))) 0.0)
    (*
     im_m
     (fma
      (* im_m im_m)
      (fma im_m (* im_m -0.008333333333333333) -0.16666666666666666)
      -1.0))
    (*
     (fma -0.25 (* re re) 0.5)
     (* im_m (* (* im_m im_m) -0.3333333333333333))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double tmp;
	if (((exp(-im_m) - exp(im_m)) * (0.5 * cos(re))) <= 0.0) {
		tmp = im_m * fma((im_m * im_m), fma(im_m, (im_m * -0.008333333333333333), -0.16666666666666666), -1.0);
	} else {
		tmp = fma(-0.25, (re * re), 0.5) * (im_m * ((im_m * im_m) * -0.3333333333333333));
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	tmp = 0.0
	if (Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re))) <= 0.0)
		tmp = Float64(im_m * fma(Float64(im_m * im_m), fma(im_m, Float64(im_m * -0.008333333333333333), -0.16666666666666666), -1.0));
	else
		tmp = Float64(fma(-0.25, Float64(re * re), 0.5) * Float64(im_m * Float64(Float64(im_m * im_m) * -0.3333333333333333)));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := N[(im$95$s * If[LessEqual[N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0], N[(im$95$m * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(im$95$m * N[(im$95$m * -0.008333333333333333), $MachinePrecision] + -0.16666666666666666), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision], N[(N[(-0.25 * N[(re * re), $MachinePrecision] + 0.5), $MachinePrecision] * N[(im$95$m * N[(N[(im$95$m * im$95$m), $MachinePrecision] * -0.3333333333333333), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < 0.0

   1. Initial program 36.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 28.5

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

   5. Applied rewrites 28.5%

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

   6. Taylor expanded in im around 0

      \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot {im}^{2} - \frac{1}{6}\right) - 1\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 58.1%

      \[\leadsto im \cdot \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.008333333333333333, -0.16666666666666666\right), -1\right)} \]

   if 0.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 99.4%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. sub-neg N/A

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

      3. *-commutative N/A

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

      4. metadata-eval N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 79.7

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

   5. Applied rewrites 79.7%

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

   6. Taylor expanded in re around 0

      \[\leadsto \color{blue}{\left(\frac{1}{2} + \frac{-1}{4} \cdot {re}^{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]
   7. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{-1}{4} \cdot {re}^{2} + \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      2. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{4}, {re}^{2}, \frac{1}{2}\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, \color{blue}{re \cdot re}, \frac{1}{2}\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, \frac{-1}{3}, -2\right)\right) \]

      4. lower-*.f64 72.5

         \[\leadsto \mathsf{fma}\left(-0.25, \color{blue}{re \cdot re}, 0.5\right) \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   8. Applied rewrites 72.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right)} \cdot \left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right) \]

   9. Taylor expanded in im around inf

      \[\leadsto \mathsf{fma}\left(\frac{-1}{4}, re \cdot re, \frac{1}{2}\right) \cdot \left(im \cdot \left(\frac{-1}{3} \cdot \color{blue}{{im}^{2}}\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 71.2%

       \[\leadsto \mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot \color{blue}{-0.3333333333333333}\right)\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 61.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 0:\\ \;\;\;\;im \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.008333333333333333, -0.16666666666666666\right), -1\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.25, re \cdot re, 0.5\right) \cdot \left(im \cdot \left(\left(im \cdot im\right) \cdot -0.3333333333333333\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 67.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ im\_s \cdot \begin{array}{l} \mathbf{if}\;\left(e^{-im\_m} - e^{im\_m}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 0:\\ \;\;\;\;im\_m \cdot \mathsf{fma}\left(im\_m \cdot im\_m, \mathsf{fma}\left(im\_m, im\_m \cdot -0.008333333333333333, -0.16666666666666666\right), -1\right)\\ \mathbf{else}:\\ \;\;\;\;im\_m \cdot \left(0.5 \cdot \left(re \cdot re\right)\right)\\ \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (*
  im_s
  (if (<= (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re))) 0.0)
    (*
     im_m
     (fma
      (* im_m im_m)
      (fma im_m (* im_m -0.008333333333333333) -0.16666666666666666)
      -1.0))
    (* im_m (* 0.5 (* re re))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double tmp;
	if (((exp(-im_m) - exp(im_m)) * (0.5 * cos(re))) <= 0.0) {
		tmp = im_m * fma((im_m * im_m), fma(im_m, (im_m * -0.008333333333333333), -0.16666666666666666), -1.0);
	} else {
		tmp = im_m * (0.5 * (re * re));
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	tmp = 0.0
	if (Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re))) <= 0.0)
		tmp = Float64(im_m * fma(Float64(im_m * im_m), fma(im_m, Float64(im_m * -0.008333333333333333), -0.16666666666666666), -1.0));
	else
		tmp = Float64(im_m * Float64(0.5 * Float64(re * re)));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := N[(im$95$s * If[LessEqual[N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0], N[(im$95$m * N[(N[(im$95$m * im$95$m), $MachinePrecision] * N[(im$95$m * N[(im$95$m * -0.008333333333333333), $MachinePrecision] + -0.16666666666666666), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision], N[(im$95$m * N[(0.5 * N[(re * re), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < 0.0

