Lectures related to Two-Stage Stochastic Programs

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

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

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

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

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

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.

Explores energy system modeling using optimization techniques and performance indicators.

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

Explores energy systems modeling, optimization, and cost analysis for efficient operations.

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

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

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

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

Discusses gradient descent for optimization with equality constraints and iterative convergence criteria.

Covers the formulation and problem definition in optimization, focusing on defining the objective function, constraints, and feasible set.

Covers the simplex algorithm, from initial tableau to general case.

Covers extreme values, optimization conditions, feasible sets, and partition formation for optimization.

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

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

Explores Quadratic Penalty Methods for optimization with enforced constraints using penalty functions.