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Figure 1 MatchingMany problems that seem unrelated to matching can be formulated this way. 10 11 Graph Search Methods Many graph problems solved using a search method Path from one vertex to another Is the graph connected Find a spanning tree Etc Commonly used search methods Breadthfirst search Depthfirst search BreadthFirst Search Visit Carmine . Cerrone. , . Raffaele. . Cerulli. , Bruce Golden. GO IX. Sirmione. , Italy. July 2014. 1. Outline. Motivation. The Minimum Label Spanning Tree (MLST) problem. Experimental justification. Introduction to Carousel . 4101/5101. Approximation Algorithms. for NP-hard. Optimization Problems. Prof. Andy Mirzaian. 1. References:. . [CLRS] chapter 35. Lecture Notes 9, 10. AAW. Sanjoy. . Dasgupta. , Christos Papadimitriou, . S.Fahimeh. . Moosavi. Fall 1389. Basic Techniques. Scanning. -N/B I/Os while linear scanning the whole array.. Sorting. -O((N/B). log. M. /B . N/B) I/Os.. 2. Simulation of Parallel Algorithms in External Memory. Shaoxu Song (. Tsinghua. ), . Hong Cheng . (CUHK), . Jeffrey . Xu. . Yu. . (CUHK), . Lei Chen . (HKUST). Outline. Motivation . e. xamples. A simple greedy heuristic. How it fails. Contraction-based technique. The two key components. Optimal Sub-structure. You solve the problem by solving a sub-problem optimally. Greedy Property. Using the choice that seems best at the moment leads to the optimal result. This is tougher to show!. Jingtao Zhu. May 13rd,2016. “Efficient Influence Maximization . in Social Networks. ”. . Written by Chen Wei, Yajun Wang, and Siyu Yang. . Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. NS.4. What is a factor?. Factor- the numbers you multiply to get a product. Factor x Factor = Product. What are the factors of 12: . What is a common factor?. Common factor- a number that is a factor of two or more given numbers.. complete the square. If you have an equation in the form h = -2.25x. 2. 4.5x 6.75, where ‘h’ is height, how do you find the maximum height?. 1. st. , factor out -2.25:. CSE 421 Greedy Algorithms / Interval Scheduling Yin Tat Lee 1 Interval Scheduling Job starts at and finishes at . Two jobs compatible if they don’t overlap. Goal: find maximum subset of mutually compatible jobs. Dynamic and Online Algorithms: Anupam Gupta Carnegie Mellon University Based on joint works with: Albert Gu, Guru Guruganesh, Ravishankar Krishnaswamy, Amit Kumar, Debmalya Panigrahi, Cliff Stein, and David Wajc Optimization. . Algorithms. . over . Graphs. Hanjun. . Dai. *. Joint work with . Elias. . Khalil. *, . Yuyu. Zhang, . Bistra. . Dilkina. , Le Song. Georgia Tech. To appear in NIPS 2017. * . equal contribution. Instructor. : . S.N.TAZI. . ASSISTANT PROFESSOR ,DEPTT CSE. GEC AJMER. satya.tazi@ecajmer.ac.in. 3. -. 2. A simple example. Problem. : Pick k numbers out of n numbers such that the sum of these k numbers is the largest.. Xuliang. Zhu. , Xin Huang, Byron Choi, Jianliang Xu . Hong Kong Baptist University. Hong Kong, China . Outlines. Motivations. Related Work. KDAG-Problem. Algorithms. Experiments. Conclusions. Motivations.