   1. Initial program 36.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 28.5

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

   5. Applied rewrites 28.5%

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

   6. Taylor expanded in im around 0

      \[\leadsto im \cdot \color{blue}{\left({im}^{2} \cdot \left(\frac{-1}{120} \cdot {im}^{2} - \frac{1}{6}\right) - 1\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 58.1%

      \[\leadsto im \cdot \color{blue}{\mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.008333333333333333, -0.16666666666666666\right), -1\right)} \]

   if 0.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 99.4%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-cos.f64 7.7

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

   5. Applied rewrites 7.7%

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

   6. Taylor expanded in re around 0

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

   8. Applied rewrites 23.6%

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

   9. Taylor expanded in re around inf

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

   11. Applied rewrites 19.5%

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

4. Final simplification 47.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 0:\\ \;\;\;\;im \cdot \mathsf{fma}\left(im \cdot im, \mathsf{fma}\left(im, im \cdot -0.008333333333333333, -0.16666666666666666\right), -1\right)\\ \mathbf{else}:\\ \;\;\;\;im \cdot \left(0.5 \cdot \left(re \cdot re\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 62.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ im\_s \cdot \begin{array}{l} \mathbf{if}\;\left(e^{-im\_m} - e^{im\_m}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 0:\\ \;\;\;\;\mathsf{fma}\left(\left(im\_m \cdot im\_m\right) \cdot -0.3333333333333333, im\_m, im\_m \cdot -2\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;im\_m \cdot \left(0.5 \cdot \left(re \cdot re\right)\right)\\ \end{array} \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (*
  im_s
  (if (<= (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re))) 0.0)
    (* (fma (* (* im_m im_m) -0.3333333333333333) im_m (* im_m -2.0)) 0.5)
    (* im_m (* 0.5 (* re re))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double tmp;
	if (((exp(-im_m) - exp(im_m)) * (0.5 * cos(re))) <= 0.0) {
		tmp = fma(((im_m * im_m) * -0.3333333333333333), im_m, (im_m * -2.0)) * 0.5;
	} else {
		tmp = im_m * (0.5 * (re * re));
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	tmp = 0.0
	if (Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re))) <= 0.0)
		tmp = Float64(fma(Float64(Float64(im_m * im_m) * -0.3333333333333333), im_m, Float64(im_m * -2.0)) * 0.5);
	else
		tmp = Float64(im_m * Float64(0.5 * Float64(re * re)));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := N[(im$95$s * If[LessEqual[N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0], N[(N[(N[(N[(im$95$m * im$95$m), $MachinePrecision] * -0.3333333333333333), $MachinePrecision] * im$95$m + N[(im$95$m * -2.0), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], N[(im$95$m * N[(0.5 * N[(re * re), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < 0.0

   1. Initial program 36.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. sub-neg N/A

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

      3. *-commutative N/A

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

      4. metadata-eval N/A

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

      5. lower-fma.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 88.7

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

   5. Applied rewrites 88.7%

      \[\leadsto \left(0.5 \cdot \cos re\right) \cdot \color{blue}{\left(im \cdot \mathsf{fma}\left(im \cdot im, -0.3333333333333333, -2\right)\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 88.7%

