'Optimal flow' diaporamas de présentation

Selfish Algorithms

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Lecture 13

Lecture 13. Introduction to Minimum Cost Flows. -. A feasible flow x is optimal for the minimum cost flow problem if and only if some set of node potentials satisfy the following reduced cost optimality conditions: for every arc (i, j) in G(x),. Cycle Canceling Algorithm.

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Face Synthesis

Face Synthesis. M. L. Gavrilova. Outline. Face Synthesis From Modeling to Synthesis Facial Expression Synthesis Conclusions. Face Synthesis. How to synthesize photorealistic images of human faces has been a fascinating yet difficult problem in computer graphics.

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How bad is selfish routing?

How bad is selfish routing? . ??? ????? ????. 027256569 ??"? ???? ?? ???? ????? ???? ??-??????. The problem:. We are given: Network. Rate of traffic between nodes. Latency time. We should route the traffic such that the sum of all travel time is minimized.

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15.082 and 6.855J

15.082 and 6.855J. Cycle Canceling Algorithm. A minimum cost flow problem. 0. 0. 10, $4. 2. 4. 30, $7. 25, $5. 25. 1. 20, $2. 20, $6. 20, $1. 3. 5. 25, $2. 0. -25. The Original Capacities and Feasible Flow. 0. 0. 10, 10. 2. 4. 30, 25. 25, 15. 25. 1. 20, 10. 20, 20.

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Network Optimization Models: Maximum Flow Problems

Network Optimization Models: Maximum Flow Problems. In this handout: The problem statement Solving by linear programming Augmenting path algorithm. A. 4. 4. 6. B. O. D. 5. 5. T. 4. 4. 5. C. Maximum Flow Problem. Given : Directed graph G=(V, E),

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The Ford-Fulkerson Augmenting Path Algorithm for the Maximum Flow Problem

The Ford-Fulkerson Augmenting Path Algorithm for the Maximum Flow Problem. Ford-Fulkerson Max Flow. 4. 2. 5. 1. 3. 1. 1. 2. 2. s. 4. t. 3. 2. 1. 3. This is the original network, and the original residual network. Ford-Fulkerson Max Flow. 4. 2. 5. 1. 3. 1. 1. 2. 2. s.

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15.082 and 6.855J

15.082 and 6.855J. The Ford-Fulkerson Augmenting Path Algorithm for the Maximum Flow Problem. Ford-Fulkerson Max Flow. 4. 2. 5. 1. 3. 1. 1. 2. 2. s. 4. t. 3. 2. 1. 3. This is the original network, and the original residual network. Ford-Fulkerson Max Flow. 4. 2. 5. 1. 3.

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15.082 and 6.855J

15.082 and 6.855J. The Ford-Fulkerson Augmenting Path Algorithm for the Maximum Flow Problem. Ford-Fulkerson Max Flow. 4. 2. 5. 1. 3. 1. 1. 2. 2. s. 4. t. 3. 2. 1. 3. This is the original network, and the original residual network. Ford-Fulkerson Max Flow. 4. 2. 5. 1. 3.

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Network Optimization Models: Maximum Flow Problems

Network Optimization Models: Maximum Flow Problems. In this handout: The problem statement Solving by linear programming Augmenting path algorithm. A. 4. 4. 6. B. O. D. 5. 5. T. 4. 4. 5. C. Maximum Flow Problem. Given : Directed graph G=(V, E),

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Network Optimization Models: Maximum Flow Problems

Network Optimization Models: Maximum Flow Problems. In this handout: The problem statement Augmenting path algorithm for solving the problem. A. 4. 4. 6. B. O. D. 5. 5. T. 4. 4. 5. C. Maximum Flow Problem. Given : Directed graph G=(V, E),

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Maximum Flow and Minimum Cut Problems

Maximum Flow and Minimum Cut Problems. In this handout: Duality theory Upper bounds for maximum flow value Minimum Cut Problem Relationship between Maximum Flow and Minimum Cut . Primal and Dual Problems in Optimization. For many optimization problems, there is a dual problem

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