Explore MATLAB source code curated for "共轭梯度法" 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.
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 SSOR preconditioned conjugate gradient method for solving Ax=b % Input parameters: % A - positive definite matrix [n*n] % b - right-hand side vector % omega - SSOR preconditioning parameter (range: 0--2) % Times - maximum iteration count % errtol - error tolerance for termination condition % % Output parameters: % NewX - approximate solution x for equation Ax=b % avgerr - current average absolute error during computation % % This implementation efficiently handles large sparse matrices using symmetric successive over-relaxation preconditioning to accelerate convergence.
Self-developed MATLAB optimization algorithms including Golden Section (0.618) method, Gradient Descent, Conjugate Gradient, and Penalty Function approaches with detailed implementation code and algorithmic explanations.
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.
Conjugate Gradient Method Algorithm - This is a verified implementation of the conjugate gradient algorithm where users need to modify certain parameters when applying it to other domains. The implementation includes key components such as initialization parameters, iterative solving process, and convergence criteria.
MATLAB program implementation combining Newton's Method and Conjugate Gradient for optimization problems
MATLAB implementation of conjugate gradient method for nonlinear optimization with algorithm efficiency analysis
Multi-parameter optimization via conjugate gradient method with adjustable precision control for parameter estimation
Key optimization algorithms including Conjugate Gradient Method, Preconditioned Conjugate Gradient Method, Direction Acceleration Method, Step Size Acceleration, and Variable Metric Method, with code implementation insights