Explore MATLAB source code curated for "牛顿法" with clean implementations, documentation, and examples.
Learn the computational steps of various search algorithms (Steepest Descent, Conjugate Gradient, Newton's, and Quasi-Newton Methods) and compare their advantages and disadvantages with code implementation insights.
This implementation features three MATLAB m-files utilizing Newton's method to solve nonlinear equation systems, where F represents the vector function on the left-hand side of the equations and JF denotes its Jacobian matrix, with the newdim function serving as the main solver routine.
MATLAB programs implementing three fundamental optimization algorithms - Steepest Descent Method, Newton's Method, and Conjugate Gradient Method - with detailed code explanations and practical applications
Implementation of Newton's method and quasi-Newton methods for solving nonlinear equation systems, with experimental validation of effectiveness, featuring code-based algorithm descriptions and convergence analysis.
A MATLAB implementation of Newton's method for nonlinear equations using symbolic operations, accepting parametric algebraic expressions as input without requiring matrix separation.
MATLAB-based power system power flow calculation source code implementing Newton's iterative method with comprehensive electrical component modeling
MATLAB programming implementations and technical insights for numerical optimization algorithms including Newton's Method, Steepest Descent, Quasi-Newton Methods, Golden Section Search, and One-Dimensional Search methods with code-related enhancements.
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.
Mechanical optimization design coursework covering one-dimensional search methods (quadratic interpolation, Newton's method, golden section search), steepest descent method, interior penalty function method, and contour plot visualization
MATLAB program implementation combining Newton's Method and Conjugate Gradient for optimization problems