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

   8. Taylor expanded in re around 0

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

   10. Applied rewrites 54.5%

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

   if 0.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 99.4%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-cos.f64 7.7

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

   5. Applied rewrites 7.7%

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

   6. Taylor expanded in re around 0

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

   8. Applied rewrites 23.6%

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

   9. Taylor expanded in re around inf

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

   11. Applied rewrites 19.5%

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

4. Final simplification 44.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 0:\\ \;\;\;\;\mathsf{fma}\left(\left(im \cdot im\right) \cdot -0.3333333333333333, im, im \cdot -2\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;im \cdot \left(0.5 \cdot \left(re \cdot re\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 15: 62.4% accurate, 0.9× speedup

Math

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

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (*
  im_s
  (if (<= (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re))) 0.0)
    (* im_m (fma -0.16666666666666666 (* im_m im_m) -1.0))
    (* im_m (* 0.5 (* re re))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double tmp;
	if (((exp(-im_m) - exp(im_m)) * (0.5 * cos(re))) <= 0.0) {
		tmp = im_m * fma(-0.16666666666666666, (im_m * im_m), -1.0);
	} else {
		tmp = im_m * (0.5 * (re * re));
	}
	return im_s * tmp;
}

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	tmp = 0.0
	if (Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re))) <= 0.0)
		tmp = Float64(im_m * fma(-0.16666666666666666, Float64(im_m * im_m), -1.0));
	else
		tmp = Float64(im_m * Float64(0.5 * Float64(re * re)));
	end
	return Float64(im_s * tmp)
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := N[(im$95$s * If[LessEqual[N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0], N[(im$95$m * N[(-0.16666666666666666 * N[(im$95$m * im$95$m), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision], N[(im$95$m * N[(0.5 * N[(re * re), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < 0.0

   1. Initial program 36.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 28.5

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

   5. Applied rewrites 28.5%

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

   6. Taylor expanded in im around 0

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

   8. Applied rewrites 54.5%

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

   if 0.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 99.4%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-cos.f64 7.7

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

   5. Applied rewrites 7.7%

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

   6. Taylor expanded in re around 0

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

   8. Applied rewrites 23.6%

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

   9. Taylor expanded in re around inf

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

   11. Applied rewrites 19.5%

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

4. Final simplification 44.8%

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

Alternative 16: 38.5% accurate, 0.9× speedup

Math

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

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m)
 :precision binary64
 (*
  im_s
  (if (<= (* (- (exp (- im_m)) (exp im_m)) (* 0.5 (cos re))) 0.0)
    (- im_m)
    (* im_m (* 0.5 (* re re))))))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	double tmp;
	if (((exp(-im_m) - exp(im_m)) * (0.5 * cos(re))) <= 0.0) {
		tmp = -im_m;
	} else {
		tmp = im_m * (0.5 * (re * re));
	}
	return im_s * tmp;
}

Fortran

im\_m = abs(im)
im\_s = copysign(1.0d0, im)
real(8) function code(im_s, re, im_m)
    real(8), intent (in) :: im_s
    real(8), intent (in) :: re
    real(8), intent (in) :: im_m
    real(8) :: tmp
    if (((exp(-im_m) - exp(im_m)) * (0.5d0 * cos(re))) <= 0.0d0) then
        tmp = -im_m
    else
        tmp = im_m * (0.5d0 * (re * re))
    end if
    code = im_s * tmp
end function

Java

im\_m = Math.abs(im);
im\_s = Math.copySign(1.0, im);
public static double code(double im_s, double re, double im_m) {
	double tmp;
	if (((Math.exp(-im_m) - Math.exp(im_m)) * (0.5 * Math.cos(re))) <= 0.0) {
		tmp = -im_m;
	} else {
		tmp = im_m * (0.5 * (re * re));
	}
	return im_s * tmp;
}

Python

im\_m = math.fabs(im)
im\_s = math.copysign(1.0, im)
def code(im_s, re, im_m):
	tmp = 0
	if ((math.exp(-im_m) - math.exp(im_m)) * (0.5 * math.cos(re))) <= 0.0:
		tmp = -im_m
	else:
		tmp = im_m * (0.5 * (re * re))
	return im_s * tmp

