Lectures related to Cutting-plane method

Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.

Explores energy system modeling using optimization techniques and performance indicators.

Discusses the problem formulation of 2-stage stochastic programs and the Benders decomposition algorithm.

Covers integer programming, convex hulls, Gomory cutting planes, and branch and bound methods.

Explores convexifying nonconvex problems using SDP and SOCP relaxations in the context of optimal power flow.

Explores local sensitivity analysis in linear programming, examining how changes impact optimality and feasibility.

Introduces spanning trees in graphs and the Minimum Spanning Tree problem, exploring efficient algorithms for optimal decision-making.

Explores convexifying nonconvex problems through relaxation techniques, illustrated with total variation reconstruction examples.

Covers the process of formulating integer programs and improving solutions.

Covers the main idea behind the Simplex algorithm and explains the Tableau method for solving linear programming problems.

Explores energy conversion systems optimization through heat recovery and mixed integer linear programming.

Covers exercises on the Simplex Algorithm, optimizing solutions subject to linear constraints.

Introduces optimisation problems, methods, and constraints for solving multi-dimensional problems with and without constraints.

Covers sensitivity analysis in linear programming, focusing on optimal solutions and their sensitivities to changes.

Explores optimal integer programming, Gomory cuts, and an iterative rounding heuristic.

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

Explores the Branch and Bound algorithm in discrete optimization, efficiently finding optimal solutions by calculating lower bounds on subsets.

Explores optimization problems, feasible regions, and objective functions with a focus on Lagrange multipliers and convexity.

Explores optimal decision making through discrete optimization, emphasizing binary variables and practical applications.

Covers the application of Gomory cuts in linear programming problems.