Linear Optimization: Active Constraints

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Discrete Optimization: Relaxation

Explores solving discrete optimization problems by relaxing integrality constraints.

Linear Constraints: Elimination of Variables

Explains linear constraints and variable elimination in optimization problems, illustrating the process step by step.

ALM with Inequalities: Next Steps in Optimization

Explores the Augmented Lagrangian Method with equality and inequality constraints in optimization, emphasizing the importance of slack variables.

Linear Constraints: Polyhedron

Explains linear constraints and the concept of a polyhedron in optimization problems.

Lagrangian Duality: Convex Optimization

Explores Lagrangian duality in convex optimization, transforming problems into min-max formulations and discussing the significance of dual solutions.

Networks: Formulation

Explains the formulation of network optimization problems to minimize transportation costs.

Support Vector Machines: SVM Basics

Covers the basics of Support Vector Machines, focusing on hard-margin and soft-margin formulations.

Optimal Control: KKT Conditions

Explores optimal control and KKT conditions for non-linear optimization with constraints.

Optimal Decision Making: Exercises and Applications

Covers exercises on optimal decision making, including minimizing costs and optimizing transportation networks.

Convex Optimization: Exercises

Covers exercises on convex optimization, focusing on formulating and solving optimization problems using YALMIP and solvers like GUROBI and MOSEK.