Matroidal degree-bounded minimum spanning trees
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Improved algorithm for degree bounded survivable network design problem
SWAT'10 Proceedings of the 12th Scandinavian conference on Algorithm Theory
Network design with weighted degree constraints
Discrete Optimization
Chain-Constrained spanning trees
IPCO'13 Proceedings of the 16th international conference on Integer Programming and Combinatorial Optimization
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Given a graph and degree upper bounds on vertices, the BDMST problem requires us to find a minimum cost spanning tree respecting the given degree bounds. This problem generalizes the Travelling Salesman Path Problem (TSPP), even in unweighted graphs, and so we expect that it is necessary to relax the degree constraints to get efficient algorithms. Könemann and Ravi (Proceedings of the Thirty Second Annual ACM Symposium on Theory of Computing, pp. 537–546, 2000; Proceedings of the Thirty-Fifth ACM Symposium on Theory of Computing, pp. 389–395, 2003) give bicriteria approximation algorithms for the problem using local search techniques of Fischer (Technical Report 14853, Cornell University, 1993). Their algorithms find solutions which make a tradeoff of the approximation factor for the cost of the resulting tree against the factor by which degree constraints are violated. In particular, they give an algorithm which, for a graph with a spanning tree of cost C and degree B, and for parameters b,w1, produces a tree whose cost is at most wC and whose degree is at most $\frac{w}{w-1}bB+\log_{b}n.$ A primary contribution of Könemann and Ravi is to use a Lagrangean relaxation to formally relate the BDMST problem to what we call the MDMST problem, which is the problem of finding an MST of minimum degree in a graph. In their solution to the MDMST problem, they make central use of a local-search approximation algorithm of Fischer. In this paper, we give the first approximation algorithms for the BDMST problem—both our algorithms find trees of optimal cost. We achieve this improvement using a primal-dual cost bounding methodology from Edmonds’ weighted matching algorithms which was not previously used in this context. In order to follow Edmonds’ approach, we develop algorithms for a variant of the MDMST problem in which there are degree lower bound requirements. This variant may be of independent interest; in particular, our results extend to a generalized version of the BDMST problem in which both upper and lower degree bounds are given. First we give a polynomial-time algorithm that finds a tree of optimal cost and with maximum degree at most $\frac{b}{2-b}B+O(\log_{b}n)$for any b∈(1,2). We also give a quasi-polynomial-time approximation algorithm which produces a tree of optimal cost C and maximum degree at most B+O(log n/log log n). That is, the error is additive as well as restricted just to the degree. This further improvement in degree is obtained by using augmenting-path techniques that search over a larger solution space than Fischer’s local-search algorithm.