math.sin on complex, real part

Percentage Accurate: 100.0% → 100.0%

Time: 2.6s

Alternatives: 11

Speedup: 1.2×

• 48.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

double code(double re, double im) {
	return (0.5 * sin(re)) * (exp((0.0 - 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((0.0d0 - im)) + exp(im))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double re, double im) {
	return (0.5 * sin(re)) * (exp((0.0 - 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((0.0d0 - im)) + exp(im))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \left(\sin re \cdot \left(\cosh im \cdot 2\right)\right) \cdot 0.5 \end{array} \]

FPCore

(FPCore (re im) :precision binary64 (* (* (sin re) (* (cosh im) 2.0)) 0.5))

C

double code(double re, double im) {
	return (sin(re) * (cosh(im) * 2.0)) * 0.5;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = (sin(re) * (cosh(im) * 2.0d0)) * 0.5d0
end function

Java

public static double code(double re, double im) {
	return (Math.sin(re) * (Math.cosh(im) * 2.0)) * 0.5;
}

Python

def code(re, im):
	return (math.sin(re) * (math.cosh(im) * 2.0)) * 0.5

Julia

function code(re, im)
	return Float64(Float64(sin(re) * Float64(cosh(im) * 2.0)) * 0.5)
end

MATLAB

function tmp = code(re, im)
	tmp = (sin(re) * (cosh(im) * 2.0)) * 0.5;
end

Wolfram

code[re_, im_] := N[(N[(N[Sin[re], $MachinePrecision] * N[(N[Cosh[im], $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]
2. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. *-commutative N/A

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

   3. lower-*.f64 100.0

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

   4. lift-+.f64 N/A

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

   5. +-commutative N/A

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

   6. lower-+.f64 100.0

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

   7. lift--.f64 N/A

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

   8. sub0-neg N/A

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

   9. lower-neg.f64 100.0

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

   10. lift-*.f64 N/A

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

   11. *-commutative N/A

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

   12. lower-*.f64 100.0

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

3. Applied rewrites 100.0%

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

   1. lift-*.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-*r* N/A

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

   4. lower-*.f64 N/A

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

   5. lower-*.f64 100.0

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

   6. lift-+.f64 N/A

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

   7. lift-exp.f64 N/A

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

   8. lift-exp.f64 N/A

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

   9. lift-neg.f64 N/A

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

   10. cosh-undef N/A

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

   11. lower-*.f64 N/A

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

   12. lower-cosh.f64 100.0

       \[\leadsto \left(\left(2 \cdot \color{blue}{\cosh im}\right) \cdot \sin re\right) \cdot 0.5 \]

5. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\left(\left(2 \cdot \cosh im\right) \cdot \sin re\right) \cdot 0.5} \]
6. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. lift-sin.f64 N/A

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

   3. *-commutative N/A

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

   4. lower-*.f64 N/A

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

   5. lift-sin.f64 100.0

      \[\leadsto \left(\color{blue}{\sin re} \cdot \left(2 \cdot \cosh im\right)\right) \cdot 0.5 \]

   6. lift-*.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 100.0

      \[\leadsto \left(\sin re \cdot \color{blue}{\left(\cosh im \cdot 2\right)}\right) \cdot 0.5 \]

7. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\left(\sin re \cdot \left(\cosh im \cdot 2\right)\right) \cdot 0.5} \]
8. Add Preprocessing

Alternative 2: 75.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right)\\ \mathbf{if}\;t\_0 \leq -\infty:\\ \;\;\;\;\left(e^{im} + 1\right) \cdot \left(\left(\mathsf{fma}\left(re \cdot re, -0.16666666666666666, 1\right) \cdot re\right) \cdot 0.5\right)\\ \mathbf{elif}\;t\_0 \leq 1:\\ \;\;\;\;2 \cdot \left(\sin re \cdot 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\left(0.5 \cdot re\right) \cdot \left(1 + e^{im}\right)\\ \end{array} \end{array} \]

FPCore

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

C

double code(double re, double im) {
	double t_0 = (0.5 * sin(re)) * (exp((0.0 - im)) + exp(im));
	double tmp;
	if (t_0 <= -((double) INFINITY)) {
		tmp = (exp(im) + 1.0) * ((fma((re * re), -0.16666666666666666, 1.0) * re) * 0.5);
	} else if (t_0 <= 1.0) {
		tmp = 2.0 * (sin(re) * 0.5);
	} else {
		tmp = (0.5 * re) * (1.0 + exp(im));
	}
	return tmp;
}

