math.cos on complex, imaginary part

Percentage Accurate: 65.9% → 99.9%

Time: 4.2s

Alternatives: 8

Speedup: 1.3×

• 50.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 65.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.3× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 65.9%

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

   1. lift-*.f64 N/A

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

   2. *-commutative N/A

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

   3. lift-*.f64 N/A

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

   4. associate-*r* N/A

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

   5. lift--.f64 N/A

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

   6. sub-negate-rev N/A

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

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

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

   8. metadata-eval N/A

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

   9. mult-flip N/A

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

   10. lift-exp.f64 N/A

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

   11. lift-exp.f64 N/A

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

   12. lift-neg.f64 N/A

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

   13. sinh-def N/A

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

   14. sinh-neg N/A

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

   15. lift-neg.f64 N/A

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

   16. lower-*.f64 N/A

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

   17. lower-sinh.f64 99.9%

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

3. Applied rewrites 99.9%

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

Alternative 2: 98.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} t_0 := -\left|im\right|\\ t_1 := e^{\left|im\right|}\\ t_2 := \sin \left(\left|re\right|\right)\\ t_3 := \left(0.5 \cdot t\_2\right) \cdot \left(e^{t\_0} - t\_1\right)\\ \mathsf{copysign}\left(1, re\right) \cdot \left(\mathsf{copysign}\left(1, im\right) \cdot \begin{array}{l} \mathbf{if}\;t\_3 \leq -\infty:\\ \;\;\;\;\left(0.5 \cdot \left|re\right|\right) \cdot \left(1 - t\_1\right)\\ \mathbf{elif}\;t\_3 \leq 4 \cdot 10^{-5}:\\ \;\;\;\;t\_2 \cdot t\_0\\ \mathbf{else}:\\ \;\;\;\;\sinh t\_0 \cdot \mathsf{fma}\left(\left(\left|re\right| \cdot \left|re\right|\right) \cdot -0.16666666666666666, \left|re\right|, \left|re\right|\right)\\ \end{array}\right) \end{array} \]

FPCore

(FPCore (re im)
  :precision binary64
  (let* ((t_0 (- (fabs im)))
       (t_1 (exp (fabs im)))
       (t_2 (sin (fabs re)))
       (t_3 (* (* 0.5 t_2) (- (exp t_0) t_1))))
  (*
   (copysign 1.0 re)
   (*
    (copysign 1.0 im)
    (if (<= t_3 (- INFINITY))
      (* (* 0.5 (fabs re)) (- 1.0 t_1))
      (if (<= t_3 4e-5)
        (* t_2 t_0)
        (*
         (sinh t_0)
         (fma
          (* (* (fabs re) (fabs re)) -0.16666666666666666)
          (fabs re)
          (fabs re)))))))))

C

double code(double re, double im) {
	double t_0 = -fabs(im);
	double t_1 = exp(fabs(im));
	double t_2 = sin(fabs(re));
	double t_3 = (0.5 * t_2) * (exp(t_0) - t_1);
	double tmp;
	if (t_3 <= -((double) INFINITY)) {
		tmp = (0.5 * fabs(re)) * (1.0 - t_1);
	} else if (t_3 <= 4e-5) {
		tmp = t_2 * t_0;
	} else {
		tmp = sinh(t_0) * fma(((fabs(re) * fabs(re)) * -0.16666666666666666), fabs(re), fabs(re));
	}
	return copysign(1.0, re) * (copysign(1.0, im) * tmp);
}

Julia

function code(re, im)
	t_0 = Float64(-abs(im))
	t_1 = exp(abs(im))
	t_2 = sin(abs(re))
	t_3 = Float64(Float64(0.5 * t_2) * Float64(exp(t_0) - t_1))
	tmp = 0.0
	if (t_3 <= Float64(-Inf))
		tmp = Float64(Float64(0.5 * abs(re)) * Float64(1.0 - t_1));
	elseif (t_3 <= 4e-5)
		tmp = Float64(t_2 * t_0);
	else
		tmp = Float64(sinh(t_0) * fma(Float64(Float64(abs(re) * abs(re)) * -0.16666666666666666), abs(re), abs(re)));
	end
	return Float64(copysign(1.0, re) * Float64(copysign(1.0, im) * tmp))
end

