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Approximation Algorithms

Approximation Algorithms. Ola Svensson. Course Information . Goal: Learn the techniques used by studying famous applications Graduate Course FDD3390 6 Points One Lecture a Week Examination Topics. Teacher Information. Ola Svensson Email: osven@kth.se Room: 1444

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Approximation Algorithms

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  1. Approximation Algorithms Ola Svensson

  2. Course Information • Goal: • Learn the techniques used by studying famous applications • Graduate Course FDD3390 • 6 Points • One Lecture a Week • Examination • Topics

  3. Teacher Information • Ola Svensson • Email: osven@kth.se • Room: 1444 • PhD on Approximation Algorithms • Second year post-doc • First time responsible for a course: • you have a lot of influence • feedback during course welcome!

  4. Approximation Algorithms • Optimization problems • Not tractable (NP-hard) • Want to find good solution Optimal Tour Ola’s Tour Google Tour Length 1000 1700 3000

  5. r-Approximation Algorithm • A polynomial time algorithm that • returns a feasible solution • with value at most if minimization • with value at least if maximization • For any instance: OPT

  6. Impossible to Analyze? How can we compare our solution to the optimal that we cannot compute? lower bound optimal value lb OPT

  7. Example: TSP (1/4) • Given ncities with distances between them • Find the shortest tour that visits each city at least once d(1,2) 1 2 1 2 1 2 3 4 3 4 3 4 5 5 5

  8. Lower bound (2/4) • Removing one edge gives a spanning tree • Kruskal’s algorithm runs in time O(n2log n) 1 2 1 2 3 4 3 4 5 5

  9. Constructing tour from MST (3/4) 1 • Run DFS on MST • Each vertex is visited at least once • Each edge traversed at most twice • Value of tour at most 3 2 5 4

  10. 2-Approximation Algorithm (4/4) • Find MST as lower bound • Construct tour by running DFS on MST MST 2*MST OPT

  11. Summary • Lower bound optimal value • Constructing feasible solutions that is “close to” bound • Verifying that analysis is tight lb OPT

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