Approximation algorithms are polynomial-time algorithms that guarantee to find a feasible solution that is optimal up to a factor of k. For some NP-hard problems, k can be chosen arbitrarily close to 1, for others there is a best possible constant, and for some problems there is no such constant (unless P=NP). We analyze the approximability of various classical NP-hard combinatorial optimization problems, including set covering, knapsack, bin packing, facility location, and satisfiability problems. We will also discuss some new results on the TSP and the Steiner tree problem.
This course will be in English. Most of the course will be based on the following book:
Prerequisites: | Combinatorial Optimization |
(in particular basic knowledge in graphs, linear programming, network flows, matching, and NP-completeness; see, e.g., Chapters 1-15 of my textbook above) | |
Class Hours: | Tuesdays and Thursdays 14:15-15:45 |
Exercise Classes: | 2 hours per week, t.b.a. |
Exams: | Oral exams, t.b.a. |
Professor J. Vygen