Wolfram

code[re_, im_] := Block[{t$95$0 = (-N[Abs[im], $MachinePrecision])}, Block[{t$95$1 = N[Exp[N[Abs[im], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[Abs[re], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[(N[(0.5 * t$95$2), $MachinePrecision] * N[(N[Exp[t$95$0], $MachinePrecision] - t$95$1), $MachinePrecision]), $MachinePrecision]}, N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[t$95$3, (-Infinity)], N[(N[(0.5 * N[Abs[re], $MachinePrecision]), $MachinePrecision] * N[(1.0 - t$95$1), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, 4e-5], N[(t$95$2 * t$95$0), $MachinePrecision], N[(N[Sinh[t$95$0], $MachinePrecision] * N[(N[(N[(N[Abs[re], $MachinePrecision] * N[Abs[re], $MachinePrecision]), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * N[Abs[re], $MachinePrecision] + N[Abs[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

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

   1. Initial program 65.9%

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

   2. Taylor expanded in re around 0

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

      1. lower-*.f64 52.6%

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

   4. Applied rewrites 52.6%

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

   5. Taylor expanded in im around 0

      \[\leadsto \left(0.5 \cdot re\right) \cdot \left(\color{blue}{1} - e^{im}\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 34.2%

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

   if -inf.0 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < 4.0000000000000003e-5

   1. Initial program 65.9%

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

   2. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-sin.f64 51.3%

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

   4. Applied rewrites 51.3%

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

      1. lift-*.f64 N/A

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

      2. mul-1-neg N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. distribute-rgt-neg-in N/A

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

      6. lift-neg.f64 N/A

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

      7. lower-*.f64 51.3%

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

   6. Applied rewrites 51.3%

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

   if 4.0000000000000003e-5 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im)))

   1. Initial program 65.9%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. lift--.f64 N/A

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

      6. sub-negate-rev N/A

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

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

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

      8. metadata-eval N/A

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

      9. mult-flip N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-exp.f64 N/A

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

      12. lift-neg.f64 N/A

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

      13. sinh-def N/A

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

      14. sinh-neg N/A

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

      15. lift-neg.f64 N/A

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

      16. lower-*.f64 N/A

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

      17. lower-sinh.f64 99.9%

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 62.7%

         \[\leadsto \sinh \left(-im\right) \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{\color{blue}{2}}\right)\right) \]

   6. Applied rewrites 62.7%

      \[\leadsto \sinh \left(-im\right) \cdot \color{blue}{\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)} \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. distribute-rgt-in N/A

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

      5. *-lft-identity N/A

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

      6. lower-fma.f64 62.7%

         \[\leadsto \sinh \left(-im\right) \cdot \mathsf{fma}\left(-0.16666666666666666 \cdot {re}^{2}, \color{blue}{re}, re\right) \]

      7. lift-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 62.7%

         \[\leadsto \sinh \left(-im\right) \cdot \mathsf{fma}\left({re}^{2} \cdot -0.16666666666666666, re, re\right) \]

      10. lift-pow.f64 N/A

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

      11. unpow2 N/A

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

      12. lower-*.f64 62.7%

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

   8. Applied rewrites 62.7%

      \[\leadsto \sinh \left(-im\right) \cdot \mathsf{fma}\left(\left(re \cdot re\right) \cdot -0.16666666666666666, \color{blue}{re}, re\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 75.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} t_0 := \sinh \left(-im\right)\\ \mathsf{copysign}\left(1, re\right) \cdot \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin \left(\left|re\right|\right) \leq 0.0002:\\ \;\;\;\;t\_0 \cdot \mathsf{fma}\left(\left(\left|re\right| \cdot \left|re\right|\right) \cdot -0.16666666666666666, \left|re\right|, \left|re\right|\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0 \cdot \left(\left|re\right| \cdot 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
  :precision binary64
  (let* ((t_0 (sinh (- im))))
  (*
   (copysign 1.0 re)
   (if (<= (* 0.5 (sin (fabs re))) 0.0002)
     (*
      t_0
      (fma
       (* (* (fabs re) (fabs re)) -0.16666666666666666)
       (fabs re)
       (fabs re)))
     (* t_0 (* (fabs re) 1.0))))))

