A Parallel 2/3-Approximation Algorithm for Vertex-Weighted Matching
We consider the maximum vertex-weighted matching problem (MVM), in which non-negative weights are assigned to the vertices of a graph, and the weight of a matching is the sum of the weights of the matched vertices. Although exact algorithms for MVM are faster than exact algorithms for the maximum edge-weighted matching problem, there are graphs on which these exact algorithms could take hundreds of hours. For a natural number k, we design a k/(k + 1)approximation algorithm for MVM on non-bipartite graphs that updates the matching along certain short paths in the graph: either augmenting paths of length at most 2k + 1 or weight-increasing paths of length at most 2k. The choice of k = 2 leads to a 2/3-approximation algorithm that computes nearly optimal weights fast. This algorithm could be initialized with a 2/3-approximate maximum cardinality matching to reduce its runtime in practice. A 1/2-approximation algorithm may be obtained using k = 1, which is faster than the 2/3-approximation algorithm but it computes lower weights. The 2/3-approximation algorithm has time complexity O(Δ2m) while the time complexity of the 1/2-approximation algorithm is O(Δm), where m is the number of edges and Δ is the maximum degree of a vertex. more »
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NSF-PAR ID:
10181551
Journal Name:
SIAM 2020 Workshop on Combinatorial Scientific Computing
Page Range or eLocation-ID:
12-21
3. We describe a paradigm for designing parallel algorithms via approximation, and illustrate it on the b-edgecover problem. A b-edgecover of minimum weight in a graph is a subset $C$ of its edges such that at least a specified number $b(v)$ of edges in $C$ is incident on each vertex $v$, and the sum of the edge weights in $C$ is minimum. The Greedy algorithm and a variant, the LSE algorithm, provide $3/2$-approximation guarantees in the worst-case for this problem, but these algorithms have limited parallelism. Hence we design two new $2$-approximation algorithms with greater concurrency. The MCE algorithm reduces the computation of a b-edgecover to that of finding a b'-matching, by exploiting the relationship between these subgraphs in an approximation context. The LSE-NW is derived from the LSEalgorithm using static edge weights rather than dynamically computing effective edge weights. This relaxation gives LSE a worse approximation guarantee but makes it more amenable to parallelization. We prove that both the MCE and LSE-NW algorithms compute the same b-edgecover with at most twice the weight of the minimum weight edge cover. In practice, the $2$-approximation and $3/2$-approximation algorithms compute edge covers of weight within $10\%$ the optimal. We implement three of themore »