math.sin on complex, real part

Percentage Accurate: 100.0% → 100.0%

Time: 6.1s

Alternatives: 12

Speedup: 1.0×

• 49.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

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

real(8) function code(re, im)
    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

real(8) function code(re, im)
    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, 0.8× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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1. Add Preprocessing

Alternative 2: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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1. Add Preprocessing

Alternative 3: 71.8% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;im \leq 4.4:\\ \;\;\;\;\sin re\\ \mathbf{elif}\;im \leq 1.32 \cdot 10^{+154}:\\ \;\;\;\;re \cdot \left(2 + 0.5 \cdot e^{im}\right)\\ \mathbf{else}:\\ \;\;\;\;{im}^{2} \cdot \left(\sin re \cdot 0.5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= im 4.4)
   (sin re)
   (if (<= im 1.32e+154)
     (* re (+ 2.0 (* 0.5 (exp im))))
     (* (pow im 2.0) (* (sin re) 0.5)))))

C

double code(double re, double im) {
	double tmp;
	if (im <= 4.4) {
		tmp = sin(re);
	} else if (im <= 1.32e+154) {
		tmp = re * (2.0 + (0.5 * exp(im)));
	} else {
		tmp = pow(im, 2.0) * (sin(re) * 0.5);
	}
	return tmp;
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if (im <= 4.4d0) then
        tmp = sin(re)
    else if (im <= 1.32d+154) then
        tmp = re * (2.0d0 + (0.5d0 * exp(im)))
    else
        tmp = (im ** 2.0d0) * (sin(re) * 0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if (im <= 4.4) {
		tmp = Math.sin(re);
	} else if (im <= 1.32e+154) {
		tmp = re * (2.0 + (0.5 * Math.exp(im)));
	} else {
		tmp = Math.pow(im, 2.0) * (Math.sin(re) * 0.5);
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if im <= 4.4:
		tmp = math.sin(re)
	elif im <= 1.32e+154:
		tmp = re * (2.0 + (0.5 * math.exp(im)))
	else:
		tmp = math.pow(im, 2.0) * (math.sin(re) * 0.5)
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (im <= 4.4)
		tmp = sin(re);
	elseif (im <= 1.32e+154)
		tmp = Float64(re * Float64(2.0 + Float64(0.5 * exp(im))));
	else
		tmp = Float64((im ^ 2.0) * Float64(sin(re) * 0.5));
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if (im <= 4.4)
		tmp = sin(re);
	elseif (im <= 1.32e+154)
		tmp = re * (2.0 + (0.5 * exp(im)));
	else
		tmp = (im ^ 2.0) * (sin(re) * 0.5);
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[im, 4.4], N[Sin[re], $MachinePrecision], If[LessEqual[im, 1.32e+154], N[(re * N[(2.0 + N[(0.5 * N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Power[im, 2.0], $MachinePrecision] * N[(N[Sin[re], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision]]]

Derivation

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1. Add Preprocessing

Alternative 4: 87.9% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;im \leq 2.3:\\ \;\;\;\;\sin re \cdot \left(0.5 \cdot {im}^{2} + 1\right)\\ \mathbf{else}:\\ \;\;\;\;\sin re \cdot \left(2 + 0.5 \cdot e^{im}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= im 2.3)
   (* (sin re) (+ (* 0.5 (pow im 2.0)) 1.0))
   (* (sin re) (+ 2.0 (* 0.5 (exp im))))))

