math.exp on complex, imaginary part

Percentage Accurate: 100.0% → 100.0%

Time: 2.9s

Alternatives: 9

Speedup: 1.0×

• 23.9% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

Alternative 2: 92.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{re} \cdot \sin im\\ t_1 := e^{re} \cdot im\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;e^{re} \cdot \left(im \cdot \mathsf{fma}\left(-0.16666666666666666 \cdot im, im, 1\right)\right)\\ \mathbf{elif}\;t\_0 \leq -0.05:\\ \;\;\;\;\sin im\\ \mathbf{elif}\;t\_0 \leq 10^{-59}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 1:\\ \;\;\;\;\sin im \cdot \left(re - -1\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (exp re) (sin im))) (t_1 (* (exp re) im)))
   (if (<= t_0 (- INFINITY))
     (* (exp re) (* im (fma (* -0.16666666666666666 im) im 1.0)))
     (if (<= t_0 -0.05)
       (sin im)
       (if (<= t_0 1e-59)
         t_1
         (if (<= t_0 1.0) (* (sin im) (- re -1.0)) t_1))))))

C

double code(double re, double im) {
	double t_0 = exp(re) * sin(im);
	double t_1 = exp(re) * im;
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = exp(re) * (im * fma((-0.16666666666666666 * im), im, 1.0));
	} else if (t_0 <= -0.05) {
		tmp = sin(im);
	} else if (t_0 <= 1e-59) {
		tmp = t_1;
	} else if (t_0 <= 1.0) {
		tmp = sin(im) * (re - -1.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(exp(re) * sin(im))
	t_1 = Float64(exp(re) * im)
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(exp(re) * Float64(im * fma(Float64(-0.16666666666666666 * im), im, 1.0)));
	elseif (t_0 <= -0.05)
		tmp = sin(im);
	elseif (t_0 <= 1e-59)
		tmp = t_1;
	elseif (t_0 <= 1.0)
		tmp = Float64(sin(im) * Float64(re - -1.0));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], N[(N[Exp[re], $MachinePrecision] * N[(im * N[(N[(-0.16666666666666666 * im), $MachinePrecision] * im + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, -0.05], N[Sin[im], $MachinePrecision], If[LessEqual[t$95$0, 1e-59], t$95$1, If[LessEqual[t$95$0, 1.0], N[(N[Sin[im], $MachinePrecision] * N[(re - -1.0), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 4 regimes

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 60.3

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

   4. Applied rewrites 60.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. lift-pow.f64 N/A

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

      5. unpow2 N/A

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

      6. associate-*r* N/A

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

      7. lower-fma.f64 N/A

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

      8. lower-*.f64 60.3

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

   6. Applied rewrites 60.3%

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

   if -inf.0 < (*.f64 (exp.f64 re) (sin.f64 im)) < -0.050000000000000003

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. lower-sin.f64 51.4

         \[\leadsto \sin im \]

   4. Applied rewrites 51.4%

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

   if -0.050000000000000003 < (*.f64 (exp.f64 re) (sin.f64 im)) < 1e-59 or 1 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

   4. Applied rewrites 68.3%

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

   if 1e-59 < (*.f64 (exp.f64 re) (sin.f64 im)) < 1

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. lower-+.f64 52.0

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

   4. Applied rewrites 52.0%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 52.0

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. add-flip N/A

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

      7. lower--.f64 N/A

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

      8. metadata-eval 52.0

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

   6. Applied rewrites 52.0%

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

Alternative 3: 92.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{re} \cdot \sin im\\ t_1 := e^{re} \cdot im\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;e^{re} \cdot \left(im \cdot \mathsf{fma}\left(-0.16666666666666666 \cdot im, im, 1\right)\right)\\ \mathbf{elif}\;t\_0 \leq -0.05:\\ \;\;\;\;\sin im\\ \mathbf{elif}\;t\_0 \leq 5 \cdot 10^{-38}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 1:\\ \;\;\;\;\sin im\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (let* ((t_0 (* (exp re) (sin im))) (t_1 (* (exp re) im)))
   (if (<= t_0 (- INFINITY))
     (* (exp re) (* im (fma (* -0.16666666666666666 im) im 1.0)))
     (if (<= t_0 -0.05)
       (sin im)
       (if (<= t_0 5e-38) t_1 (if (<= t_0 1.0) (sin im) t_1))))))

