# Mathematical optimization

# A new population-based metaheuristic algorithm for continuous numerical optimization problems. in matlab

# Multi objective optimization differential evolution algorithm in matlab

# Optimization rastrigin function by differential evolution algorithm in matlab

# Function optimization using differential evolution in matlab

# Cohss solver for complex symmetrix linear system in matlab

# Optimization tutorial in matlab

# A random search method[1] for the optimization of a function of n variables. in matlab

# Designs analog all-pass phase shift networks. in matlab

# Quadratic programming by wolf's method in matlab

# Image restoration via topological derivative in matlab

# Dynamic Programming Matlab Code

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. It is applicable to problems exhibiting the properties of overlapping subproblems[1] and optimal substructure (described below). When applicable, the method takes far less time than naive methods that don't take advantage of the subproblem overlap (like depth-first search).

The following matlab project contains the source code and matlab examples used for dynamic programming.

# Solves the 0-1 knapsack problem using preprocessing and dynamic programming. in matlab

# Quadratic programming solution to dynamic economic dispatch in matlab

# Stochastic dynamic programming for water reservoir in matlab

# Cuckoo search (cs) algorithm in matlab

# Simulated Annealing Matlab Code

**Simulated annealing (SA)** is a generic probabilistic metaheuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities).

# Stochastic search and optimization in matlab

# Genetic algorithm code with/without islands and simulated annealing in matlab

# Simulated annealing optimization m-file in matlab

# Simulated annealing for constrained optimization in matlab

# Simulated annealing optimization in matlab

# Antshrink ant colony optimization for image shrinkage in matlab

# Image edge detection using ant colony optimization in matlab

# Newton raphson optimization procedure in matlab

# Particle Swarm Optimization Matlab Code

Particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity.