Explore MATLAB source code curated for "非线性最小二乘法" with clean implementations, documentation, and examples.
The Levenberg-Marquardt algorithm is a highly effective nonlinear least squares method, particularly useful for bundle adjustment in photogrammetry and computer vision applications, with robust numerical implementation characteristics.
This guide covers MATLAB implementations for unconstrained optimization methods including Steepest Descent, Newton's Method, Conjugate Gradient, Variable Metric Methods (DFP and BFGS), and Nonlinear Least Squares. For constrained optimization, it explores Exterior Penalty Function and Generalized Multiplier Methods. The documentation includes detailed algorithm explanations, MATLAB code examples, and practical problem analysis with optimization techniques.
This source code package constitutes my major assignment for the Optimization Theory course, featuring self-implemented versions of the following prevalent optimization algorithms: Steepest Descent Method, Newton's Method, Nonlinear Least Squares Method, and DFP (Davidon-Fletcher-Powell) Method. The implementation includes two test functions, fun1 and fun2, designed to validate algorithm performance and convergence behavior across different optimization landscapes.
MATLAB Two-Dimensional Fitting Function Expression Applied to lsqcurvefit(@myfun, a0, data, zdata) (Nonlinear Least Squares Fitting)