Least Squares Polynomial Fitting for Noisy Data

This snippet exemplifies the use of a custom LU decomposition method to produce a k-order polynomial fit to noisy data.
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Updated Wed, 28 Apr 2021 14:24:11 +0000

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This snippet, is in reality the initial prototype I used for building my polynomial least squares class/module in C and Fortran, respectively. As you'll see, it matches perfectly the outputs for Matlab's polyval() and lu() functions. Although, is a very minimalist implementation of the LU decomposition method, it sometimes beats the polyval() function in speed. (Not sure why?!)

To understand how to use it, check out the example : FitPolynomialToNoisyData.m
( In it, I compare this implementation to Matlab's traditional tools/approaches )

Check it out! ;D

Cite As

Manuel A. Diaz (2024). Least Squares Polynomial Fitting for Noisy Data (https://www.mathworks.com/matlabcentral/fileexchange/91205-least-squares-polynomial-fitting-for-noisy-data), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2019b
Compatible with any release
Platform Compatibility
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Version Published Release Notes
1.0.0