Approximation error and Fast Fourier transform

Shortcuts: Differences, Similarities, Jaccard Similarity Coefficient, References.

Difference between Approximation error and Fast Fourier transform

Approximation error vs. Fast Fourier transform

The approximation error in some data is the discrepancy between an exact value and some approximation to it. A fast Fourier transform (FFT) is an algorithm that samples a signal over a period of time (or space) and divides it into its frequency components.

Similarities between Approximation error and Fast Fourier transform

Approximation error and Fast Fourier transform have 4 things in common (in Unionpedia): Algorithm, Numerical analysis, Numerical stability, Round-off error.

Algorithm

In mathematics and computer science, an algorithm is an unambiguous specification of how to solve a class of problems.

Algorithm and Approximation error · Algorithm and Fast Fourier transform · See more »

Numerical analysis

Numerical analysis is the study of algorithms that use numerical approximation (as opposed to general symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics).

Approximation error and Numerical analysis · Fast Fourier transform and Numerical analysis · See more »

Numerical stability

In the mathematical subfield of numerical analysis, numerical stability is a generally desirable property of numerical algorithms.

Approximation error and Numerical stability · Fast Fourier transform and Numerical stability · See more »

Round-off error

A round-off error, also called rounding error, is the difference between the calculated approximation of a number and its exact mathematical value due to rounding.

Approximation error and Round-off error · Fast Fourier transform and Round-off error · See more »

The list above answers the following questions

What Approximation error and Fast Fourier transform have in common
What are the similarities between Approximation error and Fast Fourier transform

Approximation error and Fast Fourier transform Comparison

Approximation error has 25 relations, while Fast Fourier transform has 154. As they have in common 4, the Jaccard index is 2.23% = 4 / (25 + 154).

References

This article shows the relationship between Approximation error and Fast Fourier transform. To access each article from which the information was extracted, please visit:

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