Near Linear Time Approximation Schemes for Uncapacitated and Capacitated b--Matching Problems in Nonbipartite Graphs

TL;DR

This paper introduces near linear time approximation schemes for maximum weighted b-matching problems in nonbipartite graphs, utilizing innovative primal-dual algorithms and fractional solutions to achieve near optimal results efficiently.

Contribution

It presents the first near linear time approximation schemes for nonbipartite b-matching problems, including novel methods for approximating dual weights and reducing formulation width.

Findings

Achieves $(1- ext{delta})$ approximation in near linear time for large graphs.

Develops efficient fractional solutions and rounding techniques for b-matching.

Introduces new primal-dual algorithms with sparse dual weight approximations.

Abstract

We present the first near optimal approximation schemes for the maximum weighted (uncapacitated or capacitated) $b$--matching problems for non-bipartite graphs that run in time (near) linear in the number of edges. For any $\delta>3/\sqrt{n}$ the algorithm produces a $(1-\delta)$ approximation in $O(m \poly(\delta^{-1},\log n))$ time. We provide fractional solutions for the standard linear programming formulations for these problems and subsequently also provide (near) linear time approximation schemes for rounding the fractional solutions. Through these problems as a vehicle, we also present several ideas in the context of solving linear programs approximately using fast primal-dual algorithms. First, even though the dual of these problems have exponentially many variables and an efficient exact computation of dual weights is infeasible, we show that we can efficiently compute and use a sparse approximation of the dual weights using a combination of (i) adding perturbation to the constraints of the polytope and (ii) amplification followed by thresholding of the dual weights. Second, we show that approximation algorithms can be used to reduce the width of the formulation, and faster convergence.