Taylor series vs. Fourier series

In summary: The Fourier series does not depend on a specific point and can be applied to any function that is integrable. It converges almost everywhere for continuous functions and for functions in L^p for p > 1. However, there exists a function in L^1 whose Fourier series diverges at every point. In summary, the Fourier series is similar to the Taylor series in that it represents a function as a series of terms. However, while the Taylor series uses polynomial terms, the Fourier series uses trigonometric terms. The convergence of the series is limited to a neighborhood around the point of expansion for the Taylor series, but for the Fourier series, it can converge almost everywhere for continuous functions and for functions in L^p for p >
  • #1
jaejoon89
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Is a Fourier series essentially the analogue to a Taylor series except expressing a function as trigs functions rather than as polynomials? Like the Taylor series, is it ok only for analytic functions, i.e. the remainder term goes to zero as n->infinity?
 
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  • #2
A Taylor series has to be expanded around a specific point, and the coefficients consist of the derivatives of the function at that point: in particular, the function must be infinitely differentiable there. Convergence may be limited to a neighborhood of a certain radius around that point.

The Fourier series for a function is not dependent upon a specific point. A function need not be infinitely differentiable at any point (or even differentiable at all) to have a Fourier series. Every function that is integrable ([itex]L^1[/itex]) has a formal Fourier series, i.e., the coefficients exist.

Mere continuity is sufficient to ensure convergence almost everywhere. More generally, if [itex]f[/itex] is any function in [itex]L^p[/itex] for [itex]p > 1[/itex], then the Fourier series for [itex]f[/itex] converges almost everywhere. (This is a very hard result that wasn't obtained until the late 1960s.) On the other hand, there exists an [itex]L^1[/itex] function whose Fourier series diverges at every point.
 
  • #3
The Taylor series is essentialy the Fourier series on a loop around the point of expansion.
 

FAQ: Taylor series vs. Fourier series

What is the difference between Taylor series and Fourier series?

The main difference between Taylor series and Fourier series is the type of functions they approximate. Taylor series are used to approximate non-periodic functions, while Fourier series are used for periodic functions. This means that Fourier series are more suitable for analyzing phenomena with recurring patterns, such as sound waves or electrical signals.

How are Taylor series and Fourier series calculated?

Taylor series are calculated by expanding a function around a specific point and finding the coefficients of the resulting polynomial. Fourier series, on the other hand, are calculated by decomposing a periodic function into a combination of sine and cosine functions using the Fourier transform. The coefficients of these sine and cosine functions determine the amplitude and frequency of the periodic function.

Which series is more accurate in approximating a function?

It depends on the type of function being approximated. For non-periodic functions, Taylor series tend to be more accurate as they can capture the local behavior of the function around a specific point. However, for periodic functions, Fourier series are more accurate as they can capture the global behavior and periodicity of the function.

Can Taylor series and Fourier series be used interchangeably?

No, Taylor series and Fourier series are fundamentally different methods of approximation and cannot be used interchangeably. Attempting to use Taylor series on a periodic function or Fourier series on a non-periodic function will result in inaccurate approximations.

What applications are Taylor series and Fourier series commonly used for?

Taylor series are often used in physics and engineering to approximate the behavior of physical systems. They are also used in computer graphics and financial modeling. Fourier series are commonly used in signal processing, image compression, and data analysis.

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