PPT-Approximation Algorithms

Author : danika-pritchard | Published Date : 2017-03-18

Guo QI Chen Zhenghai Wang Guanhua Shen Shiqi Himeshi De Silva Introduction Shen Shiqi Definition Approximation Algorithm Return the solutions to optimization problem

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Approximation Algorithms: Transcript


Guo QI Chen Zhenghai Wang Guanhua Shen Shiqi Himeshi De Silva Introduction Shen Shiqi Definition Approximation Algorithm Return the solutions to optimization problem Comparison Approximation . Prasad . Raghavendra. . Ning. Chen C. . . Thach. . Nguyen . . . Atri. . Rudra. . . Gyanit. Singh. University of Washington. Roee . Engelberg. Technion. University. . 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. 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 . 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).. 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. Lecture 12. Constantinos Daskalakis. The Lemke-. Howson. Algorithm. The Lemke-. Howson. Algorithm (1964). Problem:. Find an exact equilibrium of a 2-player game.. Since there exists a rational equilibrium this task is feasible.. Peter Andras. School of Computing and Mathematics. Keele University. p.andras@keele.ac.uk. Overview. High-dimensional functions and low-dimensional manifolds. Manifold mapping. Function approximation over low-dimensional projections. 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. LA. http://www.uasvision.com/2015/02/25/drones-collecting-cell-phone-data-in-. la. . AdNear. had already been using methods on the ground to collect consumer behavior data by using bikes, cars and trains to profile more than 530 million users in Asia, according to the company.. 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 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.

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