Dynamic Programming and Reinforcement Learning ¹

Mathematics - Optimization

Branches of Optimization Research # Convex Optimization # Convex optimization focuses on problems where the objective function and constraints are convex, ensuring a single global optimum. This field is foundational in machine learning, signal processing, and control systems due to its guaranteed convergence and efficient algorithms. Convex Optimization by Boyd and Vandenberghe - PDF Convex Optimization Theory by Dimitri P. Bertsekas - PDF Discrete, Combinatorial, and Integer Optimization # This branch deals with optimization problems involving discrete variables, such as integers or combinatorial structures, often encountered in scheduling, network design, and logistics. Bayesian optimization, a subset, is particularly useful for optimizing expensive black-box functions.