Julia

function code(re, im)
	t_0 = Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(0.0 - im)) + exp(im)))
	tmp = 0.0
	if (t_0 <= Float64(-Inf))
		tmp = Float64(Float64(exp(im) + 1.0) * Float64(Float64(fma(Float64(re * re), -0.16666666666666666, 1.0) * re) * 0.5));
	elseif (t_0 <= 1.0)
		tmp = Float64(2.0 * Float64(sin(re) * 0.5));
	else
		tmp = Float64(Float64(0.5 * re) * Float64(1.0 + exp(im)));
	end
	return tmp
end

Wolfram

code[re_, im_] := Block[{t$95$0 = N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[N[(0.0 - im), $MachinePrecision]], $MachinePrecision] + N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, (-Infinity)], N[(N[(N[Exp[im], $MachinePrecision] + 1.0), $MachinePrecision] * N[(N[(N[(N[(re * re), $MachinePrecision] * -0.16666666666666666 + 1.0), $MachinePrecision] * re), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 1.0], N[(2.0 * N[(N[Sin[re], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision], N[(N[(0.5 * re), $MachinePrecision] * N[(1.0 + N[Exp[im], $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 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < -inf.0

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in im around 0

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

   4. Applied rewrites 75.1%

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

   5. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 48.5

         \[\leadsto \left(0.5 \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{\color{blue}{2}}\right)\right)\right) \cdot \left(1 + e^{im}\right) \]

   7. Applied rewrites 48.5%

      \[\leadsto \left(0.5 \cdot \color{blue}{\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)}\right) \cdot \left(1 + e^{im}\right) \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 48.5

         \[\leadsto \color{blue}{\left(1 + e^{im}\right) \cdot \left(0.5 \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)\right)} \]

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 48.5

         \[\leadsto \color{blue}{\left(e^{im} + 1\right)} \cdot \left(0.5 \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)\right) \]

      7. lift-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 48.5

         \[\leadsto \left(e^{im} + 1\right) \cdot \color{blue}{\left(\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right) \cdot 0.5\right)} \]

   9. Applied rewrites 48.5%

      \[\leadsto \color{blue}{\left(e^{im} + 1\right) \cdot \left(\left(\mathsf{fma}\left(re \cdot re, -0.16666666666666666, 1\right) \cdot re\right) \cdot 0.5\right)} \]

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

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 100.0

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 100.0

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

      7. lift--.f64 N/A

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

      8. sub0-neg N/A

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

      9. lower-neg.f64 100.0

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

      10. lift-*.f64 N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 100.0

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

   3. Applied rewrites 100.0%

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

   4. Taylor expanded in im around 0

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

   6. Applied rewrites 51.5%

      \[\leadsto \color{blue}{2} \cdot \left(\sin re \cdot 0.5\right) \]

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

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in im around 0

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

   4. Applied rewrites 75.1%

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

   5. Taylor expanded in re around 0

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

   7. Applied rewrites 45.2%

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

Alternative 3: 71.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \left(0.5 \cdot \sin re\right) \cdot \left(1 + e^{im}\right) \end{array} \]

FPCore

(FPCore (re im) :precision binary64 (* (* 0.5 (sin re)) (+ 1.0 (exp im))))

C

double code(double re, double im) {
	return (0.5 * sin(re)) * (1.0 + 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)) * (1.0d0 + exp(im))
end function

Java

public static double code(double re, double im) {
	return (0.5 * Math.sin(re)) * (1.0 + Math.exp(im));
}

Python

def code(re, im):
	return (0.5 * math.sin(re)) * (1.0 + math.exp(im))

Julia

function code(re, im)
	return Float64(Float64(0.5 * sin(re)) * Float64(1.0 + exp(im)))
end

MATLAB

function tmp = code(re, im)
	tmp = (0.5 * sin(re)) * (1.0 + exp(im));
end

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

2. Taylor expanded in im around 0

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

4. Applied rewrites 75.1%

   \[\leadsto \left(0.5 \cdot \sin re\right) \cdot \left(\color{blue}{1} + e^{im}\right) \]
5. Add Preprocessing

