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 the TSP, set covering, knapsack, bin packing, facility location, and satisfiability problems.
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 |
Zoom link: | See e-Campus. |
Exercise Classes: | Jannis Blauth, Mondays 16:15-17:45, see eCampus |
Exams: | Oral exams have been scheduled on July 29/30 and September 30. |
Professor J. Vygen