Interior-point method

Related lectures (31)

Page 3 of 4

Explores linear programming duality, covering constraints, variables, solutions, and the relationship between primal and dual LP.

Explores primal-dual optimization methods, algorithms, convergence, and applications in nonconvex optimization and image deconvolution.

Explores primal-dual optimization with a focus on Lagrangian methods and their convergence, drawbacks, and enhancements.

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Covers the basics of the Simplex method and its application in solving optimization problems.

Covers the process of solving Linear Programs (LPs) using the simplex method.

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.

Explores self-concordant functions, their properties, definitions, and optimization in mathematical problems.

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

Explains the difference between infimum and optimum in optimization problems and how to find the optimal solution.