A Novel Symbolic Algorithm for Maximum Weighted Matching in Bipartite Graphs
Tianlong Gu, Liang Chang, Zhoubo Xu
DOI: 10.4236/ijcns.2011.42014   PDF    HTML     5,435 Downloads   10,017 Views   Citations


The maximum weighted matching problem in bipartite graphs is one of the classic combinatorial optimization problems, and arises in many different applications. Ordered binary decision diagram (OBDD) or algebraic decision diagram (ADD) or variants thereof provides canonical forms to represent and manipulate Boolean functions and pseudo-Boolean functions efficiently. ADD and OBDD-based symbolic algorithms give improved results for large-scale combinatorial optimization problems by searching nodes and edges implicitly. We present novel symbolic ADD formulation and algorithm for maximum weighted matching in bipartite graphs. The symbolic algorithm implements the Hungarian algorithm in the context of ADD and OBDD formulation and manipulations. It begins by setting feasible labelings of nodes and then iterates through a sequence of phases. Each phase is divided into two stages. The first stage is building equality bipartite graphs, and the second one is finding maximum cardinality matching in equality bipartite graph. The second stage iterates through the following steps: greedily searching initial matching, building layered network, backward traversing node-disjoint augmenting paths, updating cardinality matching and building residual network. The symbolic algorithm does not require explicit enumeration of the nodes and edges, and therefore can handle many complex executions in each step. Simulation experiments indicate that symbolic algorithm is competitive with traditional algorithms.

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T. Gu, L. Chang and Z. Xu, "A Novel Symbolic Algorithm for Maximum Weighted Matching in Bipartite Graphs," International Journal of Communications, Network and System Sciences, Vol. 4 No. 2, 2011, pp. 111-121. doi: 10.4236/ijcns.2011.42014.

Conflicts of Interest

The authors declare no conflicts of interest.


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