C

double code(double re, double im) {
	double t_0 = sinh(-im);
	double tmp;
	if ((0.5 * sin(fabs(re))) <= 0.0002) {
		tmp = t_0 * fma(((fabs(re) * fabs(re)) * -0.16666666666666666), fabs(re), fabs(re));
	} else {
		tmp = t_0 * (fabs(re) * 1.0);
	}
	return copysign(1.0, re) * tmp;
}

Julia

function code(re, im)
	t_0 = sinh(Float64(-im))
	tmp = 0.0
	if (Float64(0.5 * sin(abs(re))) <= 0.0002)
		tmp = Float64(t_0 * fma(Float64(Float64(abs(re) * abs(re)) * -0.16666666666666666), abs(re), abs(re)));
	else
		tmp = Float64(t_0 * Float64(abs(re) * 1.0));
	end
	return Float64(copysign(1.0, re) * tmp)
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[Sinh[(-im)], $MachinePrecision]}, N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[N[(0.5 * N[Sin[N[Abs[re], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], 0.0002], N[(t$95$0 * N[(N[(N[(N[Abs[re], $MachinePrecision] * N[Abs[re], $MachinePrecision]), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * N[Abs[re], $MachinePrecision] + N[Abs[re], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t$95$0 * N[(N[Abs[re], $MachinePrecision] * 1.0), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < 2.0000000000000001e-4

   1. Initial program 65.9%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. lift--.f64 N/A

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

      6. sub-negate-rev N/A

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

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

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

      8. metadata-eval N/A

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

      9. mult-flip N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-exp.f64 N/A

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

      12. lift-neg.f64 N/A

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

      13. sinh-def N/A

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

      14. sinh-neg N/A

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

      15. lift-neg.f64 N/A

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

      16. lower-*.f64 N/A

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

      17. lower-sinh.f64 99.9%

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 62.7%

         \[\leadsto \sinh \left(-im\right) \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{\color{blue}{2}}\right)\right) \]

   6. Applied rewrites 62.7%

      \[\leadsto \sinh \left(-im\right) \cdot \color{blue}{\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)} \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. distribute-rgt-in N/A

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

      5. *-lft-identity N/A

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

      6. lower-fma.f64 62.7%

         \[\leadsto \sinh \left(-im\right) \cdot \mathsf{fma}\left(-0.16666666666666666 \cdot {re}^{2}, \color{blue}{re}, re\right) \]

      7. lift-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 62.7%

         \[\leadsto \sinh \left(-im\right) \cdot \mathsf{fma}\left({re}^{2} \cdot -0.16666666666666666, re, re\right) \]

      10. lift-pow.f64 N/A

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

      11. unpow2 N/A

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

      12. lower-*.f64 62.7%

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

   8. Applied rewrites 62.7%

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

   if 2.0000000000000001e-4 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.9%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. lift--.f64 N/A

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

      6. sub-negate-rev N/A

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

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

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

      8. metadata-eval N/A

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

      9. mult-flip N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-exp.f64 N/A

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

      12. lift-neg.f64 N/A

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

      13. sinh-def N/A

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

      14. sinh-neg N/A

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

      15. lift-neg.f64 N/A

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

      16. lower-*.f64 N/A

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

      17. lower-sinh.f64 99.9%

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 62.7%

         \[\leadsto \sinh \left(-im\right) \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{\color{blue}{2}}\right)\right) \]

   6. Applied rewrites 62.7%

      \[\leadsto \sinh \left(-im\right) \cdot \color{blue}{\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)} \]

