Constrained Nonlinear Programming Algorithm Library

We present a MATLAB-based constrained nonlinear programming algorithm library designed for both educational purposes and practical research applications. This library provides detailed implementations of key optimization algorithms including interior-point methods, sequential quadratic programming (SQP), and active-set strategies, featuring functions like fmincon for handling nonlinear constraints. Through hands-on examples demonstrating constraint handling techniques and gradient-based optimization approaches, users can deepen their understanding of algorithm principles and apply them effectively to real-world problems. The library includes extensive case studies covering engineering design, resource allocation, and parameter estimation scenarios, complete with MATLAB code templates showcasing proper constraint formulation, Jacobian matrix implementation, and convergence analysis. By exploring these practical applications with fully documented source code, users can master implementation techniques and optimization best practices. If you're interested in constrained nonlinear programming, this library offers valuable resources to enhance your learning and research endeavors.