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#### Rank-maximal through maximum weight matchings

##### MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45040

Michail,  Dimitrios
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

##### Externe Ressourcen
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##### Volltexte (frei zugänglich)

MPI-I-2005-1-001.ps
(beliebiger Volltext), 448KB

##### Ergänzendes Material (frei zugänglich)
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##### Zitation

Michail, D.(2005). Rank-maximal through maximum weight matchings (MPI-I-2005-1-001). Saarbrücken: Max-Planck-Institut für Informatik.

Given a bipartite graph $G( V, E)$, $V = A \disjointcup B$ where $|V|=n, |E|=m$ and a partition of the edge set into $r \le m$ disjoint subsets $E = E_1 \disjointcup E_2 \disjointcup \dots \disjointcup E_r$, which are called ranks, the {\em rank-maximal matching} problem is to find a matching $M$ of $G$ such that $|M \cap E_1|$ is maximized and given that $|M \cap E_2|$, and so on. Such a problem arises as an optimization criteria over a possible assignment of a set of applicants to a set of posts. The matching represents the assignment and the ranks on the edges correspond to a ranking on the posts submitted by the applicants. The rank-maximal matching problem has been previously studied where a $O( r \sqrt n m )$ time and linear space algorithm~\cite{IKMMP} was presented. In this paper we present a new simpler algorithm which matches the running time and space complexity of the above algorithm. The new algorithm is based on a different approach, by exploiting that the rank-maximal matching problem can be reduced to a maximum weight matching problem where the weight of an edge of rank $i$ is $2^{ \ceil{\log n} (r-i)}$. By exploiting that these edge weights are steeply distributed we design a scaling algorithm which scales by a factor of $n$ in each phase. We also show that in each phase one maximum cardinality computation is sufficient to get a new optimal solution. This algorithm answers an open question raised on the same paper on whether the reduction to the maximum-weight matching problem can help us derive an efficient algorithm.