Constrained Optimization Algorithms

This collection contains MATLAB programs implementing various constrained optimization algorithms. These implementations are designed to assist users in solving different types of constrained optimization problems. Below is the detailed list of programs with their functionalities and implementation approaches: minRosen.m: Implements Rosen's gradient projection method for solving multivariate constrained optimization problems. This algorithm projects the gradient onto the feasible set and uses projection operations to maintain constraints during optimization iterations. minFactor.m: The multiplier method (also known as method of multipliers) for handling optimization problems with both equality and inequality constraints. This implementation uses Lagrange multipliers and updates the multiplier estimates during the optimization process. minconPS.m: Coordinate rotation method for solving nonlinear constrained problems. This algorithm systematically explores different coordinate directions while maintaining feasibility through constraint handling mechanisms. minPF.m: Exterior penalty function method for problems with equality and inequality constraints. This approach transforms constrained problems into unconstrained ones by adding penalty terms that increase when constraints are violated. minMixFun.m: Mixed penalty function method combining different penalty strategies for optimization problems with both equality and inequality constraints. The implementation balances constraint satisfaction and objective function optimization. minJSMixFun.m: Accelerated mixed penalty function method designed to improve computational efficiency of the standard mixed penalty approach. This version incorporates convergence acceleration techniques for faster solution times. These MATLAB implementations provide practical tools for solving constrained optimization problems efficiently. Each program includes appropriate algorithmic implementations with constraint handling mechanisms, making them suitable for various engineering and scientific applications. The code is structured to allow easy integration into larger optimization frameworks while maintaining computational efficiency.