Carnegie Mellon University

Linear Programming

Course Number: 47834

Linear programming lies at the basis of modern optimization theory. This course focuses primarily on linear programming theory and algorithms, leaving beyond the scope of its practical applications. The main topics to be covered include modeling examples and expressive power of linear programs, polyhedral sets and their geometry, theory of systems of linear inequalities and duality, classical linear optimization algorithms (simplex and network simplex), and decomposition approaches for large-scale optimization. If time permits polynomial time solvability of linear programs, extensions to conic optimization problems, conic duality, and an introduction to interior point methods are topics of interest in the given order.

Degree: PhD
Concentration: Operations Research
Academic Year: 2020-2021
Semester(s): Mini 1
Required/Elective: Elective
Units: 6


Lecture: 100min/wk and Recitation: 50min/wk