PPT-CSC317 1 Greedy algorithms:

Overarching principle Take the choice that looks best at the current moment More efficient than dynamic programming Always make the choice that looks best at the moment just one choice . Optimization problems, Greedy Algorithms, Optimal Substructure and Greedy choice. Learning & Development Team. http://academy.telerik.com. . Telerik Software Academy. Table of Contents. Optimization Problems. 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 . Yuli. Ye . Joint work with Allan Borodin, University of Toronto. Why do we study greedy algorithms? . don’t. A quote from Jeff Erickson’s algorithms book. . Everyone should tattoo the following sentence on the back of their hands, right under all the rules about logarithms and big-Oh notation. to . Greedy Routing Algorithms . in Ad-Hoc Networks. ○. Truong . Minh . Tien. Joint work with. Jinhee. . Chun, . Akiyoshi. . Shioura. , . and Takeshi . Tokuyama. Tohoku University. Japan. Our . Problem. Hamed Pirsiavash, Deva . Ramanan. , . Charless. . Fowlkes. Department of Computer Science, UC Irvine. 2. Estimate number of tracks and their extent. Do not initialize manually. Estimate birth and death of each track. 1. Equal costs at all levels. Root dominated. L. eave dominated. CSC317. 2. Master method. a. . subproblems. n/b. . size of each . subproblem. f(n). . cost of dividing problem and . combining results of . 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!. 1. Insertion/Deletion in binary trees. The operations of insertion and deletion cause the dynamic set represented by . a binary . search tree to change. The data structure must be modified . but preserve the . Breadth-first search tree: If node . v. is discovered after . u. then edge . uv. is added to the tree. We say that . u. is a predecessor (parent) of . v. . A vertex is discovered at most once.. Run time: . algorithms. So far we only looked at . unweighted. graphs. But what if we need to account for weights (and on top of it . negative. weights)?. Definition of a . shortest path problem. : We are given a weighted graph . 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. Fall 20151 Week . 7. CSCI-141. Scott C. Johnson. Say we go to the bank to cash our paycheck. We ask the teller for the fewest bills and coins as possible. Moments later the teller gives us our money and we leave. 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.. and SMA*. Remark: SMA* will be covered by Group Homework Credit Group C’s presentation but not in Dr. . Eick’s. lecture in 2022. Best-first search. Idea: use an . evaluation function. . f(n) . for each node.