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Chapter 7

  • Matching in bipartite graphs can model situations in which objects are being assigned to other objects
    • Nodes in X represent jobs, and the nodes in Y represent machines, and an edge (xi,yj) indicates that machine yj is capable of processing job xi.
  • Perfect Matching: a way of assigning each job to a machine that can process it
    • Property that each machine is assigned exactly one job
  • One of the oldest problems in combinatorial algorithms: determining the size of the largest matching in a bipartite graph G
    • Network Flow Problems: includes the Bipartite Matching Problem as a special case

7.1 The Maximum-Flow Problem and the Ford-Fulkerson Algorithm

The Problem

  • Transportation Networks: networks whose edges carry some sort of traffic and whose nodes act as “switches” passing traffic between different edges
    • capacities on the edges
    • source nodes in the graph, which generate traffic
    • sink (or destination) nodes, which can “absorb” traffic as it arrives
    • traffic, which is transmitted across the edges
  • Flow Networks
    • traffic = flow
    • associated with each edge e is a capacity, ce
    • single source node s in V
    • single sink node t in V
    • all other nodes besides s and t are internal nodes
    • no edge enters s and no edge leaves t
    • at least one edge incident to each edge
    • all capacities are integers
  • Defining Flow
    • s-t flow is a function f that maps each edge e to a nonnegative real number, f
      • Flow must follow two properties:
        • Capacity: for each e in E: we have 0 ⇐ f(e) ⇐ ce
        • Conservation: for each node v other than s and t: sum of the flow value f(e) over all edges entering node v = sum of the flow values over all edges leaving node v
  • The Maximum-Flow Problem
    • goal: arrange the traffic for maximum efficiency of the available capacity
      • given a flow network, find a flow network
    • Each “cut” of the graph puts a bound on the maximum possible flow value
      • maximum-flow algorithm here will be intertwined with a proof that the maximum flow value equals the minimum capacity of any such division, called the minimum cut.
courses/cs211/winter2018/journals/patelk/chapter7.1522437552.txt.gz · Last modified: by patelk
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