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Hello,

I have used taylor polynomials to approximate floating-point values before. I am wondering whether there is a way to still apply them to functions with complex arguments, and if there is a way what its constraints would be. For example, to calculate the sine of a floating-point number to a specific precision I keep adding terms until the error value is less than the precision. However, I noticed (by testing it with the taylor expansion of ln(x) ) that this gives the wrong value when I use complex numbers (with the standard operators appropriately defined). For example:

ln(0.5)~=-(0.5+(0.5^2)/2+(0.5^3)/3)     --- gives -0.66, real is -0.69                Close enough
ln(0.5i)~=-(0.5i+(0.5i^2)/2+(0.5i^3)/3) --- gives -0.25+0.33i, real is -0.69+1.57i    WHAAT?!

How can I approximate imaginary functions to within an error range? Basically I need a method such that if I give you any given function's definition, a complex number, and a rational number (floating-point value) you can give me the value of that given function, at that given complex number, accurate in both real and imaginary parts, to within +- the given rational number. Clearly standard taylor polynomials dont always work, so what does?

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Last Post by ddanbe
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Did you try to use more terms?
My first guess is that the series of ln(0.5i) converges much slower than the series of ln(0.5).

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