C

double code(double re, double im) {
	double tmp;
	if (im <= 2.3) {
		tmp = sin(re) * ((0.5 * pow(im, 2.0)) + 1.0);
	} else {
		tmp = sin(re) * (2.0 + (0.5 * exp(im)));
	}
	return tmp;
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if (im <= 2.3d0) then
        tmp = sin(re) * ((0.5d0 * (im ** 2.0d0)) + 1.0d0)
    else
        tmp = sin(re) * (2.0d0 + (0.5d0 * exp(im)))
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if (im <= 2.3) {
		tmp = Math.sin(re) * ((0.5 * Math.pow(im, 2.0)) + 1.0);
	} else {
		tmp = Math.sin(re) * (2.0 + (0.5 * Math.exp(im)));
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if im <= 2.3:
		tmp = math.sin(re) * ((0.5 * math.pow(im, 2.0)) + 1.0)
	else:
		tmp = math.sin(re) * (2.0 + (0.5 * math.exp(im)))
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (im <= 2.3)
		tmp = Float64(sin(re) * Float64(Float64(0.5 * (im ^ 2.0)) + 1.0));
	else
		tmp = Float64(sin(re) * Float64(2.0 + Float64(0.5 * exp(im))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if (im <= 2.3)
		tmp = sin(re) * ((0.5 * (im ^ 2.0)) + 1.0);
	else
		tmp = sin(re) * (2.0 + (0.5 * exp(im)));
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[im, 2.3], N[(N[Sin[re], $MachinePrecision] * N[(N[(0.5 * N[Power[im, 2.0], $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[Sin[re], $MachinePrecision] * N[(2.0 + N[(0.5 * N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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1. Add Preprocessing

Alternative 5: 75.2% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;im \leq 1.3:\\ \;\;\;\;\sin re\\ \mathbf{else}:\\ \;\;\;\;\sin re \cdot \left(2 + 0.5 \cdot e^{im}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= im 1.3) (sin re) (* (sin re) (+ 2.0 (* 0.5 (exp im))))))

C

double code(double re, double im) {
	double tmp;
	if (im <= 1.3) {
		tmp = sin(re);
	} else {
		tmp = sin(re) * (2.0 + (0.5 * exp(im)));
	}
	return tmp;
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if (im <= 1.3d0) then
        tmp = sin(re)
    else
        tmp = sin(re) * (2.0d0 + (0.5d0 * exp(im)))
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if (im <= 1.3) {
		tmp = Math.sin(re);
	} else {
		tmp = Math.sin(re) * (2.0 + (0.5 * Math.exp(im)));
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if im <= 1.3:
		tmp = math.sin(re)
	else:
		tmp = math.sin(re) * (2.0 + (0.5 * math.exp(im)))
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (im <= 1.3)
		tmp = sin(re);
	else
		tmp = Float64(sin(re) * Float64(2.0 + Float64(0.5 * exp(im))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if (im <= 1.3)
		tmp = sin(re);
	else
		tmp = sin(re) * (2.0 + (0.5 * exp(im)));
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[im, 1.3], N[Sin[re], $MachinePrecision], N[(N[Sin[re], $MachinePrecision] * N[(2.0 + N[(0.5 * N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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1. Add Preprocessing

Alternative 6: 71.8% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;im \leq 5.6:\\ \;\;\;\;\sin re\\ \mathbf{elif}\;im \leq 2.65 \cdot 10^{+154}:\\ \;\;\;\;re \cdot \left(2 + 0.5 \cdot e^{im}\right)\\ \mathbf{else}:\\ \;\;\;\;\sin re \cdot \left(2.5 + im \cdot \left(0.5 + im \cdot 0.25\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= im 5.6)
   (sin re)
   (if (<= im 2.65e+154)
     (* re (+ 2.0 (* 0.5 (exp im))))
     (* (sin re) (+ 2.5 (* im (+ 0.5 (* im 0.25))))))))