Alternative 4: 60.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \leq -0.01:\\ \;\;\;\;\left(e^{im} + 1\right) \cdot \left(\left(\mathsf{fma}\left(re \cdot re, -0.16666666666666666, 1\right) \cdot re\right) \cdot 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\left(re \cdot \left(2 \cdot \cosh im\right)\right) \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (* 0.5 (sin re)) (+ (exp (- 0.0 im)) (exp im))) -0.01)
   (* (+ (exp im) 1.0) (* (* (fma (* re re) -0.16666666666666666 1.0) re) 0.5))
   (* (* re (* 2.0 (cosh im))) 0.5)))

C

double code(double re, double im) {
	double tmp;
	if (((0.5 * sin(re)) * (exp((0.0 - im)) + exp(im))) <= -0.01) {
		tmp = (exp(im) + 1.0) * ((fma((re * re), -0.16666666666666666, 1.0) * re) * 0.5);
	} else {
		tmp = (re * (2.0 * cosh(im))) * 0.5;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(0.0 - im)) + exp(im))) <= -0.01)
		tmp = Float64(Float64(exp(im) + 1.0) * Float64(Float64(fma(Float64(re * re), -0.16666666666666666, 1.0) * re) * 0.5));
	else
		tmp = Float64(Float64(re * Float64(2.0 * cosh(im))) * 0.5);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[N[(0.0 - im), $MachinePrecision]], $MachinePrecision] + N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -0.01], N[(N[(N[Exp[im], $MachinePrecision] + 1.0), $MachinePrecision] * N[(N[(N[(N[(re * re), $MachinePrecision] * -0.16666666666666666 + 1.0), $MachinePrecision] * re), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision], N[(N[(re * N[(2.0 * N[Cosh[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (+.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < -0.0100000000000000002

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in im around 0

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

   4. Applied rewrites 75.1%

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

   5. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 48.5

         \[\leadsto \left(0.5 \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{\color{blue}{2}}\right)\right)\right) \cdot \left(1 + e^{im}\right) \]

   7. Applied rewrites 48.5%

      \[\leadsto \left(0.5 \cdot \color{blue}{\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)}\right) \cdot \left(1 + e^{im}\right) \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 48.5

         \[\leadsto \color{blue}{\left(1 + e^{im}\right) \cdot \left(0.5 \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)\right)} \]

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 48.5

         \[\leadsto \color{blue}{\left(e^{im} + 1\right)} \cdot \left(0.5 \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)\right) \]

      7. lift-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 48.5

         \[\leadsto \left(e^{im} + 1\right) \cdot \color{blue}{\left(\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right) \cdot 0.5\right)} \]

   9. Applied rewrites 48.5%

      \[\leadsto \color{blue}{\left(e^{im} + 1\right) \cdot \left(\left(\mathsf{fma}\left(re \cdot re, -0.16666666666666666, 1\right) \cdot re\right) \cdot 0.5\right)} \]

   if -0.0100000000000000002 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (+.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-exp.f64 N/A

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

      5. lower-exp.f64 N/A

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

      6. lower-neg.f64 63.6

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

   4. Applied rewrites 63.6%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 63.6

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

      4. lift-+.f64 N/A

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

      5. lift-exp.f64 N/A

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

      6. lift-exp.f64 N/A

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

      7. lift-neg.f64 N/A

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

      8. cosh-undef N/A

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

      9. lower-*.f64 N/A

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

      10. lower-cosh.f64 63.6

          \[\leadsto \left(re \cdot \left(2 \cdot \cosh im\right)\right) \cdot 0.5 \]

   6. Applied rewrites 63.6%

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

Alternative 5: 56.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \leq -0.01:\\ \;\;\;\;\left(\left(1 + im\right) + 1\right) \cdot \left(\left(\mathsf{fma}\left(re \cdot re, -0.16666666666666666, 1\right) \cdot re\right) \cdot 0.5\right)\\ \mathbf{else}:\\ \;\;\;\;\left(re \cdot \left(2 \cdot \cosh im\right)\right) \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* (* 0.5 (sin re)) (+ (exp (- 0.0 im)) (exp im))) -0.01)
   (*
    (+ (+ 1.0 im) 1.0)
    (* (* (fma (* re re) -0.16666666666666666 1.0) re) 0.5))
   (* (* re (* 2.0 (cosh im))) 0.5)))