C

double code(double re, double im) {
	double t_0 = exp(re) * sin(im);
	double t_1 = exp(re) * im;
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = exp(re) * (im * fma((-0.16666666666666666 * im), im, 1.0));
	} else if (t_0 <= -0.05) {
		tmp = sin(im);
	} else if (t_0 <= 5e-38) {
		tmp = t_1;
	} else if (t_0 <= 1.0) {
		tmp = sin(im);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(exp(re) * sin(im))
	t_1 = Float64(exp(re) * im)
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(exp(re) * Float64(im * fma(Float64(-0.16666666666666666 * im), im, 1.0)));
	elseif (t_0 <= -0.05)
		tmp = sin(im);
	elseif (t_0 <= 5e-38)
		tmp = t_1;
	elseif (t_0 <= 1.0)
		tmp = sin(im);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], N[(N[Exp[re], $MachinePrecision] * N[(im * N[(N[(-0.16666666666666666 * im), $MachinePrecision] * im + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, -0.05], N[Sin[im], $MachinePrecision], If[LessEqual[t$95$0, 5e-38], t$95$1, If[LessEqual[t$95$0, 1.0], N[Sin[im], $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 3 regimes

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

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 60.3

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

   4. Applied rewrites 60.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. lift-pow.f64 N/A

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

      5. unpow2 N/A

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

      6. associate-*r* N/A

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

      7. lower-fma.f64 N/A

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

      8. lower-*.f64 60.3

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

   6. Applied rewrites 60.3%

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

   if -inf.0 < (*.f64 (exp.f64 re) (sin.f64 im)) < -0.050000000000000003 or 5.00000000000000033e-38 < (*.f64 (exp.f64 re) (sin.f64 im)) < 1

   1. Initial program 100.0%

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

   2. Taylor expanded in re around 0

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

      1. lower-sin.f64 51.4

         \[\leadsto \sin im \]

   4. Applied rewrites 51.4%

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

   if -0.050000000000000003 < (*.f64 (exp.f64 re) (sin.f64 im)) < 5.00000000000000033e-38 or 1 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

   4. Applied rewrites 68.3%

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

Alternative 4: 68.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \sin im \leq -0.05:\\ \;\;\;\;e^{re} \cdot \left(im \cdot \mathsf{fma}\left(-0.16666666666666666 \cdot im, im, 1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (sin im)) -0.05)
   (* (exp re) (* im (fma (* -0.16666666666666666 im) im 1.0)))
   (* (exp re) im)))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * sin(im)) <= -0.05) {
		tmp = exp(re) * (im * fma((-0.16666666666666666 * im), im, 1.0));
	} else {
		tmp = exp(re) * im;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * sin(im)) <= -0.05)
		tmp = Float64(exp(re) * Float64(im * fma(Float64(-0.16666666666666666 * im), im, 1.0)));
	else
		tmp = Float64(exp(re) * im);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], -0.05], N[(N[Exp[re], $MachinePrecision] * N[(im * N[(N[(-0.16666666666666666 * im), $MachinePrecision] * im + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.050000000000000003

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 60.3

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

   4. Applied rewrites 60.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. lift-pow.f64 N/A

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

      5. unpow2 N/A

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

      6. associate-*r* N/A

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

      7. lower-fma.f64 N/A

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

      8. lower-*.f64 60.3

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

   6. Applied rewrites 60.3%

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

   if -0.050000000000000003 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

   4. Applied rewrites 68.3%

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

Alternative 5: 68.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \sin im \leq -0.05:\\ \;\;\;\;\left(re - -1\right) \cdot \mathsf{fma}\left(im \cdot \left(im \cdot im\right), -0.16666666666666666, im\right)\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (sin im)) -0.05)
   (* (- re -1.0) (fma (* im (* im im)) -0.16666666666666666 im))
   (* (exp re) im)))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * sin(im)) <= -0.05) {
		tmp = (re - -1.0) * fma((im * (im * im)), -0.16666666666666666, im);
	} else {
		tmp = exp(re) * im;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * sin(im)) <= -0.05)
		tmp = Float64(Float64(re - -1.0) * fma(Float64(im * Float64(im * im)), -0.16666666666666666, im));
	else
		tmp = Float64(exp(re) * im);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], -0.05], N[(N[(re - -1.0), $MachinePrecision] * N[(N[(im * N[(im * im), $MachinePrecision]), $MachinePrecision] * -0.16666666666666666 + im), $MachinePrecision]), $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.050000000000000003

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 60.3

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

   4. Applied rewrites 60.3%

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

      1. lift-*.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. distribute-lft-in N/A

