Optimization: Mathematical Principles and Algorithms

Related lectures (31)

Page 3 of 4

Covers optimization algorithms, focusing on Proximal Gradient Descent and its variations.

Explores optimization duality, weak and strong duality, practical optimization algorithms, and challenges in nonconvex-concave problems.

Covers methods for solving nonlinear optimization problems, including direct search, Newton-Raphson, and branch and bound.

Discusses algorithms for balancing exploration and exploitation in decision-making processes.

Discusses variance reduction techniques in stochastic simulation, focusing on allocation strategies and replica generation algorithms.

Covers the formulation of optimization programs for minimizing piecewise linear cost functions.

Explores the simplex algorithm and how optimal solutions can be found on vertices of constraint polyhedra.

Explores solving strategies for energy system optimization problems and different types of optimization approaches.

Covers the Simplex algorithm for function minimization with linear constraints.

Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.