C

double code(double re, double im) {
	double tmp;
	if (((0.5 * sin(re)) * (exp((0.0 - im)) + exp(im))) <= -0.01) {
		tmp = ((1.0 + im) + 1.0) * ((fma((re * re), -0.16666666666666666, 1.0) * re) * 0.5);
	} else {
		tmp = (re * (2.0 * cosh(im))) * 0.5;
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(Float64(0.5 * sin(re)) * Float64(exp(Float64(0.0 - im)) + exp(im))) <= -0.01)
		tmp = Float64(Float64(Float64(1.0 + im) + 1.0) * Float64(Float64(fma(Float64(re * re), -0.16666666666666666, 1.0) * re) * 0.5));
	else
		tmp = Float64(Float64(re * Float64(2.0 * cosh(im))) * 0.5);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision] * N[(N[Exp[N[(0.0 - im), $MachinePrecision]], $MachinePrecision] + N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], -0.01], N[(N[(N[(1.0 + im), $MachinePrecision] + 1.0), $MachinePrecision] * N[(N[(N[(N[(re * re), $MachinePrecision] * -0.16666666666666666 + 1.0), $MachinePrecision] * re), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision], N[(N[(re * N[(2.0 * N[Cosh[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (+.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im))) < -0.0100000000000000002

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in im around 0

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

   4. Applied rewrites 75.1%

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

   5. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-pow.f64 48.5

         \[\leadsto \left(0.5 \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{\color{blue}{2}}\right)\right)\right) \cdot \left(1 + e^{im}\right) \]

   7. Applied rewrites 48.5%

      \[\leadsto \left(0.5 \cdot \color{blue}{\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)}\right) \cdot \left(1 + e^{im}\right) \]

   8. Taylor expanded in im around 0

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

      1. lower-+.f64 36.2

         \[\leadsto \left(0.5 \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)\right) \cdot \left(1 + \left(1 + \color{blue}{im}\right)\right) \]

   10. Applied rewrites 36.2%

       \[\leadsto \left(0.5 \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)\right) \cdot \left(1 + \color{blue}{\left(1 + im\right)}\right) \]
   11. Step-by-step derivation

       1. lift-*.f64 N/A

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

       2. *-commutative N/A

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

       3. lower-*.f64 36.2

          \[\leadsto \color{blue}{\left(1 + \left(1 + im\right)\right) \cdot \left(0.5 \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)\right)} \]

       4. lift-+.f64 N/A

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

       5. +-commutative N/A

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

       6. lower-+.f64 36.2

          \[\leadsto \color{blue}{\left(\left(1 + im\right) + 1\right)} \cdot \left(0.5 \cdot \left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right)\right) \]

       7. lift-*.f64 N/A

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

       8. *-commutative N/A

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

       9. lower-*.f64 36.2

          \[\leadsto \left(\left(1 + im\right) + 1\right) \cdot \color{blue}{\left(\left(re \cdot \left(1 + -0.16666666666666666 \cdot {re}^{2}\right)\right) \cdot 0.5\right)} \]

   12. Applied rewrites 36.2%

       \[\leadsto \color{blue}{\left(\left(1 + im\right) + 1\right) \cdot \left(\left(\mathsf{fma}\left(re \cdot re, -0.16666666666666666, 1\right) \cdot re\right) \cdot 0.5\right)} \]

   if -0.0100000000000000002 < (*.f64 (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) (+.f64 (exp.f64 (-.f64 #s(literal 0 binary64) im)) (exp.f64 im)))

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-exp.f64 N/A

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

      5. lower-exp.f64 N/A

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

      6. lower-neg.f64 63.6

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

   4. Applied rewrites 63.6%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 63.6

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

      4. lift-+.f64 N/A

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

      5. lift-exp.f64 N/A

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

      6. lift-exp.f64 N/A

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

      7. lift-neg.f64 N/A

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

      8. cosh-undef N/A

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

      9. lower-*.f64 N/A

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

      10. lower-cosh.f64 63.6

          \[\leadsto \left(re \cdot \left(2 \cdot \cosh im\right)\right) \cdot 0.5 \]