   7. Taylor expanded in re around 0

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

   9. Applied rewrites 63.4%

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

Alternative 4: 73.9% accurate, 0.8× speedup

Math

\[\mathsf{copysign}\left(1, re\right) \cdot \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin \left(\left|re\right|\right) \leq -0.004:\\ \;\;\;\;\left|re\right| \cdot \left(0.16666666666666666 \cdot \left(im \cdot {\left(\left|re\right|\right)}^{2}\right) - im\right)\\ \mathbf{else}:\\ \;\;\;\;\sinh \left(-im\right) \cdot \left(\left|re\right| \cdot 1\right)\\ \end{array} \]

FPCore

(FPCore (re im)
  :precision binary64
  (*
 (copysign 1.0 re)
 (if (<= (* 0.5 (sin (fabs re))) -0.004)
   (*
    (fabs re)
    (- (* 0.16666666666666666 (* im (pow (fabs re) 2.0))) im))
   (* (sinh (- im)) (* (fabs re) 1.0)))))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(fabs(re))) <= -0.004) {
		tmp = fabs(re) * ((0.16666666666666666 * (im * pow(fabs(re), 2.0))) - im);
	} else {
		tmp = sinh(-im) * (fabs(re) * 1.0);
	}
	return copysign(1.0, re) * tmp;
}

Java

public static double code(double re, double im) {
	double tmp;
	if ((0.5 * Math.sin(Math.abs(re))) <= -0.004) {
		tmp = Math.abs(re) * ((0.16666666666666666 * (im * Math.pow(Math.abs(re), 2.0))) - im);
	} else {
		tmp = Math.sinh(-im) * (Math.abs(re) * 1.0);
	}
	return Math.copySign(1.0, re) * tmp;
}

Python

def code(re, im):
	tmp = 0
	if (0.5 * math.sin(math.fabs(re))) <= -0.004:
		tmp = math.fabs(re) * ((0.16666666666666666 * (im * math.pow(math.fabs(re), 2.0))) - im)
	else:
		tmp = math.sinh(-im) * (math.fabs(re) * 1.0)
	return math.copysign(1.0, re) * tmp

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(abs(re))) <= -0.004)
		tmp = Float64(abs(re) * Float64(Float64(0.16666666666666666 * Float64(im * (abs(re) ^ 2.0))) - im));
	else
		tmp = Float64(sinh(Float64(-im)) * Float64(abs(re) * 1.0));
	end
	return Float64(copysign(1.0, re) * tmp)
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if ((0.5 * sin(abs(re))) <= -0.004)
		tmp = abs(re) * ((0.16666666666666666 * (im * (abs(re) ^ 2.0))) - im);
	else
		tmp = sinh(-im) * (abs(re) * 1.0);
	end
	tmp_2 = (sign(re) * abs(1.0)) * tmp;
end

Wolfram

code[re_, im_] := N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[N[(0.5 * N[Sin[N[Abs[re], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], -0.004], N[(N[Abs[re], $MachinePrecision] * N[(N[(0.16666666666666666 * N[(im * N[Power[N[Abs[re], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - im), $MachinePrecision]), $MachinePrecision], N[(N[Sinh[(-im)], $MachinePrecision] * N[(N[Abs[re], $MachinePrecision] * 1.0), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0040000000000000001

   1. Initial program 65.9%

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

   2. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-sin.f64 51.3%

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

   4. Applied rewrites 51.3%

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

   5. Taylor expanded in re around 0

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

      1. lower-*.f64 32.9%

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

   7. Applied rewrites 32.9%

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

      1. lift-*.f64 N/A

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

      2. mul-1-neg N/A

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

      3. lower-neg.f64 32.9%

         \[\leadsto -im \cdot re \]

      4. lift-*.f64 N/A

         \[\leadsto -im \cdot re \]

      5. *-commutative N/A

         \[\leadsto -re \cdot im \]

      6. lower-*.f64 32.9%

         \[\leadsto -re \cdot im \]

