PPT-Min Cost Flow: Polynomial Algorithms

Overview Recap Min Cost Flow Residual Network Potential and Reduced Cost Polynomial Algorithms Approach Capacity Scaling Successive Shortest Path Algorithm Recap Incorporating Scaling. . NP-Complete. CSE 680. Prof. Roger Crawfis. Polynomial Time. Most (but not all) of the algorithms we have studied so far are easy, in that they can be solved in polynomial time, be it linear, quadratic, cubic, etc.. Lecture . 22. : . The P vs. NP question. , . NP-Completeness. Lauren Milne. Summer 2015. Admin. Homework 6 is posted. Due next Wednesday. No partners. Algorithm Design Techniques. Greedy. Shortest path, minimum spanning tree, …. P = . { computational problems that can be solved efficiently }. i.e., solved in time . ·. n. c. , for some constant . c. , where . n. =. input size. This is a bit vague. Consider an LP max { . c. T. 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. JAMES B. ORLIN. Aviv Eisenschtat . 6/5/2013. Introduction. Developed in 1989. Based . on the Edmonds & Karp scaling algorithm. Fastest strongly polynomial algorithm for min-cost flow. Fairly simple and intuitive . (a brief introduction to theoretical computer science). slides by Vincent Conitzer. Set Cover . (a . computational problem. ). We are given:. A finite set S = {1, …, n}. A collection of subsets of S: S. Introduction. Minimum-Mean Cycle Canceling . Algorithm. Repeated Capacity Scaling . Algorithm. Enhanced Capacity Scaling. Algorithm. Summary. Minimum Cost Flow Problem –. Strongly Polynomial Algorithms. Cost . Flow, Kevin D. Wayne. Eyal Dushkin – 03.06.13. Reminder – Generalized Flows. We are given a graph . We associate a positive . with every . arc. Assume that if 1 unit of flow was sent from node . Network Flow Models. Transportation Models. (Flow, Cost). [External Flow]. [2]. [4]. [3]. [-3]. [-3]. [-3]. (3,3). (1,1). (0,4). (2,2). (3,3). (0,3). (0,4). 1. 2. 3. 4. 5. 6. Transportation Models. (Flow, Cost). Optical flow , A tutorial of the paper: KH Wong Optical Flow v.5a (beta) 1 G. Farneback , “Two-frame Motion Estimation based on Polynomial Expansion”, 13th Scandinavian Conference, SCIA 2003 Halmstad Matching Algorithms and Networks Algorithms and Networks: Matching 2 This lecture Matching: problem statement and applications Bipartite matching (recap) Matching in arbitrary undirected graphs: Edmonds algorithm Hans Bodlaender. Teacher. 2. nd. Teacher Algorithms and Networks. Hans Bodlaender. Room 503, Buys Ballot Gebouw. Schedule:. Mondays: Hans works in Eindhoven. A&N: Maximum flow. 2. A&N: Maximum flow. Lecture 17: Shortest Paths. Catie Baker. Spring . 2015. Announcements. Homework . 4 due next Wednesday, May . 13th. Spring 2015. 2. CSE373: Data Structures & Algorithms. Graph Traversals. For an arbitrary graph and a starting node . Mingwei. Hu. . . F. airness criteria in network resource allocation. How do we achieve fairness between multiplexed packets traffic ?. By allocating rate among flows (flow rate fairness). Flow rate fairness has been the goal behind fair...