   6. Applied rewrites 63.6%

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

Alternative 6: 49.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin re \leq -0.01:\\ \;\;\;\;0.5 \cdot \left(re \cdot \left(1 + \left(1 + -1 \cdot im\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(re \cdot \left(2 \cdot \cosh im\right)\right) \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* 0.5 (sin re)) -0.01)
   (* 0.5 (* re (+ 1.0 (+ 1.0 (* -1.0 im)))))
   (* (* re (* 2.0 (cosh im))) 0.5)))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(re)) <= -0.01) {
		tmp = 0.5 * (re * (1.0 + (1.0 + (-1.0 * im))));
	} else {
		tmp = (re * (2.0 * cosh(im))) * 0.5;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if ((0.5d0 * sin(re)) <= (-0.01d0)) then
        tmp = 0.5d0 * (re * (1.0d0 + (1.0d0 + ((-1.0d0) * im))))
    else
        tmp = (re * (2.0d0 * cosh(im))) * 0.5d0
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if ((0.5 * Math.sin(re)) <= -0.01) {
		tmp = 0.5 * (re * (1.0 + (1.0 + (-1.0 * im))));
	} else {
		tmp = (re * (2.0 * Math.cosh(im))) * 0.5;
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if (0.5 * math.sin(re)) <= -0.01:
		tmp = 0.5 * (re * (1.0 + (1.0 + (-1.0 * im))))
	else:
		tmp = (re * (2.0 * math.cosh(im))) * 0.5
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= -0.01)
		tmp = Float64(0.5 * Float64(re * Float64(1.0 + Float64(1.0 + Float64(-1.0 * im)))));
	else
		tmp = Float64(Float64(re * Float64(2.0 * cosh(im))) * 0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if ((0.5 * sin(re)) <= -0.01)
		tmp = 0.5 * (re * (1.0 + (1.0 + (-1.0 * im))));
	else
		tmp = (re * (2.0 * cosh(im))) * 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], -0.01], N[(0.5 * N[(re * N[(1.0 + N[(1.0 + N[(-1.0 * im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(re * N[(2.0 * N[Cosh[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0100000000000000002

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-exp.f64 N/A

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

      5. lower-exp.f64 N/A

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

      6. lower-neg.f64 63.6

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

   4. Applied rewrites 63.6%

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

   5. Taylor expanded in im around 0

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

   7. Applied rewrites 44.7%

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

   8. Taylor expanded in im around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 33.0

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

   10. Applied rewrites 33.0%

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

   if -0.0100000000000000002 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-exp.f64 N/A

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

      5. lower-exp.f64 N/A

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

      6. lower-neg.f64 63.6

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

   4. Applied rewrites 63.6%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 63.6

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

      4. lift-+.f64 N/A

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

      5. lift-exp.f64 N/A

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

      6. lift-exp.f64 N/A

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

      7. lift-neg.f64 N/A

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

      8. cosh-undef N/A

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

      9. lower-*.f64 N/A

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

      10. lower-cosh.f64 63.6

          \[\leadsto \left(re \cdot \left(2 \cdot \cosh im\right)\right) \cdot 0.5 \]

   6. Applied rewrites 63.6%

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

Alternative 7: 48.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin re \leq -0.01:\\ \;\;\;\;0.5 \cdot \left(re \cdot \left(1 + \left(1 + -1 \cdot im\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(0.5 \cdot re\right) \cdot \left(1 + e^{im}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* 0.5 (sin re)) -0.01)
   (* 0.5 (* re (+ 1.0 (+ 1.0 (* -1.0 im)))))
   (* (* 0.5 re) (+ 1.0 (exp im)))))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(re)) <= -0.01) {
		tmp = 0.5 * (re * (1.0 + (1.0 + (-1.0 * im))));
	} else {
		tmp = (0.5 * re) * (1.0 + exp(im));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(re, im)
use fmin_fmax_functions
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if ((0.5d0 * sin(re)) <= (-0.01d0)) then
        tmp = 0.5d0 * (re * (1.0d0 + (1.0d0 + ((-1.0d0) * im))))
    else
        tmp = (0.5d0 * re) * (1.0d0 + exp(im))
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if ((0.5 * Math.sin(re)) <= -0.01) {
		tmp = 0.5 * (re * (1.0 + (1.0 + (-1.0 * im))));
	} else {
		tmp = (0.5 * re) * (1.0 + Math.exp(im));
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if (0.5 * math.sin(re)) <= -0.01:
		tmp = 0.5 * (re * (1.0 + (1.0 + (-1.0 * im))))
	else:
		tmp = (0.5 * re) * (1.0 + math.exp(im))
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= -0.01)
		tmp = Float64(0.5 * Float64(re * Float64(1.0 + Float64(1.0 + Float64(-1.0 * im)))));
	else
		tmp = Float64(Float64(0.5 * re) * Float64(1.0 + exp(im)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if ((0.5 * sin(re)) <= -0.01)
		tmp = 0.5 * (re * (1.0 + (1.0 + (-1.0 * im))));
	else
		tmp = (0.5 * re) * (1.0 + exp(im));
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], -0.01], N[(0.5 * N[(re * N[(1.0 + N[(1.0 + N[(-1.0 * im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(0.5 * re), $MachinePrecision] * N[(1.0 + N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0100000000000000002

