PPT-Approximation Algorithms for Capacitated Set Cover

Ravishankar Krishnaswamy joint work with Nikhil Bansal and Barna Saha Approximating Set Cover Given m sets n elements Find minimum cost collection of sets to cover . Your Schedule and Dells invoice to You will prove if You have purchased 1 only Accidental Damage Cover or 2 only Theft Cover or 3 both Accidental Damage and Theft Cover The support You are entitled to will depend on the Cover that You have chosen as . of Edit Distance. Robert Krauthgamer, . Weizmann Institute of Science. SPIRE 2013. TexPoint. fonts used in EMF. . Read the . TexPoint. manual before you delete this box. .: . A. A. A. A. A. A. A. Sometimes we can handle NP problems with polynomial time algorithms which are guaranteed to return a solution within some specific bound of the optimal solution. within a constant . c. . of the optimal. Algorithms. and Networks 2014/2015. Hans L. . Bodlaender. Johan M. M. van Rooij. C-approximation. Optimization problem: output has a value that we want to . maximize . or . minimize. An algorithm A is an . Problem. Yan Lu. 2011-04-26. Klaus Jansen SODA 2009. CPSC669 Term Project—Paper Reading. 1. Problem Definition. 2. Approximation Scheme. 2.1 Instances with similar capacities. 2.2 General cases . Outline. Julia Chuzhoy. Toyota Technological Institute at Chicago. Routing Problems. Input. : Graph G, source-sink pairs (s. 1. ,t. 1. ),…,(. s. k. ,t. k. ).. Goal. : Route as many pairs as possible; minimize edge congestion.. Grigory. . Yaroslavtsev. . Penn State + AT&T Labs - Research (intern). Joint work with . Berman (PSU). , . Bhattacharyya (MIT). , . Makarychev. (IBM). , . Raskhodnikova. (PSU). Directed. Spanner Problem. How accurate is your estimate?. Differential Notation. The Linear Approximation to . y. = . f. (. x. ) is often written using the “differentials” . dx. and . dy. . In this notation, . dx. is used instead of . LECTURE 13. Pagerank. , Absorbing Random Walks. Coverage Problems. PAGERANK. PageRank algorithm. T. he PageRank random walk. Start from a page chosen uniformly at random. With . probability . α. . follow a random outgoing . Stochastic . Optimization. Anupam Gupta. Carnegie Mellon University. IPCO Summer . School. Approximation . Algorithms for. Multi-Stage Stochastic Optimization. {vertex cover, . S. teiner tree, MSTs}. EECT 7327 . Fall 2014. Successive Approximation. (SA) ADC. Successive Approximation ADC. – . 2. –. Data Converters Successive Approximation ADC Professor Y. Chiu. EECT 7327 . Fall 2014. Binary search algorithm → N*. Lecture 17. May 27, . 2014. May 27, 2014. 1. CS38 Lecture 17. May 27, 2014. CS38 Lecture 17. 2. Outline. coping with . intractibility. NP-completeness. special cases. fixed parameter complexity. approximation algorithms. When the best just isn’t possible. Jeff Chastine. Approximation Algorithms. Some NP-Complete problems are too important to ignore. Approaches:. If input small, run it anyway. Consider special cases that may run in polynomial time. How do weights affects approximation algorithms?. As usual there are no rules whatsoever.. However I will provide some interesting examples in which weights make a difference. I will try and say for every problem, why weights make a difference..