ORIE 6338

Heuristic Mixed-Integer Programming

Course Description

Course information provided by the Courses of Study 2022-2023. Courses of Study 2022-2023 is scheduled to publish mid-June.

Discrete Optimization problems are generally formulated through Mixed-Integer Linear and Nonlinear Programming (MIP) and solved, to a large extent, by both commercial and noncommercial MIP solvers. Solving the formulated models to proven optimality is generally not necessary on the application side and often out of reach. However, computing high quality feasible solutions is a strong requirement in applications. The course covers the methodology behind the development of effective heuristic methods within MIP solvers and presents recent hybridization mechanisms based on data and Machine Learning.

When Offered Fall.

Permission Note The course is intended for PhD students. Basic knowledge on linear and discrete optimization is required.

• Understand MIP technology and its implementation in modern MIP solvers to produce fast and reliable high-quality solutions for Discrete Optimization problems.
• Master the use of MIP solvers to solve problems in the applied context.
• Hybridize Discrete Optimization techniques through Machine Learning.
• Pinpoint important research directions in the field.

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