Lecture

Optimization in Networks

Related lectures (31)

Explains the process of finding an initial Basic Feasible Solution for linear optimization problems using the Simplex Algorithm.

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

Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.

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

Explores the formulation of the auxiliary problem in linear optimization and its role in optimal decision-making.

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

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.

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

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

Covers semi-definite programming and optimization over positive semidefinite cones.

Explores the Legendre transform, duality in convex analysis, and optimization problems.

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

Explores self-dual cones in convex optimization and their applications in various optimization problems.

Explores the concept of duality in linear programming, discussing the relationship between primal and dual problems.

Covers fundamental concepts of optimization and operations research, exploring real-world examples and key topics over a semester.

Explores duality in linear programming, strong duality, complementary slackness, and the economic interpretation of dual variables as prices.

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

Delves into duality in optimization, weak duality, cost certificates, and transforming non-linear programs into linear ones.

Covers linear programming duality and complementary slackness condition.