   9. Applied rewrites 32.9%

      \[\leadsto -re \cdot im \]

   10. Taylor expanded in re around 0

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

       1. lower-*.f64 N/A

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

       2. lower--.f64 N/A

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

       3. lower-*.f64 N/A

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

       4. lower-*.f64 N/A

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

       5. lower-pow.f64 36.5%

          \[\leadsto re \cdot \left(0.16666666666666666 \cdot \left(im \cdot {re}^{2}\right) - im\right) \]

   12. Applied rewrites 36.5%

       \[\leadsto re \cdot \color{blue}{\left(0.16666666666666666 \cdot \left(im \cdot {re}^{2}\right) - im\right)} \]

   if -0.0040000000000000001 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.9%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. lift--.f64 N/A

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

      6. sub-negate-rev N/A

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

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

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

      8. metadata-eval N/A

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

      9. mult-flip N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-exp.f64 N/A

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

      12. lift-neg.f64 N/A

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

      13. sinh-def N/A

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

      14. sinh-neg N/A

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

      15. lift-neg.f64 N/A

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

      16. lower-*.f64 N/A

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

      17. lower-sinh.f64 99.9%

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 62.7%

         \[\leadsto \sinh \left(-im\right) \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{\color{blue}{2}}\right)\right) \]

   6. Applied rewrites 62.7%

      \[\leadsto \sinh \left(-im\right) \cdot \color{blue}{\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)} \]

   7. Taylor expanded in re around 0

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

   9. Applied rewrites 63.4%

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

Alternative 5: 73.3% accurate, 0.8× speedup

Math

\[\mathsf{copysign}\left(1, re\right) \cdot \left(\mathsf{copysign}\left(1, im\right) \cdot \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin \left(\left|re\right|\right) \leq -0.004:\\ \;\;\;\;\left(0.5 \cdot \left|re\right|\right) \cdot \left(\left(1 + \left|im\right| \cdot \left(0.5 \cdot \left|im\right| - 1\right)\right) - \left(1 + \left|im\right|\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\sinh \left(-\left|im\right|\right) \cdot \left(\left|re\right| \cdot 1\right)\\ \end{array}\right) \]

FPCore

(FPCore (re im)
  :precision binary64
  (*
 (copysign 1.0 re)
 (*
  (copysign 1.0 im)
  (if (<= (* 0.5 (sin (fabs re))) -0.004)
    (*
     (* 0.5 (fabs re))
     (-
      (+ 1.0 (* (fabs im) (- (* 0.5 (fabs im)) 1.0)))
      (+ 1.0 (fabs im))))
    (* (sinh (- (fabs im))) (* (fabs re) 1.0))))))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(fabs(re))) <= -0.004) {
		tmp = (0.5 * fabs(re)) * ((1.0 + (fabs(im) * ((0.5 * fabs(im)) - 1.0))) - (1.0 + fabs(im)));
	} else {
		tmp = sinh(-fabs(im)) * (fabs(re) * 1.0);
	}
	return copysign(1.0, re) * (copysign(1.0, im) * tmp);
}

Java

public static double code(double re, double im) {
	double tmp;
	if ((0.5 * Math.sin(Math.abs(re))) <= -0.004) {
		tmp = (0.5 * Math.abs(re)) * ((1.0 + (Math.abs(im) * ((0.5 * Math.abs(im)) - 1.0))) - (1.0 + Math.abs(im)));
	} else {
		tmp = Math.sinh(-Math.abs(im)) * (Math.abs(re) * 1.0);
	}
	return Math.copySign(1.0, re) * (Math.copySign(1.0, im) * tmp);
}

Python

def code(re, im):
	tmp = 0
	if (0.5 * math.sin(math.fabs(re))) <= -0.004:
		tmp = (0.5 * math.fabs(re)) * ((1.0 + (math.fabs(im) * ((0.5 * math.fabs(im)) - 1.0))) - (1.0 + math.fabs(im)))
	else:
		tmp = math.sinh(-math.fabs(im)) * (math.fabs(re) * 1.0)
	return math.copysign(1.0, re) * (math.copysign(1.0, im) * tmp)

