Northwestern University 1 credit Computer Science
This course studies approximation algorithms β algorithms that are used for solving hard optimization problems. Such algorithms find approximate (slightly suboptimal) solutions to optimization problems in polynomial time. Unlike heuristics, approximation algorithms have provable performance guarantees: they have bounds on the running time and on the quality of the obtained solutions. In this course, we will introduce various algorithmic techniques used for solving optimization problems such as greedy algorithms, local search, dynamic programming, linear programming (LP), semidefinite programming (SDP), LP duality, randomized rounding, and primal-dual analysis. The course assumes background in basic probability theory and discrete mathematics. Key mathematical concepts will be reviewed before they are used. Prerequisistes: COMP_SCI 212 and COMP_SCI 336 (or similar courses) or CS MS or CS PhDs.
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