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	tmp = 0.0
	if (Float64(Float64(exp(Float64(-im_m)) - exp(im_m)) * Float64(0.5 * cos(re))) <= 0.0)
		tmp = Float64(-im_m);
	else
		tmp = Float64(im_m * Float64(0.5 * Float64(re * re)));
	end
	return Float64(im_s * tmp)
end

MATLAB

im\_m = abs(im);
im\_s = sign(im) * abs(1.0);
function tmp_2 = code(im_s, re, im_m)
	tmp = 0.0;
	if (((exp(-im_m) - exp(im_m)) * (0.5 * cos(re))) <= 0.0)
		tmp = -im_m;
	else
		tmp = im_m * (0.5 * (re * re));
	end
	tmp_2 = im_s * tmp;
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := N[(im$95$s * If[LessEqual[N[(N[(N[Exp[(-im$95$m)], $MachinePrecision] - N[Exp[im$95$m], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0], (-im$95$m), N[(im$95$m * N[(0.5 * N[(re * re), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < 0.0

   1. Initial program 36.0%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-exp.f64 N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-exp.f64 28.5

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

   5. Applied rewrites 28.5%

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

   6. Taylor expanded in im around 0

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

   8. Applied rewrites 40.0%

      \[\leadsto -im \]

   if 0.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (cos.f64 re)) (-.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 99.4%

      \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in im around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-cos.f64 7.7

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

   5. Applied rewrites 7.7%

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

   6. Taylor expanded in re around 0

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

   8. Applied rewrites 23.6%

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

   9. Taylor expanded in re around inf

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

   11. Applied rewrites 19.5%

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

4. Final simplification 34.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(e^{-im} - e^{im}\right) \cdot \left(0.5 \cdot \cos re\right) \leq 0:\\ \;\;\;\;-im\\ \mathbf{else}:\\ \;\;\;\;im \cdot \left(0.5 \cdot \left(re \cdot re\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 17: 29.0% accurate, 105.7× speedup

Math

\[\begin{array}{l} im\_m = \left|im\right| \\ im\_s = \mathsf{copysign}\left(1, im\right) \\ im\_s \cdot \left(-im\_m\right) \end{array} \]

FPCore

im\_m = (fabs.f64 im)
im\_s = (copysign.f64 #s(literal 1 binary64) im)
(FPCore (im_s re im_m) :precision binary64 (* im_s (- im_m)))

C

im\_m = fabs(im);
im\_s = copysign(1.0, im);
double code(double im_s, double re, double im_m) {
	return im_s * -im_m;
}

Fortran

im\_m = abs(im)
im\_s = copysign(1.0d0, im)
real(8) function code(im_s, re, im_m)
    real(8), intent (in) :: im_s
    real(8), intent (in) :: re
    real(8), intent (in) :: im_m
    code = im_s * -im_m
end function

Java

im\_m = Math.abs(im);
im\_s = Math.copySign(1.0, im);
public static double code(double im_s, double re, double im_m) {
	return im_s * -im_m;
}

Python

im\_m = math.fabs(im)
im\_s = math.copysign(1.0, im)
def code(im_s, re, im_m):
	return im_s * -im_m

Julia

im\_m = abs(im)
im\_s = copysign(1.0, im)
function code(im_s, re, im_m)
	return Float64(im_s * Float64(-im_m))
end

MATLAB

im\_m = abs(im);
im\_s = sign(im) * abs(1.0);
function tmp = code(im_s, re, im_m)
	tmp = im_s * -im_m;
end

Wolfram

im\_m = N[Abs[im], $MachinePrecision]
im\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[im$95$s_, re_, im$95$m_] := N[(im$95$s * (-im$95$m)), $MachinePrecision]

Derivation

1. Initial program 53.6%

   \[\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right) \]
2. Add Preprocessing

