Lectures related to Lagrange Multipliers

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Covers non-convex optimization techniques and their applications in machine learning.

Explores finding extrema of functions over compact sets and edge parametrization.

Covers the Lipschitz gradient theorem and its applications in function optimization.

Explores the Lagrange Multipliers Theorem, covering extrema conditions and geometric interpretations.

Covers the Simplex algorithm for function minimization with linear constraints.

Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.

Explores the terminology of extrema lies and optimization problems with constraints.

Explores optimization methods in engineering, covering decision variables, constraints, and various solving techniques.