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-exp.f64 N/A

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

      5. lower-exp.f64 N/A

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

      6. lower-neg.f64 63.6

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

   4. Applied rewrites 63.6%

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

   5. Taylor expanded in im around 0

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

   7. Applied rewrites 44.7%

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

   8. Taylor expanded in im around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 33.0

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

   10. Applied rewrites 33.0%

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

   if -0.0100000000000000002 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in im around 0

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

   4. Applied rewrites 75.1%

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

   5. Taylor expanded in re around 0

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

   7. Applied rewrites 45.2%

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

Alternative 8: 47.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;0.5 \cdot \sin re \leq -0.01:\\ \;\;\;\;0.5 \cdot \left(re \cdot \left(1 + \left(1 + -1 \cdot im\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\left(im \cdot im\right) \cdot re, 0.5, re\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= (* 0.5 (sin re)) -0.01)
   (* 0.5 (* re (+ 1.0 (+ 1.0 (* -1.0 im)))))
   (fma (* (* im im) re) 0.5 re)))

C

double code(double re, double im) {
	double tmp;
	if ((0.5 * sin(re)) <= -0.01) {
		tmp = 0.5 * (re * (1.0 + (1.0 + (-1.0 * im))));
	} else {
		tmp = fma(((im * im) * re), 0.5, re);
	}
	return tmp;
}

Julia

function code(re, im)
	tmp = 0.0
	if (Float64(0.5 * sin(re)) <= -0.01)
		tmp = Float64(0.5 * Float64(re * Float64(1.0 + Float64(1.0 + Float64(-1.0 * im)))));
	else
		tmp = fma(Float64(Float64(im * im) * re), 0.5, re);
	end
	return tmp
end

Wolfram

code[re_, im_] := If[LessEqual[N[(0.5 * N[Sin[re], $MachinePrecision]), $MachinePrecision], -0.01], N[(0.5 * N[(re * N[(1.0 + N[(1.0 + N[(-1.0 * im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(im * im), $MachinePrecision] * re), $MachinePrecision] * 0.5 + re), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 1/2 binary64) (sin.f64 re)) < -0.0100000000000000002

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-exp.f64 N/A

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

      5. lower-exp.f64 N/A

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

      6. lower-neg.f64 63.6

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

   4. Applied rewrites 63.6%

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

   5. Taylor expanded in im around 0

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

   7. Applied rewrites 44.7%

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

   8. Taylor expanded in im around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 33.0

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

   10. Applied rewrites 33.0%

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

   if -0.0100000000000000002 < (*.f64 #s(literal 1/2 binary64) (sin.f64 re))

   1. Initial program 100.0%

      \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

   2. Taylor expanded in im around 0

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

      1. lower-+.f64 N/A

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

      2. lower-sin.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-pow.f64 N/A

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

      6. lower-sin.f64 77.0

         \[\leadsto \sin re + 0.5 \cdot \left({im}^{2} \cdot \sin re\right) \]

   4. Applied rewrites 77.0%

      \[\leadsto \color{blue}{\sin re + 0.5 \cdot \left({im}^{2} \cdot \sin re\right)} \]

   5. Taylor expanded in re around 0

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

   7. Applied rewrites 52.6%

      \[\leadsto re + \color{blue}{0.5} \cdot \left({im}^{2} \cdot \sin re\right) \]

   8. Taylor expanded in re around 0

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

   10. Applied rewrites 48.8%

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

       1. lift-+.f64 N/A

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

       2. +-commutative N/A

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

       3. lift-*.f64 N/A

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

       4. *-commutative N/A

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

       5. lower-fma.f64 48.8

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

       6. lift-pow.f64 N/A

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

       7. unpow2 N/A

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

       8. lower-*.f64 48.8

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

   12. Applied rewrites 48.8%

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

Alternative 9: 45.1% accurate, 5.4× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