3. Taylor expanded in re around 0

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

   1. lower-*.f64 N/A

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

   2. lower--.f64 N/A

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

   3. lower-exp.f64 N/A

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

   4. lower-neg.f64 N/A

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

   5. lower-exp.f64 41.6

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

5. Applied rewrites 41.6%

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

6. Taylor expanded in im around 0

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

8. Applied rewrites 30.6%

   \[\leadsto -im \]
9. Add Preprocessing

Developer Target 1: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left|im\right| < 1:\\ \;\;\;\;-\cos re \cdot \left(\left(im + \left(\left(0.16666666666666666 \cdot im\right) \cdot im\right) \cdot im\right) + \left(\left(\left(\left(0.008333333333333333 \cdot im\right) \cdot im\right) \cdot im\right) \cdot im\right) \cdot im\right)\\ \mathbf{else}:\\ \;\;\;\;\left(0.5 \cdot \cos re\right) \cdot \left(e^{0 - im} - e^{im}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (< (fabs im) 1.0)
   (-
    (*
     (cos re)
     (+
      (+ im (* (* (* 0.16666666666666666 im) im) im))
      (* (* (* (* (* 0.008333333333333333 im) im) im) im) im))))
   (* (* 0.5 (cos re)) (- (exp (- 0.0 im)) (exp im)))))

C

double code(double re, double im) {
	double tmp;
	if (fabs(im) < 1.0) {
		tmp = -(cos(re) * ((im + (((0.16666666666666666 * im) * im) * im)) + (((((0.008333333333333333 * im) * im) * im) * im) * im)));
	} else {
		tmp = (0.5 * cos(re)) * (exp((0.0 - im)) - exp(im));
	}
	return tmp;
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if (abs(im) < 1.0d0) then
        tmp = -(cos(re) * ((im + (((0.16666666666666666d0 * im) * im) * im)) + (((((0.008333333333333333d0 * im) * im) * im) * im) * im)))
    else
        tmp = (0.5d0 * cos(re)) * (exp((0.0d0 - im)) - exp(im))
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if (Math.abs(im) < 1.0) {
		tmp = -(Math.cos(re) * ((im + (((0.16666666666666666 * im) * im) * im)) + (((((0.008333333333333333 * im) * im) * im) * im) * im)));
	} else {
		tmp = (0.5 * Math.cos(re)) * (Math.exp((0.0 - im)) - Math.exp(im));
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if math.fabs(im) < 1.0:
		tmp = -(math.cos(re) * ((im + (((0.16666666666666666 * im) * im) * im)) + (((((0.008333333333333333 * im) * im) * im) * im) * im)))
	else:
		tmp = (0.5 * math.cos(re)) * (math.exp((0.0 - im)) - math.exp(im))
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (abs(im) < 1.0)
		tmp = Float64(-Float64(cos(re) * Float64(Float64(im + Float64(Float64(Float64(0.16666666666666666 * im) * im) * im)) + Float64(Float64(Float64(Float64(Float64(0.008333333333333333 * im) * im) * im) * im) * im))));
	else
		tmp = Float64(Float64(0.5 * cos(re)) * Float64(exp(Float64(0.0 - im)) - exp(im)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if (abs(im) < 1.0)
		tmp = -(cos(re) * ((im + (((0.16666666666666666 * im) * im) * im)) + (((((0.008333333333333333 * im) * im) * im) * im) * im)));
	else
		tmp = (0.5 * cos(re)) * (exp((0.0 - im)) - exp(im));
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[Less[N[Abs[im], $MachinePrecision], 1.0], (-N[(N[Cos[re], $MachinePrecision] * N[(N[(im + N[(N[(N[(0.16666666666666666 * im), $MachinePrecision] * im), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(N[(N[(0.008333333333333333 * im), $MachinePrecision] * im), $MachinePrecision] * im), $MachinePrecision] * im), $MachinePrecision] * im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), N[(N[(0.5 * N[Cos[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[N[(0.0 - im), $MachinePrecision]], $MachinePrecision] - N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Reproduce

herbie shell --seed 2024221 
(FPCore (re im)
  :name "math.sin on complex, imaginary part"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< (fabs im) 1) (- (* (cos re) (+ im (* 1/6 im im im) (* 1/120 im im im im im)))) (* (* 1/2 (cos re)) (- (exp (- 0 im)) (exp im)))))

  (* (* 0.5 (cos re)) (- (exp (- 0.0 im)) (exp im))))