'Max flow' diaporamas de présentation

Soviet Rail Network, 1955

Soviet Rail Network, 1955. Reference: On the history of the transportation and maximum flow problems . Alexander Schrijver in Math Programming, 91: 3, 2002. Max flow and min cut. Two very rich algorithmic problems. Cornerstone problems in combinatorial optimization.

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Randomization in Graph Optimization Problems

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Announcements

Announcements. Assignment 4: Due Monday, April 5 at Midnight (due to university regulations). Submit solutions to any 2 of the 4 problems. Responsible for understanding all 4 problems (related material may be on final exam). Tutorial: Fri, Apr 2 A. Kolokolova 16:00-17:30 PSE 321

1.15k views • 104 slides

Optimization

Optimization. Spring 2012. 1. Some practical remarks. Lecturers: Kristoffer Arnsfelt Hansen and Peter Bro Miltersen . Homepage: www.cs.au.dk/dOpt Exam: Written, 3 hours.

871 views • 75 slides

Network Coding: A New Direction in Combinatorial Optimization

Network Coding: A New Direction in Combinatorial Optimization. Nick Harvey. Collaborators. David Karger Robert Kleinberg April Rasala Lehman Kazuo Murota. Kamal Jain Micah Adler. UMass. Transportation Problems. Max Flow. Min Cut. Transportation Problems. Communication Problems.

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CS137: Electronic Design Automation

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Max Flow Applications

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ENGM 792 Network Flow Programming

ENGM 792 Network Flow Programming. Minimum Cost Flow Solutions. Minimum Cost Flow Solutions. Primal Algorithm Find an initial network flow that satisfies primal feasibility Determine is network is optimal. If so, stop. If not, go to 3. Modify network flow, return to step 2.

540 views • 41 slides

Algorithm Design and Analysis

L ECTURES 18-19 Network Flows Flows, cuts Ford-Fulkerson Min-cut/max- flow theorem. Algorithm Design and Analysis. CSE 565. Adam Smith. Detecting Negative Cycles. Theorem. Can find negative cost cycle in O(mn ) time. Add new node t and connect all nodes to t with 0-cost edge.

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Randomness in Computation and Communication

Randomness in Computation and Communication. Part 1: Randomized algorithms. Lap Chi Lau CSE CUHK. Making Decision. Flip a coin. Making Decision. Flip a coin!. An algorithm which flip coins is called a randomized algorithm. Why Randomness?.

448 views • 35 slides

Prof. Swarat Chaudhuri

COMP 482: Design and Analysis of Algorithms. Prof. Swarat Chaudhuri. Spring 2012 Lecture 18. Soviet Rail Network, 1955. Reference: On the history of the transportation and maximum flow problems . Alexander Schrijver in Math Programming, 91: 3, 2002. Max flow and min cut.

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Visualizing Change over Time: the Pipes Metaphor

Visualizing Change over Time: the Pipes Metaphor. Jim Blythe USC Information Sciences Institute. Talk outline. Why pipes?? Visualizing, interacting with and animating flow. Scaling up: flow in citation and hats data with 1m nodes+links The emerging semantics/provenance of a visualization

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CSE 326: Data Structures Network Flow

CSE 326: Data Structures Network Flow. James Fogarty Autumn 2007. Network Flows. Given a weighted, directed graph G=(V,E) Treat the edge weights as capacities How much can we flow through the graph?. 1. F. 11. A. B. H. 7. 5. 3. 2. 6. 12. 9. 6. C. G. 11. 4. 10. 13. 20.

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Manipulation Resistant Reputation Systems

Manipulation Resistant Reputation Systems. Friedman Resnick Sami. Trust Graphs. Let t( i , j) > 0 denote the feedback i reports about j Let G = (V, E, t) where V is the set of agents, E the set of directed edges, and t is as before

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Market Equilibrium via a Primal-Dual-Type Algorithm

Market Equilibrium via a Primal-Dual-Type Algorithm. Written By Nikhil R. Devanur, Christos H. Papadimitriou, Amin Saberi, Vijay V. Vazirani Presented by Zhouyan Wang Advised by Prof. Kirk Pruhs November 18, 2003. Outline. Introduction Basic Algorithm: --- Basic Idea

371 views • 20 slides

Some Fundamental Insights of Computational Complexity Theory

Some Fundamental Insights of Computational Complexity Theory. Avi Wigderson IAS, Princeton, NJ Hebrew University, Jerusalem. ADD. MULT. PRIME. FACTOR. Complexity of Functions. Complexity Classes. Counting Problems Non-DET [Efficient Verification] Efficient Prob. Time

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Fluidized Bed

Fluidized Bed. CHE 381 Group 5 3/30/2011 Alex Guerrero Andrew Duffy Bernard Hsu Daniyal Qamar Jeff Tyska Ryan Kosak Tomi Damo. Source: EPA. Introduction. Purpose Measure the pressure drop through a fluidized bed column Observe the affects of fluid velocity on the bed

1.17k views • 10 slides

ECE 665 Spring 2004 Computer Algorithms with Applications to VLSI CAD

ECE 665 Spring 2004 Computer Algorithms with Applications to VLSI CAD. Linear Programming - Duality Max-Flow/Min-cut. Primal Problem. x3. x7. x1. x4. x0. x0. max x0. st. x2. x5. x8. -x1-x2+x0 = 0. x6. x1-x3-x4 = 0. Max flow. x2-x5-x6 = 0. x3+x5-x7 = 0. x4+x6-x8 = 0.

188 views • 6 slides

Shortest path

Shortest path. Shortest path with dijkstra algorithm A good written lecture here: http://www.cs.cornell.edu/courses/cs312/2002sp/lectures/lec20/lec20.htm

226 views • 5 slides