C

double code(double re, double im) {
	double tmp;
	if (im <= 5.6) {
		tmp = sin(re);
	} else if (im <= 2.65e+154) {
		tmp = re * (2.0 + (0.5 * exp(im)));
	} else {
		tmp = sin(re) * (2.5 + (im * (0.5 + (im * 0.25))));
	}
	return tmp;
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if (im <= 5.6d0) then
        tmp = sin(re)
    else if (im <= 2.65d+154) then
        tmp = re * (2.0d0 + (0.5d0 * exp(im)))
    else
        tmp = sin(re) * (2.5d0 + (im * (0.5d0 + (im * 0.25d0))))
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if (im <= 5.6) {
		tmp = Math.sin(re);
	} else if (im <= 2.65e+154) {
		tmp = re * (2.0 + (0.5 * Math.exp(im)));
	} else {
		tmp = Math.sin(re) * (2.5 + (im * (0.5 + (im * 0.25))));
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if im <= 5.6:
		tmp = math.sin(re)
	elif im <= 2.65e+154:
		tmp = re * (2.0 + (0.5 * math.exp(im)))
	else:
		tmp = math.sin(re) * (2.5 + (im * (0.5 + (im * 0.25))))
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (im <= 5.6)
		tmp = sin(re);
	elseif (im <= 2.65e+154)
		tmp = Float64(re * Float64(2.0 + Float64(0.5 * exp(im))));
	else
		tmp = Float64(sin(re) * Float64(2.5 + Float64(im * Float64(0.5 + Float64(im * 0.25)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if (im <= 5.6)
		tmp = sin(re);
	elseif (im <= 2.65e+154)
		tmp = re * (2.0 + (0.5 * exp(im)));
	else
		tmp = sin(re) * (2.5 + (im * (0.5 + (im * 0.25))));
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[im, 5.6], N[Sin[re], $MachinePrecision], If[LessEqual[im, 2.65e+154], N[(re * N[(2.0 + N[(0.5 * N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Sin[re], $MachinePrecision] * N[(2.5 + N[(im * N[(0.5 + N[(im * 0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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1. Add Preprocessing

Alternative 7: 68.4% accurate, 2.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;im \leq 5.6:\\ \;\;\;\;\sin re\\ \mathbf{else}:\\ \;\;\;\;re \cdot \left(2 + 0.5 \cdot e^{im}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= im 5.6) (sin re) (* re (+ 2.0 (* 0.5 (exp im))))))

C

double code(double re, double im) {
	double tmp;
	if (im <= 5.6) {
		tmp = sin(re);
	} else {
		tmp = re * (2.0 + (0.5 * exp(im)));
	}
	return tmp;
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if (im <= 5.6d0) then
        tmp = sin(re)
    else
        tmp = re * (2.0d0 + (0.5d0 * exp(im)))
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if (im <= 5.6) {
		tmp = Math.sin(re);
	} else {
		tmp = re * (2.0 + (0.5 * Math.exp(im)));
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if im <= 5.6:
		tmp = math.sin(re)
	else:
		tmp = re * (2.0 + (0.5 * math.exp(im)))
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (im <= 5.6)
		tmp = sin(re);
	else
		tmp = Float64(re * Float64(2.0 + Float64(0.5 * exp(im))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if (im <= 5.6)
		tmp = sin(re);
	else
		tmp = re * (2.0 + (0.5 * exp(im)));
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[im, 5.6], N[Sin[re], $MachinePrecision], N[(re * N[(2.0 + N[(0.5 * N[Exp[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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1. Add Preprocessing

Alternative 8: 61.1% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;im \leq 6 \cdot 10^{+32}:\\ \;\;\;\;\sin re\\ \mathbf{else}:\\ \;\;\;\;{im}^{2} \cdot \left(re \cdot 0.5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= im 6e+32) (sin re) (* (pow im 2.0) (* re 0.5))))

C

double code(double re, double im) {
	double tmp;
	if (im <= 6e+32) {
		tmp = sin(re);
	} else {
		tmp = pow(im, 2.0) * (re * 0.5);
	}
	return tmp;
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if (im <= 6d+32) then
        tmp = sin(re)
    else
        tmp = (im ** 2.0d0) * (re * 0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if (im <= 6e+32) {
		tmp = Math.sin(re);
	} else {
		tmp = Math.pow(im, 2.0) * (re * 0.5);
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if im <= 6e+32:
		tmp = math.sin(re)
	else:
		tmp = math.pow(im, 2.0) * (re * 0.5)
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (im <= 6e+32)
		tmp = sin(re);
	else
		tmp = Float64((im ^ 2.0) * Float64(re * 0.5));
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if (im <= 6e+32)
		tmp = sin(re);
	else
		tmp = (im ^ 2.0) * (re * 0.5);
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[im, 6e+32], N[Sin[re], $MachinePrecision], N[(N[Power[im, 2.0], $MachinePrecision] * N[(re * 0.5), $MachinePrecision]), $MachinePrecision]]