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

      5. lift-*.f64 N/A

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

      6. *-commutative N/A

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

      7. associate-*r* N/A

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

      8. *-rgt-identity N/A

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

      9. lower-fma.f64 N/A

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

      10. lower-*.f64 60.3

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

      11. lift-pow.f64 N/A

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

      12. unpow2 N/A

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

      13. lower-*.f64 60.3

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

   6. Applied rewrites 60.3%

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

   7. Taylor expanded in re around 0

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

      1. lower-+.f64 31.5

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

   9. Applied rewrites 31.5%

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

       1. lift-+.f64 N/A

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

       2. +-commutative N/A

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

       3. add-flip N/A

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

       4. lower--.f64 N/A

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

       5. metadata-eval 31.5

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

   11. Applied rewrites 31.5%

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

   if -0.050000000000000003 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

   4. Applied rewrites 68.3%

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

Alternative 6: 62.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{re} \cdot \sin im \leq -0.05:\\ \;\;\;\;1 \cdot \left(im \cdot \mathsf{fma}\left(-0.16666666666666666 \cdot im, im, 1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;e^{re} \cdot im\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (exp re) (sin im)) -0.05)
   (* 1.0 (* im (fma (* -0.16666666666666666 im) im 1.0)))
   (* (exp re) im)))

C

double code(double re, double im) {
	double tmp;
	if ((exp(re) * sin(im)) <= -0.05) {
		tmp = 1.0 * (im * fma((-0.16666666666666666 * im), im, 1.0));
	} else {
		tmp = exp(re) * im;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(exp(re) * sin(im)) <= -0.05)
		tmp = Float64(1.0 * Float64(im * fma(Float64(-0.16666666666666666 * im), im, 1.0)));
	else
		tmp = Float64(exp(re) * im);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[Exp[re], $MachinePrecision] * N[Sin[im], $MachinePrecision]), $MachinePrecision], -0.05], N[(1.0 * N[(im * N[(N[(-0.16666666666666666 * im), $MachinePrecision] * im + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Exp[re], $MachinePrecision] * im), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 re) (sin.f64 im)) < -0.050000000000000003

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 60.3

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

   4. Applied rewrites 60.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. lift-pow.f64 N/A

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

      5. unpow2 N/A

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

      6. associate-*r* N/A

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

      7. lower-fma.f64 N/A

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

      8. lower-*.f64 60.3

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

   6. Applied rewrites 60.3%

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

   7. Taylor expanded in re around 0

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

   9. Applied rewrites 30.3%

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

   if -0.050000000000000003 < (*.f64 (exp.f64 re) (sin.f64 im))

   1. Initial program 100.0%

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

   2. Taylor expanded in im around 0

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

   4. Applied rewrites 68.3%

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

Alternative 7: 61.5% accurate, 3.3× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

2. Taylor expanded in im around 0

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

4. Applied rewrites 68.3%

   \[\leadsto e^{re} \cdot \color{blue}{im} \]
5. Add Preprocessing

Alternative 8: 29.3% accurate, 6.9× speedup

Math

\[\begin{array}{l} \\ \left(1 + re\right) \cdot im \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

2. Taylor expanded in im around 0

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

   1. lower-*.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-pow.f64 60.3

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

4. Applied rewrites 60.3%

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

   1. lift-*.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. +-commutative N/A

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

   4. distribute-lft-in N/A

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

   5. lift-*.f64 N/A

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

   6. *-commutative N/A

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

   7. associate-*r* N/A

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

   8. *-rgt-identity N/A

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

   9. lower-fma.f64 N/A

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

   10. lower-*.f64 60.3

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

   11. lift-pow.f64 N/A

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

   12. unpow2 N/A

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

   13. lower-*.f64 60.3

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

6. Applied rewrites 60.3%

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

7. Taylor expanded in re around 0

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

   1. lower-+.f64 31.5

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

9. Applied rewrites 31.5%

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

10. Taylor expanded in im around 0

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

12. Applied rewrites 29.3%

    \[\leadsto \left(1 + re\right) \cdot \color{blue}{im} \]
13. Add Preprocessing

Alternative 9: 26.4% accurate, 11.6× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

2. Taylor expanded in im around 0

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

   1. lower-*.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-pow.f64 60.3

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

4. Applied rewrites 60.3%

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

   1. lift-*.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. +-commutative N/A

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

   4. distribute-lft-in N/A

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

   5. lift-*.f64 N/A

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

   6. *-commutative N/A

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

   7. associate-*r* N/A

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

   8. *-rgt-identity N/A

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

   9. lower-fma.f64 N/A

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

   10. lower-*.f64 60.3

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

   11. lift-pow.f64 N/A

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

   12. unpow2 N/A

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

   13. lower-*.f64 60.3

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

6. Applied rewrites 60.3%

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

7. Taylor expanded in re around 0

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

9. Applied rewrites 30.3%

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

10. Taylor expanded in im around 0

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

12. Applied rewrites 26.4%

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

Reproduce

herbie shell --seed 2025156 
(FPCore (re im)
  :name "math.exp on complex, imaginary part"
  :precision binary64
  (* (exp re) (sin im)))