math.exp on complex, imaginary part

Average Error: 0.0 → 0.0

Time: 8.7s

Precision: binary64

Cost: 12992

Math

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

↓

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

FPCore

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

↓

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

C

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

↓

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

Fortran

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

↓

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

Java

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

↓

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

Python

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

↓

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

Julia

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

↓

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

MATLAB

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

↓

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

Wolfram

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

↓

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

Derivation

1. Initial program 0.0

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

2. Final simplification 0.0

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

Alternatives

Alternative 1
Error	0.7
Cost	13892

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

Alternative 2
Error	0.8
Cost	13636

\[\begin{array}{l} \mathbf{if}\;e^{re} \leq 0:\\ \;\;\;\;e^{re} \cdot im\\ \mathbf{else}:\\ \;\;\;\;\sin im \cdot \left(\left(re + 1\right) + re \cdot \left(re \cdot 0.5\right)\right)\\ \end{array} \]

Alternative 3
Error	1.0
Cost	13252

\[\begin{array}{l} \mathbf{if}\;e^{re} \leq 0:\\ \;\;\;\;e^{re} \cdot im\\ \mathbf{else}:\\ \;\;\;\;\sin im \cdot \left(re + 1\right)\\ \end{array} \]

Alternative 4
Error	1.4
Cost	13124

\[\begin{array}{l} \mathbf{if}\;e^{re} \leq 0:\\ \;\;\;\;e^{re} \cdot im\\ \mathbf{else}:\\ \;\;\;\;\sin im\\ \end{array} \]

Alternative 5
Error	14.8
Cost	6728

\[\begin{array}{l} \mathbf{if}\;re \leq -1.82 \cdot 10^{+112}:\\ \;\;\;\;\frac{im}{1 - re}\\ \mathbf{elif}\;re \leq -108:\\ \;\;\;\;\left(1 + \left(im + re \cdot im\right)\right) + -1\\ \mathbf{else}:\\ \;\;\;\;\sin im\\ \end{array} \]

Alternative 6
Error	34.9
Cost	1224

\[\begin{array}{l} t_0 := \frac{im}{1 - re}\\ \mathbf{if}\;re \leq -1.9 \cdot 10^{+112}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;re \leq -6.8:\\ \;\;\;\;\left(1 + \left(im + re \cdot im\right)\right) + -1\\ \mathbf{else}:\\ \;\;\;\;t_0 - \frac{re}{\frac{\frac{1 - re}{im}}{re}}\\ \end{array} \]

Alternative 7
Error	36.1
Cost	1220

\[\begin{array}{l} t_0 := 1 - re \cdot re\\ \mathbf{if}\;re \leq -1.32 \cdot 10^{+154}:\\ \;\;\;\;\frac{im}{1 - re}\\ \mathbf{else}:\\ \;\;\;\;\left(re + 1\right) \cdot \frac{t_0}{\frac{t_0}{im}}\\ \end{array} \]

Alternative 8
Error	34.9
Cost	840

\[\begin{array}{l} \mathbf{if}\;re \leq -1.9 \cdot 10^{+112}:\\ \;\;\;\;\frac{im}{1 - re}\\ \mathbf{elif}\;re \leq -6.8:\\ \;\;\;\;\left(1 + \left(im + re \cdot im\right)\right) + -1\\ \mathbf{else}:\\ \;\;\;\;im \cdot \left(re + 1\right)\\ \end{array} \]

Alternative 9
Error	37.1
Cost	320

\[\frac{im}{1 - re} \]

Alternative 10
Error	42.3
Cost	64

\[im \]

Reproduce

herbie shell --seed 2023016 
(FPCore (re im)
  :name "math.exp on complex, imaginary part"
  :precision binary64
  (* (exp re) (sin im)))