MATLAB Implementation of Moving Least Squares Method

This documentation presents a comprehensive MATLAB implementation of the Moving Least Squares (MLS) method, specifically designed for surface and curve fitting applications. Our program employs sophisticated algorithms that allow users to perform weighted least squares fitting with local approximation capabilities, enabling more accurate modeling of complex datasets. The implementation features adaptive weighting functions and neighborhood selection mechanisms that dynamically adjust based on data point distribution. Through our optimized code structure, users can efficiently analyze data patterns, predict future trends, and make informed decisions. The program includes robust error handling and parameter optimization routines to ensure reliable performance across various dataset sizes. We provide detailed explanations of key functions including the weight function computation, local matrix operations, and approximation algorithms. Additional guidance covers parameter selection strategies and performance optimization techniques. Whether you are a researcher, data analyst, or professional requiring advanced data fitting capabilities, our MATLAB implementation offers powerful tools to streamline your workflow and enhance analytical accuracy.