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(abs(re))) <= -0.004)
		tmp = Float64(Float64(0.5 * abs(re)) * Float64(Float64(1.0 + Float64(abs(im) * Float64(Float64(0.5 * abs(im)) - 1.0))) - Float64(1.0 + abs(im))));
	else
		tmp = Float64(sinh(Float64(-abs(im))) * Float64(abs(re) * 1.0));
	end
	return Float64(copysign(1.0, re) * Float64(copysign(1.0, im) * tmp))
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if ((0.5 * sin(abs(re))) <= -0.004)
		tmp = (0.5 * abs(re)) * ((1.0 + (abs(im) * ((0.5 * abs(im)) - 1.0))) - (1.0 + abs(im)));
	else
		tmp = sinh(-abs(im)) * (abs(re) * 1.0);
	end
	tmp_2 = (sign(re) * abs(1.0)) * ((sign(im) * abs(1.0)) * tmp);
end

Wolfram

code[re_, im_] := N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[N[(0.5 * N[Sin[N[Abs[re], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], -0.004], N[(N[(0.5 * N[Abs[re], $MachinePrecision]), $MachinePrecision] * N[(N[(1.0 + N[(N[Abs[im], $MachinePrecision] * N[(N[(0.5 * N[Abs[im], $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(1.0 + N[Abs[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Sinh[(-N[Abs[im], $MachinePrecision])], $MachinePrecision] * N[(N[Abs[re], $MachinePrecision] * 1.0), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0040000000000000001

   1. Initial program 65.9%

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

   2. Taylor expanded in re around 0

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

      1. lower-*.f64 52.6%

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

   4. Applied rewrites 52.6%

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

   5. Taylor expanded in im around 0

      \[\leadsto \left(0.5 \cdot re\right) \cdot \left(e^{-im} - \color{blue}{\left(1 + im\right)}\right) \]
   6. Step-by-step derivation

      1. lower-+.f64 36.9%

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

   7. Applied rewrites 36.9%

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

   8. Taylor expanded in im around 0

      \[\leadsto \left(0.5 \cdot re\right) \cdot \left(\color{blue}{\left(1 + im \cdot \left(\frac{1}{2} \cdot im - 1\right)\right)} - \left(1 + im\right)\right) \]
   9. Step-by-step derivation

      1. lower-+.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-*.f64 30.4%

         \[\leadsto \left(0.5 \cdot re\right) \cdot \left(\left(1 + im \cdot \left(0.5 \cdot im - 1\right)\right) - \left(1 + im\right)\right) \]

   10. Applied rewrites 30.4%

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

   if -0.0040000000000000001 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 65.9%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. lift--.f64 N/A

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

      6. sub-negate-rev N/A

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

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

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

      8. metadata-eval N/A

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

      9. mult-flip N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-exp.f64 N/A

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

      12. lift-neg.f64 N/A

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

      13. sinh-def N/A

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

      14. sinh-neg N/A

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

      15. lift-neg.f64 N/A

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

      16. lower-*.f64 N/A

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

      17. lower-sinh.f64 99.9%

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 62.7%

         \[\leadsto \sinh \left(-im\right) \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{\color{blue}{2}}\right)\right) \]

   6. Applied rewrites 62.7%

      \[\leadsto \sinh \left(-im\right) \cdot \color{blue}{\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)} \]

   7. Taylor expanded in re around 0

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

   9. Applied rewrites 63.4%

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

Alternative 6: 63.4% accurate, 3.0× speedup

Math

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

FPCore

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

C

double code(double re, double im) {
	return sinh(-im) * (re * 1.0);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = sinh(-im) * (re * 1.0d0)
end function

Java

public static double code(double re, double im) {
	return Math.sinh(-im) * (re * 1.0);
}

Python

def code(re, im):
	return math.sinh(-im) * (re * 1.0)

