EECS 495: APPROXIMATION ALGORITHMS
Spring 2017, MW 11:00-12:20, Room Tech F281
This course studies approximation algorithms – 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.
• “Approximation Algorithms” by Vijay Vazirani.
• “The Design of Approximation Algorithms” by David Williamson and David Shmoys. A copy of this book is available online (http://www.designofapproxalgs.com/book.pdf).
1. Monday, April 10: Problem Set #1 due Wednesday, April 19, http://konstantin.makarychev.net/teaching/approx_alg/hw1.pdf
There will be 4 homework and no exam.