Dual Transformation: Relaxed Formulation

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

Covers the modelling and optimization of energy systems, focusing on solving optimization problems with constraints and variables.

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

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

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

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

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

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

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

Explores subgradients in convex functions, emphasizing non-differentiable yet convex scenarios and properties of subdifferentials.