Combinatorial optimization

Lectures: 2 sessions / week, 1.5 hours / session

18.06 Linear Algebra or 18.700 Linear Algebra.

The course will present a thorough introduction to the fundamental algorithmic techniques of Discrete Mathematics - Linear and Convex Programming, Flow & Matching Theory, Randomization, and Approximation. We will tackle a variety of optimization problems by applying these techniques to find efficient algorithms.

Topics include

In addition to 3 hours of lectures each week, students will have regular assignments, and two in-class exams. There will also be a course project which can be either theoretical (e.g. write a report, solve an open problem) or practical (e.g. evaluate an algorithm).

For the course project you will:

Students can work individually or in pairs.
Projects should be set up by the day after session #5.

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