Explore MATLAB source code curated for "最小值" with clean implementations, documentation, and examples.
The Steepest Descent Method is an optimization technique that searches for the minimum of an N-dimensional objective function by following the negative gradient direction. This program implements the method to solve unconstrained optimization problems with step size control and convergence criteria.
Divide the original image into e x e pixel sub-blocks, identify maximum and minimum values in each block, calculate and quantize their differences, then compute fractal values using r x r sub-blocks with texture analysis implementation
A function optimization implementation using genetic algorithms to find minimum values, developed with MATLAB programming
MATLAB implementation of simulated annealing algorithm for computing the minimum value of a function, featuring parameter configuration and optimization process explanation
Comprehension of genetic algorithm principles and their application in function minimization using optimization techniques such as crossover and mutation operations with code implementation insights
MATLAB implementation of standard genetic algorithm to find the minimum value of function f(x)=x²-22x+30 with x∈[0,31], including complete source code and optimization results
MATLAB Genetic Algorithm Toolbox Functions and Case Study: Minimizing f(x1,x2)=-20*exp(-0.2*sqrt(0.5*(x1^2+x2^2)))-exp(0.5*(cos(2πx1)+cos(2πx2)))+22.71282 within domain -5≤Xi≤5 (i=1,2) using optimization algorithms with code implementation details.
GUI-based simulated annealing algorithm demonstration for finding global maximum and minimum values, featuring interactive parameter controls and real-time optimization visualization
Fully original MATLAB implementation of Quantum Immune Genetic Algorithm designed for function minimization. This executable program (resubmitted with corrected format) features quantum-inspired optimization combined with immune system mechanisms and genetic operations.
GA (Simple Genetic Algorithm) is a powerful intelligent multivariable optimization algorithm that mimics population reproduction dynamics for optimization. This SGA implementation supports variable optimization for both minimization and maximization problems (converted to minimization via function reciprocal), featuring floating-point encoding, Gray encoding, and binary encoding; selection methods include roulette wheel selection and tournament selection; crossover operations support single-point, uniform, and arithmetic crossover; mutation operations include single-point and floating-point mutation.