PPT-A Near-Optimal Planarization Algorithm

Bart M P Jansen Daniel Lokshtanov University of Bergen Norway Saket Saurabh Institute of Mathematical Sciences India Insert Academic unit on every page 1 Go to the menu Insert. (associated lab: CS386). Pushpak Bhattacharyya. CSE Dept., . IIT Bombay . Lecture 3: A* and its properties. 6. th. . Jan, 2011. Search building blocks. State Space : Graph of states (Express constraints and parameters of the problem). CIS 606. Spring 2010. Greedy Algorithms. Similar to dynamic programming.. Used for optimization problems.. Idea. When we have a choice to make, make the one that looks best . right now. . Make . a locally . F. n. = F. n-1. + F. n-2. F. 0 . =0, F. 1 . =1. 0, 1, 1, 2, 3, 5, 8, 13, 21, 34 … . Straightforward recursive procedure is slow!. Why? How slow? . Let’s draw the recursion tree. Fibonacci Numbers (2). Lecture 10. Fang Yu. Department of Management Information Systems. National . Chengchi. University. Fall 2010. Fundamental Algorithms. Brute force, Greedy, Dynamic Programming:. Matrix Chain-Products . Xiaohua. Li and . Jeong. . Kyun. Lee. Department of Electrical and Computer Engineering . State University of New York at Binghamton . {xli,jlee54}@binghamton.edu . Major contributions. Develop efficient algorithm to construct optimal multi-hop path in arbitrarily large wireless networks. Lecture 22. N. Harvey. TexPoint. fonts used in EMF. . Read the . TexPoint. manual before you delete this box. .: . A. A. A. A. A. A. A. A. A. A. Topics. Integral . Polyhedra. Minimum s-t Cuts via Ellipsoid Method. Uninformed search . algorithms. Discussion Class CS 171. Friday, October, 2nd. (Please read lecture topic material before and after each lecture on that topic). Thanks to professor . Kask. Some of the slides (page 2-7) were copied from his lectures.. Evolutionary Multi-objective Algorithms. Karthik. . Sindhya. , . PhD. Postdoctoral Researcher. Industrial Optimization Group. Department of Mathematical Information Technology. Karthik.sindhya@jyu.fi. Abhilasha Seth. CSCE 669. Replacement Paths. G = (V,E) - directed graph with positive edge weights. ‘s’, ‘t’ - specified vertices. π. (s, t) - shortest path between them. Replacement Paths:. Evaluation. . Sequential: runtime (execution time). . Ts. =T (. InputSize. ). . Parallel: runtime (. s. tart-->last PE ends). . Tp. =T (. InputSize,p,architecture. ). . Note: Cannot be Evaluated in Isolation from the Parallel architecture. like me to cover on . Thursday. Asymptotic Notation. Binary. Search. T(n)=T(n/2) O(1). O(log. n). Merge Sort. T(n)=2T(n/2) O(n). O(n log n). Towers of Hanoi. T(n)=2T(n-1) O(1). O(2. n. ). Integer. Multiplication (. Greedy algorithms, coin changing problem. Haidong. . Xue. Summer 2012, at GSU. What is a greedy algorithm?. Greedy algorithm. : “an algorithm always makes the choice that looks best at the moment”. Or how to be 1-competitive with any set of strategies. Slides courtesy of . Avrim. Blum . Plan. Online Algorithms. Game Theory. Using “expert” advice. We solicit . N. “experts” for their advice. (Will the market go up or down?). Filters. . Chapter-7 : Wiener Filters and the LMS Algorithm. Marc Moonen . Dept. E.E./ESAT-STADIUS, KU Leuven. marc.moonen@esat.kuleuven.be. www.esat.kuleuven.be. /. stadius. /. Part-III : Optimal & Adaptive Filters.