Convex Relaxation in Optimization

Related lectures (31)

Page 3 of 4

Covers deriving basic linear program representation, finding solutions, and exploring optimality.

Explains network flows, LP formulations, simplex method, duality, and practical applications.

Covers the formulation of optimization programs for minimizing piecewise linear cost functions.

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

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

Explores optimization problems in standard form, convex optimization, and optimality criteria.

Explores convex optimization problems, optimality criteria, equivalent problems, and practical applications in transportation and robotics.

Explores Two-Stage Stochastic Programs, problem reformulation, Benders decomposition, extreme points, and sensitivity analysis.

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

Explores energy system modeling, optimization, infrastructure development, and technology assessment for long-term energy planning.