2. Taylor expanded in im around 0

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

   1. lower-+.f64 N/A

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

   2. lower-sin.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-*.f64 N/A

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

   5. lower-pow.f64 N/A

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

   6. lower-sin.f64 77.0

      \[\leadsto \sin re + 0.5 \cdot \left({im}^{2} \cdot \sin re\right) \]

4. Applied rewrites 77.0%

   \[\leadsto \color{blue}{\sin re + 0.5 \cdot \left({im}^{2} \cdot \sin re\right)} \]

5. Taylor expanded in re around 0

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

7. Applied rewrites 52.6%

   \[\leadsto re + \color{blue}{0.5} \cdot \left({im}^{2} \cdot \sin re\right) \]

8. Taylor expanded in re around 0

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

10. Applied rewrites 48.8%

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

    1. lift-+.f64 N/A

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

    2. +-commutative N/A

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

    3. lift-*.f64 N/A

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

    4. *-commutative N/A

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

    5. lower-fma.f64 48.8

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

    6. lift-pow.f64 N/A

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

    7. unpow2 N/A

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

    8. lower-*.f64 48.8

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

12. Applied rewrites 48.8%

    \[\leadsto \mathsf{fma}\left(\left(im \cdot im\right) \cdot re, \color{blue}{0.5}, re\right) \]
13. Add Preprocessing

Alternative 10: 42.9% accurate, 5.4× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

2. Taylor expanded in im around 0

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

   1. lower-+.f64 N/A

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

   2. lower-sin.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-*.f64 N/A

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

   5. lower-pow.f64 N/A

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

   6. lower-sin.f64 77.0

      \[\leadsto \sin re + 0.5 \cdot \left({im}^{2} \cdot \sin re\right) \]

4. Applied rewrites 77.0%

   \[\leadsto \color{blue}{\sin re + 0.5 \cdot \left({im}^{2} \cdot \sin re\right)} \]

5. Taylor expanded in re around 0

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

7. Applied rewrites 52.6%

   \[\leadsto re + \color{blue}{0.5} \cdot \left({im}^{2} \cdot \sin re\right) \]

8. Taylor expanded in re around 0

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

10. Applied rewrites 48.8%

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

    1. lift-+.f64 N/A

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

    2. +-commutative N/A

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

    3. lift-*.f64 N/A

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

    4. *-commutative N/A

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

    5. lower-fma.f64 48.8

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

    6. lift-pow.f64 N/A

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

    7. unpow2 N/A

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

    8. lower-*.f64 48.8

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

12. Applied rewrites 48.8%

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

    1. lift-*.f64 N/A

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

    2. lift-*.f64 N/A

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

    3. associate-*l* N/A

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

    4. lower-*.f64 N/A

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

    5. lower-*.f64 42.9

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

14. Applied rewrites 42.9%

    \[\leadsto \mathsf{fma}\left(im \cdot \left(im \cdot re\right), 0.5, re\right) \]
15. Add Preprocessing

Alternative 11: 27.3% accurate, 9.3× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot \left(re \cdot 2\right) \end{array} \]

FPCore

(FPCore (re im) :precision binary64 (* 0.5 (* re 2.0)))

C

double code(double re, double im) {
	return 0.5 * (re * 2.0);
}

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 = 0.5d0 * (re * 2.0d0)
end function

Java

public static double code(double re, double im) {
	return 0.5 * (re * 2.0);
}

Python

def code(re, im):
	return 0.5 * (re * 2.0)

Julia

function code(re, im)
	return Float64(0.5 * Float64(re * 2.0))
end

MATLAB

function tmp = code(re, im)
	tmp = 0.5 * (re * 2.0);
end

Wolfram

code[re_, im_] := N[(0.5 * N[(re * 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\left(0.5 \cdot \sin re\right) \cdot \left(e^{0 - im} + e^{im}\right) \]

2. Taylor expanded in re around 0

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

   1. lower-*.f64 N/A

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

   2. lower-*.f64 N/A

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

   3. lower-+.f64 N/A

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

   4. lower-exp.f64 N/A

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

   5. lower-exp.f64 N/A

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

   6. lower-neg.f64 63.6

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

4. Applied rewrites 63.6%

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

5. Taylor expanded in im around 0

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

7. Applied rewrites 27.3%

   \[\leadsto 0.5 \cdot \left(re \cdot 2\right) \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025149 
(FPCore (re im)
  :name "math.sin on complex, real part"
  :precision binary64
  (* (* 0.5 (sin re)) (+ (exp (- 0.0 im)) (exp im))))