Directory of Classes
NOTE: Course information changes frequently. Please re-visit these pages periodically for the most recent and up-to-date information.

Spring 2019 Industrial Engineering and Operations Research E4008 section 001
Computational Discrete Optimization

Call Number 78441
Day & Time
Location
MW 10:10am-11:25am
303 Seeley W. Mudd Building
Points 3
Grading Mode Standard
Approvals Required None
Instructor Yuri Faenza
Type LECTURE
Course Description Discrete optimization is a powerful tool for modelling a wide range of problems in science, engineering, and many other areas of technological everyday life. As the name suggests, it deals with problems where the decisions to be made are discrete, for instance: which cities should be connected with a road, how many airplanes should we build, or to whom should a highly-requested job be given? In this course, you will be introduced to those problems and to different techniques for solving them. We will study these techniques mathematically and test their strengths and limits in practice using state-of-the-art solvers. Problems that we will consider include: transportation problems (TSP, vehicle routing, etc.), matching problems (school assignment, adwords, etc.), discrete problems in machine learning (submodular function maximization, etc.). We will see relevant application of those problems in different areas, including some surprising ones, such as: How can we use graph algorithms to compress images and reconstruct genomas? How can we use the theory of matching and integer programming to facilitate kidney transplants? How can discrete optimization help in feature selection for machine learning problems? Prerequisites. Basic knowledge of linear programming, probability theory, and a pinch of coding experience. Textbooks. Most of the classes will be based on lecture notes and survey or research articles. Softwares and programming languages. Gurobi and Python. Previous experience with them is not required (but willingness to learn them is required). Grading. 30% Assignments. Roughly one every two weeks. 10% Class participation. 60% Final Team Project (2-3 members). Students can choose between a theoretical, practical, or mixed theoret
Web Site Vergil
Department Industrial Engineering and Operations Research
Enrollment 17 students (80 max) as of 12:16AM Tuesday, September 17, 2019
Final Exam Day/Time
W 9:00am-12:00pm
Final Location 227 Seeley W. Mudd Building
Subject Industrial Engineering and Operations Research
Number E4008
Section 001
Division School of Engineering and Applied Science: Graduate
Campus Morningside
Section key 20191IEOR4008E001

Home      About This Directory      Online Bulletins      ColumbiaWeb      SSOL
SIS update 09/17/19 00:16    web update 09/17/19 08:19