Branch & Bound: Optimization

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Semi-Definite Programming

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

Introduction to Optimization

Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.

Hedging for LPs

Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.

Discrete Optimization: Relaxation

Explores solving discrete optimization problems by relaxing integrality constraints.

Optimization Principles

Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.

Optimization with Constraints: KKT Conditions

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

Linear Programming Basics

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

Linear Programming: Solving LPs

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

Simplex Algorithm: Solution on a Vertex

Explores the simplex algorithm and how optimal solutions can be found on vertices of constraint polyhedra.

Branch and Bound: Heuristic Maximization

Explains the Branch and Bound algorithm for heuristic maximization problems using LP relaxations and pruning techniques.