3,949 research outputs found
Max-Min Greedy Matching
A bipartite graph G(U,V;E) that admits a perfect matching is given. One player imposes a permutation pi over V, the other player imposes a permutation sigma over U. In the greedy matching algorithm, vertices of U arrive in order sigma and each vertex is matched to the highest (under pi) yet unmatched neighbor in V (or left unmatched, if all its neighbors are already matched). The obtained matching is maximal, thus matches at least a half of the vertices. The max-min greedy matching problem asks: suppose the first (max) player reveals pi, and the second (min) player responds with the worst possible sigma for pi, does there exist a permutation pi ensuring to match strictly more than a half of the vertices? Can such a permutation be computed in polynomial time?
The main result of this paper is an affirmative answer for these questions: we show that there exists a polytime algorithm to compute pi for which for every sigma at least rho > 0.51 fraction of the vertices of V are matched. We provide additional lower and upper bounds for special families of graphs, including regular and Hamiltonian graphs. Our solution solves an open problem regarding the welfare guarantees attainable by pricing in sequential markets with binary unit-demand valuations
Bi-Criteria and Approximation Algorithms for Restricted Matchings
In this work we study approximation algorithms for the \textit{Bounded Color
Matching} problem (a.k.a. Restricted Matching problem) which is defined as
follows: given a graph in which each edge has a color and a profit
, we want to compute a maximum (cardinality or profit)
matching in which no more than edges of color are
present. This kind of problems, beside the theoretical interest on its own
right, emerges in multi-fiber optical networking systems, where we interpret
each unique wavelength that can travel through the fiber as a color class and
we would like to establish communication between pairs of systems. We study
approximation and bi-criteria algorithms for this problem which are based on
linear programming techniques and, in particular, on polyhedral
characterizations of the natural linear formulation of the problem. In our
setting, we allow violations of the bounds and we model our problem as a
bi-criteria problem: we have two objectives to optimize namely (a) to maximize
the profit (maximum matching) while (b) minimizing the violation of the color
bounds. We prove how we can "beat" the integrality gap of the natural linear
programming formulation of the problem by allowing only a slight violation of
the color bounds. In particular, our main result is \textit{constant}
approximation bounds for both criteria of the corresponding bi-criteria
optimization problem
The complexity of approximating the matching polynomial in the complex plane
We study the problem of approximating the value of the matching polynomial on
graphs with edge parameter , where takes arbitrary values in
the complex plane.
When is a positive real, Jerrum and Sinclair showed that the problem
admits an FPRAS on general graphs. For general complex values of ,
Patel and Regts, building on methods developed by Barvinok, showed that the
problem admits an FPTAS on graphs of maximum degree as long as
is not a negative real number less than or equal to
. Our first main result completes the picture for the
approximability of the matching polynomial on bounded degree graphs. We show
that for all and all real less than ,
the problem of approximating the value of the matching polynomial on graphs of
maximum degree with edge parameter is #P-hard.
We then explore whether the maximum degree parameter can be replaced by the
connective constant. Sinclair et al. showed that for positive real it
is possible to approximate the value of the matching polynomial using a
correlation decay algorithm on graphs with bounded connective constant (and
potentially unbounded maximum degree). We first show that this result does not
extend in general in the complex plane; in particular, the problem is #P-hard
on graphs with bounded connective constant for a dense set of values
on the negative real axis. Nevertheless, we show that the result does extend
for any complex value that does not lie on the negative real axis. Our
analysis accounts for complex values of using geodesic distances in
the complex plane in the metric defined by an appropriate density function
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