PPT-Approximation algorithms
Author : marina-yarberry | Published Date : 2016-09-02
Algorithms and Networks 20152016 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
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Approximation algorithms: Transcript
Algorithms and Networks 20152016 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. Prasad . Raghavendra. . Ning. Chen C. . . Thach. . Nguyen . . . Atri. . Rudra. . . Gyanit. Singh. University of Washington. Roee . Engelberg. Technion. University. Ankush Sharma . A0079739H. Xiao Liu . A0060004E. Tarek Ben Youssef A0093229. Reference. Terminologies – TSP & PTAS (Polynomial Time Approximation Schemes). Algorithm – A PTAS for Euclidian TSP (2D). Daniel . Dadush (CWI). Joint . with Santosh . Vempala. Volume Estimation. Given convex body . and factor . , compute . such that . .. . . given by a membership oracle.. . . . Volume Estimation. Lecture 11. Constantinos Daskalakis. Algorithms for Nash . Equilibria. Simplicial. Approximation Algorithms. Support Enumeration Algorithms. Lipton-. Markakis. -Mehta. Algorithms for Symmetric Games. Part I: Multistage problems. Anupam. Gupta. Carnegie Mellon University. stochastic optimization. Question: . How to model uncertainty in the inputs?. data may not yet be available. obtaining exact data is difficult/expensive/time-consuming. Anupam. Gupta. Carnegie Mellon University. stochastic optimization. Question: . How to model uncertainty in the inputs?. data may not yet be available. obtaining exact data is difficult/expensive/time-consuming. 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. 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. 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 . 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. 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..
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