Optimization Duality: Theory and Algorithms

Related lectures (31)

Page 1 of 4

Covers weak and strong duality in optimization problems, focusing on Lagrange multipliers and KKT conditions.

Covers the proof of strong duality in optimization problems and provides examples of Rayleigh quotient optimization.

Covers the concept of linear optimization and the duality relationship between primal and dual problems.

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

Explores primal-dual optimization, conjugation of functions, strong duality, and quadratic penalty methods in data mathematics.

Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.

Explores primal-dual optimization, minimax problems, and gradient descent-ascent methods for optimization algorithms.

Explores duality in network optimization problems with examples and step-by-step explanations.

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Explores gradient descent with memory, momentum methods, conjugate gradients, and nonlinear CG on manifolds.