PPT-An O(1) Approximation Algorithm for Generalized Min-Sum Set

Ravishankar Krishnaswamy Carnegie Mellon University joint work with Nikhil Bansal IBM and Anupam Gupta CMU elgooG A Hypothetical Search Engine Given a search query Q Identify relevant webpages and order them. lastnameceafr Michel Kieffer L2S CNRS Sup57577lec Univ ParisSud 91192 GifsurYvette kiefferlsssupelecfr ABSTRACT Computing a tight inner approximation of the range of a function over some set is notoriously di64259cult way beyond obtaining outer ap Matt Weinberg. MIT .  Princeton  MSR. References: . . http. ://arxiv.org/abs/. 1305.4002. http. ://arxiv.org/abs/. 1405.5940. http. ://arxiv.org/abs/. 1305.4000. Recap. Costis. ’ Talk: . Optimal multi-dimensional mechanism: additive bidders, no constraints. Princeton University. Game Theory Meets. Compressed Sensing. Based on joint work with:. Volkan. Cevher. Robert. Calderbank. Rob. Schapire. Compressed Sensing. Main tasks:. Design a . sensing . matrix. 1. Tsvi. . Kopelowitz. Knapsack. Given: a set S of n objects with weights and values, and a weight bound:. w. 1. , w. 2. , …, w. n. , B (weights, weight bound).. v. 1. , v. 2. , …, v. n. (values - profit).. Alexander . Veniaminovich. IM. , . room. . 3. 44. Friday. 1. 7. :00. or. Saturday 14:30. Approximation. . algorithms. . 2. We will study. . NP. -. hard optimization problem. 3. What you should know. 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 . Algorithms. and Networks 2015/2016. Hans L. . Bodlaender. Johan M. M. van Rooij. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. A. A. A. A. A. A. What to do if a problem is. 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. δ. -Timeliness. Carole . Delporte-Gallet. , . LIAFA . UMR 7089. , Paris VII. Stéphane Devismes. , VERIMAG UMR 5104, Grenoble I. Hugues Fauconnier. , . LIAFA . UMR 7089. , Paris VII. LIAFA. Motivation. 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 . 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. David P. Williamson. Joint work with Matthias Poloczek (Cornell), Georg Schnitger (Frankfurt), and Anke van Zuylen (William & Mary). Greedy algorithms. “Greed. , for lack of a better word, is good. Greed is right. Greed works. Lecture 18. May 29, . 2014. May 29, 2014. 1. CS38 Lecture 18. May 29, 2014. CS38 Lecture 18. 2. Outline. coping with . intractibility. approximation algorithms. set cover. TSP. center selection. randomness in algorithms.