1 / 18

Network Flow & Linear Programming

Thinking about Algorithms Abstractly. Network Flow & Linear Programming. Jeff Edmonds York University. Network Flow. Instance: A Network is a directed graph G Edges represent pipes that carry flow Each edge <u,v> has a maximum capacity c <u,v> A source node s in which flow arrives

raja
Télécharger la présentation

Network Flow & Linear Programming

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Thinking about Algorithms Abstractly Network Flow & Linear Programming Jeff Edmonds York University Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  2. Network Flow • Instance: • A Network is a directed graph G • Edges represent pipes that carry flow • Each edge <u,v> has a maximum capacity c<u,v> • A source node s in which flow arrives • A sink node t out which flow leaves Goal: Max Flow Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  3. Network Flow • Instance: • A Network is a directed graph G • Edges represent pipes that carry flow • Each edge <u,v> has a maximum capacity c<u,v> • A source node s in which flow arrives • A sink node t out which flow leaves Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  4. Network Flow • Solution: • The amount of flow F<u,v> through each edge. • Flow can’t exceed capacity i.e. F<u,v> c<u,v>. • Unidirectional flow • F<u,v> 0 and F<v,u> = 0 • or • F<u,v> = 0 and F<v,u> 0 Some texts: F<u,v>= -F<v,u> Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  5. Network Flow • Solution: • The amount of flow F<u,v> through each edge. • Flow F<u,v> can’t exceed capacity c<u,v>. • Unidirectional flow • No leaks, no extra flow. Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  6. Network Flow Except for s and t. • Solution: • The amount of flow F<u,v> through each edge. • Flow F<u,v> cant exceed capacity c<u,v>. • Unidirectional flow • No leaks, no extra flow. • For each node v: flow in = flow out • u F<u,v> = w F<v,w> Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  7. Network Flow • Value of Solution: • Flow from s into the network • minus flow from the network back into s. • rate(F) = u F<s,u> • = flow from network into t • minus flow back in. • = u F<u,t> - v F<t,v> - v F<v,s> What about flow back into s? Goal: Max Flow Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  8. Network flow problem is a linear program Taken from www.infosun.fim.uni-passau.de/br/lehrstuhl/Kurse/Proseminar_ss01/Network_flow_problems.ppt

  9. Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  10. Network Flow A network with its edge capacities What is the maximum that can flow from s to t? Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  11. Network Flow The max total rate of the flow is 1+2-0 = 3. flow/capacity = 2/5 A network with its edge capacities Can prove that total cannot be higher. Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  12. Network Flow No more flow can be pushedalong the top path because theedge <b,c> is at capacity. Similarly, the edge <e,f>. No flow is pushed along the bottom path because this would decrease the total from s to t. Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  13. Network Flow <U,V> is a minimum cut Its capacity is the sum of the capacities crossing the cut = 1+2 = 3. <i,j> is not included in because it is going in the wrong direction. Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  14. Network Flow The edges crossing forward across the cut are at capacity those crossing backwards have zero flow. This is always true. Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  15. Maxflow = Mincut The maximum flow is 1+2=3 The minimum cut is 1+2=3. These are always equal. Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  16. An Application: Matching 3 matches Can we do better? 4 matches Who loves whom. Who should be matched with whomso as many as possible matchedand nobody matched twice? Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  17. An Application: Matching s t 1 1 u v • c<s,u> = 1 • Total flow out of u = flow into u  1 • Boy u matched to at most one girl. • c<v,t> = 1 • Total flow into v = flow out of v  1 • Girl v matched to at most one boy. Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

  18. Min Cut s t • Instance: • A Network is a directed graph G • Special nodes s and t. • Edges represent pipes that carry flow • Each edge <u,v> has a maximum capacity c<u,v> • Partition into two regions so that the cut between the two is minimized Adapted from www.cse.yorku.ca/~jeff/notes/3101/03.5-NetworkFlow.ppt

More Related