LP Duality: An Economics Perspective

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

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

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

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Covers coin rendering and the limitations of the greedy algorithm in finding optimal solutions.

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

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

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

Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.

Explores variational problems, emphasizing convexity and coercivity conditions in functionals with integral side constraints.