PPT-A Simple ¾-Approximation Algorithm for MAX SAT

David P Williamson Joint work with Matthias Poloczek Cornell Georg Schnitger Frankfurt and Anke van Zuylen William amp Mary Greedy algorithms Greed for lack of a better word is good Greed is right Greed works. 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 . 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. 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. and Matroids. Soheil Ehsani. January 2018. Joint work with M. . Hajiaghayi. , T. . Kesselheim. , S. . Singla. The problem consists of an . initial setting . and a . sequence of events. .. We have to take particular actions . 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.