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 65.9%

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

   1. lift-*.f64 N/A

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

   2. *-commutative N/A

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

   3. lift-*.f64 N/A

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

   4. associate-*r* N/A

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

   5. lift--.f64 N/A

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

   6. sub-negate-rev N/A

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

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

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

   8. metadata-eval N/A

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

   9. mult-flip N/A

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

   10. lift-exp.f64 N/A

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

   11. lift-exp.f64 N/A

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

   12. lift-neg.f64 N/A

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

   13. sinh-def N/A

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

   14. sinh-neg N/A

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

   15. lift-neg.f64 N/A

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

   16. lower-*.f64 N/A

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

   17. lower-sinh.f64 99.9%

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

3. Applied rewrites 99.9%

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

4. Taylor expanded in re around 0

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

   1. lower-*.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-pow.f64 62.7%

      \[\leadsto \sinh \left(-im\right) \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{\color{blue}{2}}\right)\right) \]

6. Applied rewrites 62.7%

   \[\leadsto \sinh \left(-im\right) \cdot \color{blue}{\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)} \]

7. Taylor expanded in re around 0

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

9. Applied rewrites 63.4%

   \[\leadsto \sinh \left(-im\right) \cdot \left(re \cdot 1\right) \]
10. Add Preprocessing

Alternative 7: 63.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} t_0 := e^{\left|im\right|}\\ \mathsf{copysign}\left(1, re\right) \cdot \left(\mathsf{copysign}\left(1, im\right) \cdot \begin{array}{l} \mathbf{if}\;\left(0.5 \cdot \sin \left(\left|re\right|\right)\right) \cdot \left(e^{-\left|im\right|} - t\_0\right) \leq -2 \cdot 10^{-13}:\\ \;\;\;\;\left(0.5 \cdot \left|re\right|\right) \cdot \left(1 - t\_0\right)\\ \mathbf{else}:\\ \;\;\;\;-\left|re\right| \cdot \left|im\right|\\ \end{array}\right) \end{array} \]

FPCore

(FPCore (re im)
  :precision binary64
  (let* ((t_0 (exp (fabs im))))
  (*
   (copysign 1.0 re)
   (*
    (copysign 1.0 im)
    (if (<=
         (* (* 0.5 (sin (fabs re))) (- (exp (- (fabs im))) t_0))
         -2e-13)
      (* (* 0.5 (fabs re)) (- 1.0 t_0))
      (- (* (fabs re) (fabs im))))))))

C

double code(double re, double im) {
	double t_0 = exp(fabs(im));
	double tmp;
	if (((0.5 * sin(fabs(re))) * (exp(-fabs(im)) - t_0)) <= -2e-13) {
		tmp = (0.5 * fabs(re)) * (1.0 - t_0);
	} else {
		tmp = -(fabs(re) * fabs(im));
	}
	return copysign(1.0, re) * (copysign(1.0, im) * tmp);
}

Java

public static double code(double re, double im) {
	double t_0 = Math.exp(Math.abs(im));
	double tmp;
	if (((0.5 * Math.sin(Math.abs(re))) * (Math.exp(-Math.abs(im)) - t_0)) <= -2e-13) {
		tmp = (0.5 * Math.abs(re)) * (1.0 - t_0);
	} else {
		tmp = -(Math.abs(re) * Math.abs(im));
	}
	return Math.copySign(1.0, re) * (Math.copySign(1.0, im) * tmp);
}

Python

def code(re, im):
	t_0 = math.exp(math.fabs(im))
	tmp = 0
	if ((0.5 * math.sin(math.fabs(re))) * (math.exp(-math.fabs(im)) - t_0)) <= -2e-13:
		tmp = (0.5 * math.fabs(re)) * (1.0 - t_0)
	else:
		tmp = -(math.fabs(re) * math.fabs(im))
	return math.copysign(1.0, re) * (math.copysign(1.0, im) * tmp)

