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Matching (Graph Theory)

Last Updated : 30 Sep, 2024
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Matching (Graph Theory): In graph theory, matching is a fundamental concept used to describe a set of edges without common vertices. Matchings are used in various applications such as network design, job assignments, and scheduling. Understanding matchings is essential for solving problems involving optimal pairings and resource allocation.

Table of Content

  • Matching Definition
  • Types of Matching in Graphic Theory
    • Perfect Matching
    • Maximum Matching
    • Maximum Bipartite Matching
  • Algorithms for Finding Matchings
  • Applications – Matching in Graphic Theory
  • Practice Problems – Matching (Graph Theory)

Matching Definition

A matching in a graph is a set of edges such that no two edges share a common vertex. In other words, matching is a way of pairing up vertices so that each vertex is included in at most one pair.

Matching Example

For a bipartite graph with vertices U={u1,u2,u3} and V={v1,v2,v3}, a maximum matching could be {u1, v2), (u2, v3), (u3, v1).}

Types of Matching in Graphic Theory

Perfect Matching

A perfect matching is a matching where every vertex in the graph is connected to exactly one edge in the matching.

Example: For the bipartite graph G=(U,V,E) where U={u1,u2,u3} and V={v1,v2,v3}, a perfect matching could be M={(u1,v1),(u2,v2),(u3,v3)}

Maximum Matching

A maximum matching is a matching that contains the largest possible number of edges.

Example: For the same graph G, a maximum matching could be M = {(u1,v1),(u2,v2)}, assuming there are no other edges connecting u3 to V.

Maximum Bipartite Matching

In a bipartite graph, a maximum bipartite matching covers the maximum number of vertices in both partitions.

Example: For the bipartite graph GGG, a maximum bipartite matching could be M={(u1,v1),(u2,v2),(u3,v3)}

Read: Graph Theory Basics

Algorithms for Finding Matchings

  1. Hungarian Algorithm
    • The Hungarian algorithm is used to find the maximum matching in a bipartite graph.
    • Steps:
      1. Initialize the matching MMM to be empty.
      2. Find augmenting paths and increase the size of the matching.
      3. Repeat until no more augmenting paths can be found.
  2. Hopcroft-Karp Algorithm
    • The Hopcroft-Karp algorithm is an efficient algorithm for finding maximum matchings in bipartite graphs.
    • Steps:
      1. Perform a breadth-first search to find the shortest augmenting path.
      2. Perform a depth-first search to augment the matching along the path.
      3. Repeat until no more augmenting paths can be found.
  3. Blossom Algorithm
    • The Blossom algorithm, developed by Jack Edmonds, is used to find maximum matchings in general graphs (non-bipartite).
    • Steps:
      1. Find augmenting paths using the concept of blossoms (odd-length cycles).
      2. Contract blossoms into single vertices.
      3. Repeat the process until no more augmenting paths can be found.

Applications – Matching in Graphic Theory

  1. Network Design: Efficient routing and resource allocation.
  2. Job Assignment: Assigning jobs to machines or workers.
  3. Scheduling: Optimal scheduling of tasks.

Practice Problems – Matching (Graph Theory)

Problem 1: Given a graph, determine if a perfect matching exists.

Problem 2: Find all maximum matchings in a given bipartite graph.

Problem 3: Given a graph, find a maximum matching using augmenting paths.

Problem 4: In a bipartite graph, determine the minimum path cover.

Problem 5: Find a maximum weighted matching in a general graph.

Problem 6: Implement the Hungarian algorithm for assignment problems.

Problem 7: Determine if a matching is stable in a given preference list.

Problem 8: Find a maximum matching in a general graph using the Blossom algorithm.

Problem 9: Determine the maximum cardinality matching in a bipartite graph.

Problem 10: Find the minimum edge cover in a bipartite graph.

Related Articles:

  • Bipartite Graph
  • Graph Theory

Conclusion – Matching (Graph Theory)

Matching in graph theory is a fundamental concept with significant applications in optimization and network design. Understanding different types of matchings and algorithms to find them provides efficient solutions to complex problems involving pairings and resource allocation.


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