Average Error: 0.0 → 0.0
Time: 3.1s
Precision: binary64
\[e^{re} \cdot \sin im \]
\[e^{re} \cdot \sin im \]
(FPCore (re im) :precision binary64 (* (exp re) (sin im)))
(FPCore (re im) :precision binary64 (* (exp re) (sin im)))
double code(double re, double im) {
	return exp(re) * sin(im);
}
double code(double re, double im) {
	return exp(re) * sin(im);
}
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
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);
}
def code(re, im):
	return math.exp(re) * math.sin(im)
def code(re, im):
	return math.exp(re) * math.sin(im)
function code(re, im)
	return Float64(exp(re) * sin(im))
end
function code(re, im)
	return Float64(exp(re) * sin(im))
end
function tmp = code(re, im)
	tmp = exp(re) * sin(im);
end
function tmp = code(re, im)
	tmp = exp(re) * sin(im);
end
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]
e^{re} \cdot \sin im
e^{re} \cdot \sin im

Error

Bits error versus re

Bits error versus im

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.0

    \[e^{re} \cdot \sin im \]
  2. Applied egg-rr13.4

    \[\leadsto \color{blue}{\sqrt[3]{{\left(e^{re} \cdot \sin im\right)}^{3}}} \]
  3. Applied egg-rr0.0

    \[\leadsto \color{blue}{{\left(e^{re} \cdot \sin im\right)}^{1}} \]
  4. Final simplification0.0

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

Reproduce

herbie shell --seed 2022150 
(FPCore (re im)
  :name "math.exp on complex, imaginary part"
  :precision binary64
  (* (exp re) (sin im)))