Julia

function code(re, im)
	t_0 = exp(abs(im))
	tmp = 0.0
	if (Float64(Float64(0.5 * sin(abs(re))) * Float64(exp(Float64(-abs(im))) - t_0)) <= -2e-13)
		tmp = Float64(Float64(0.5 * abs(re)) * Float64(1.0 - t_0));
	else
		tmp = Float64(-Float64(abs(re) * abs(im)));
	end
	return Float64(copysign(1.0, re) * Float64(copysign(1.0, im) * tmp))
end

MATLAB

function tmp_2 = code(re, im)
	t_0 = exp(abs(im));
	tmp = 0.0;
	if (((0.5 * sin(abs(re))) * (exp(-abs(im)) - t_0)) <= -2e-13)
		tmp = (0.5 * abs(re)) * (1.0 - t_0);
	else
		tmp = -(abs(re) * abs(im));
	end
	tmp_2 = (sign(re) * abs(1.0)) * ((sign(im) * abs(1.0)) * tmp);
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[Exp[N[Abs[im], $MachinePrecision]], $MachinePrecision]}, N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[im]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[N[(N[(0.5 * N[Sin[N[Abs[re], $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[(-N[Abs[im], $MachinePrecision])], $MachinePrecision] - t$95$0), $MachinePrecision]), $MachinePrecision], -2e-13], N[(N[(0.5 * N[Abs[re], $MachinePrecision]), $MachinePrecision] * N[(1.0 - t$95$0), $MachinePrecision]), $MachinePrecision], (-N[(N[Abs[re], $MachinePrecision] * N[Abs[im], $MachinePrecision]), $MachinePrecision])]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im))) < -2.0000000000000001e-13

   1. Initial program 65.9%

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

   2. Taylor expanded in re around 0

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

      1. lower-*.f64 52.6%

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

   4. Applied rewrites 52.6%

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

   5. Taylor expanded in im around 0

      \[\leadsto \left(0.5 \cdot re\right) \cdot \left(\color{blue}{1} - e^{im}\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 34.2%

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

   if -2.0000000000000001e-13 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (-.f64 (exp.f64 (neg.f64 im)) (exp.f64 im)))

   1. Initial program 65.9%

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

   2. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-sin.f64 51.3%

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

   4. Applied rewrites 51.3%

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

   5. Taylor expanded in re around 0

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

      1. lower-*.f64 32.9%

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

   7. Applied rewrites 32.9%

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

      1. lift-*.f64 N/A

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

      2. mul-1-neg N/A

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

      3. lower-neg.f64 32.9%

         \[\leadsto -im \cdot re \]

      4. lift-*.f64 N/A

         \[\leadsto -im \cdot re \]

      5. *-commutative N/A

         \[\leadsto -re \cdot im \]

      6. lower-*.f64 32.9%

         \[\leadsto -re \cdot im \]

   9. Applied rewrites 32.9%

      \[\leadsto -re \cdot im \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 32.9% accurate, 13.2× speedup

Math

\[-re \cdot im \]

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 65.9%

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

2. Taylor expanded in im around 0

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

   1. lower-*.f64 N/A

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

   2. lower-*.f64 N/A

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

   3. lower-sin.f64 51.3%

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

4. Applied rewrites 51.3%

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

5. Taylor expanded in re around 0

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

   1. lower-*.f64 32.9%

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

7. Applied rewrites 32.9%

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

   1. lift-*.f64 N/A

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

   2. mul-1-neg N/A

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

   3. lower-neg.f64 32.9%

      \[\leadsto -im \cdot re \]

   4. lift-*.f64 N/A

      \[\leadsto -im \cdot re \]

   5. *-commutative N/A

      \[\leadsto -re \cdot im \]

   6. lower-*.f64 32.9%

      \[\leadsto -re \cdot im \]

9. Applied rewrites 32.9%

   \[\leadsto -re \cdot im \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2025212 
(FPCore (re im)
  :name "math.cos on complex, imaginary part"
  :precision binary64
  (* (* 0.5 (sin re)) (- (exp (- im)) (exp im))))