Derivation

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1. Add Preprocessing

Alternative 9: 53.8% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;im \leq 1.65 \cdot 10^{+33}:\\ \;\;\;\;\sin re\\ \mathbf{else}:\\ \;\;\;\;re \cdot \left(2.5 + 0.5 \cdot im\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= im 1.65e+33) (sin re) (* re (+ 2.5 (* 0.5 im)))))

C

double code(double re, double im) {
	double tmp;
	if (im <= 1.65e+33) {
		tmp = sin(re);
	} else {
		tmp = re * (2.5 + (0.5 * im));
	}
	return tmp;
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if (im <= 1.65d+33) then
        tmp = sin(re)
    else
        tmp = re * (2.5d0 + (0.5d0 * im))
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if (im <= 1.65e+33) {
		tmp = Math.sin(re);
	} else {
		tmp = re * (2.5 + (0.5 * im));
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if im <= 1.65e+33:
		tmp = math.sin(re)
	else:
		tmp = re * (2.5 + (0.5 * im))
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (im <= 1.65e+33)
		tmp = sin(re);
	else
		tmp = Float64(re * Float64(2.5 + Float64(0.5 * im)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if (im <= 1.65e+33)
		tmp = sin(re);
	else
		tmp = re * (2.5 + (0.5 * im));
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[im, 1.65e+33], N[Sin[re], $MachinePrecision], N[(re * N[(2.5 + N[(0.5 * im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 10: 30.5% accurate, 34.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;im \leq 410:\\ \;\;\;\;re\\ \mathbf{else}:\\ \;\;\;\;re \cdot \left(2.5 + 0.5 \cdot im\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (if (<= im 410.0) re (* re (+ 2.5 (* 0.5 im)))))

C

double code(double re, double im) {
	double tmp;
	if (im <= 410.0) {
		tmp = re;
	} else {
		tmp = re * (2.5 + (0.5 * im));
	}
	return tmp;
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8) :: tmp
    if (im <= 410.0d0) then
        tmp = re
    else
        tmp = re * (2.5d0 + (0.5d0 * im))
    end if
    code = tmp
end function

Java

public static double code(double re, double im) {
	double tmp;
	if (im <= 410.0) {
		tmp = re;
	} else {
		tmp = re * (2.5 + (0.5 * im));
	}
	return tmp;
}

Python

def code(re, im):
	tmp = 0
	if im <= 410.0:
		tmp = re
	else:
		tmp = re * (2.5 + (0.5 * im))
	return tmp

Julia

function code(re, im)
	tmp = 0.0
	if (im <= 410.0)
		tmp = re;
	else
		tmp = Float64(re * Float64(2.5 + Float64(0.5 * im)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(re, im)
	tmp = 0.0;
	if (im <= 410.0)
		tmp = re;
	else
		tmp = re * (2.5 + (0.5 * im));
	end
	tmp_2 = tmp;
end

Wolfram

code[re_, im_] := If[LessEqual[im, 410.0], re, N[(re * N[(2.5 + N[(0.5 * im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 11: 4.7% accurate, 309.0× speedup

Math

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

FPCore

(FPCore (re im) :precision binary64 1.0)

C

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

Fortran

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

Java

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

Python

def code(re, im):
	return 1.0

Julia

function code(re, im)
	return 1.0
end

MATLAB

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

Wolfram

code[re_, im_] := 1.0

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 12: 27.4% accurate, 309.0× speedup

Math

\[\begin{array}{l} \\ re \end{array} \]

FPCore

(FPCore (re im) :precision binary64 re)

C

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

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = re
end function

Java

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

Python

def code(re, im):
	return re

Julia

function code(re, im)
	return re
end

MATLAB

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

Wolfram

code[re_, im_] := re

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Reproduce

herbie shell --seed 2023343 
(FPCore (re im)
  :name "math.sin on complex, real part"
  :precision binary64
  (* (* 0.5 (sin re)) (+ (exp (- 0.0 im)) (exp im))))