3,695 research outputs found
Network Flow Models for Designing Diameter-Constrained Minimum Spanning and Steiner Trees
The Diameter-Constrained Minimum Spanning Tree Problem seeks a least cost spanning tree subject to a (diameter) bound imposed on the number of edges in the tree between any node pair. A traditional multicommodity flow model with a commodity for every pair of nodes was unable to solve a 20-node and 100-edge problem after one week of computation. We formulate the problem as a directed tree from a selected central node or a selected central edge. Our model simultaneously finds a central node or a central edge and uses it as the source for the commodities in a directed multicommodity flow model with hop constraints. The new model has been able to solve the 20-node, 100-edge instance to optimality after less than four seconds. We also present model enhancements when the diameter bound is odd (these situations are more difficult). We show that the linear programming relaxation of the best formulations discussed in this paper always give an optimal integer solution for two special, polynomially-solvable cases of the problem. We also examine the Diameter Constrained Minimum Steiner Tree problem. We present computational experience in solving problem instances with up to 100 nodes and 1000 edges. The largest model contains more than 250,000 integer variables and more than 125,000 constraints
Computational results for Constrained Minimum Spanning Trees in Flow Networks
In this work, we address the problem of finding a minimum cost spanning tree on a single source flow network. The tree must span all vertices in the given network and satisfy customer demands at a minimum cost. The total cost is given by the summation of the arc setup costs and of the nonlinear flow routing costs over all used arcs. Furthermore, we restrict the trees of interest by imposing a maximum number of arcs on the longest arc emanating from the single source vertex. We propose a dynamic programming model an solution procedure to solve this problem exactly. Intensive computational experiments were performed using randomly generated test problems and the results obtained are reported. From them we can conclude that the method performance is independent of the type of cost functions considered and improves with the tightness of the constrains.Dynamic programming, network flows, constrained trees, general nonlinear costs
Spanning trees short or small
We study the problem of finding small trees. Classical network design
problems are considered with the additional constraint that only a specified
number of nodes are required to be connected in the solution. A
prototypical example is the MST problem in which we require a tree of
minimum weight spanning at least nodes in an edge-weighted graph. We show
that the MST problem is NP-hard even for points in the Euclidean plane. We
provide approximation algorithms with performance ratio for the
general edge-weighted case and for the case of points in the
plane. Polynomial-time exact solutions are also presented for the class of
decomposable graphs which includes trees, series-parallel graphs, and bounded
bandwidth graphs, and for points on the boundary of a convex region in the
Euclidean plane. We also investigate the problem of finding short trees, and
more generally, that of finding networks with minimum diameter. A simple
technique is used to provide a polynomial-time solution for finding -trees
of minimum diameter. We identify easy and hard problems arising in finding
short networks using a framework due to T. C. Hu.Comment: 27 page
Target-based Distributionally Robust Minimum Spanning Tree Problem
Due to its broad applications in practice, the minimum spanning tree problem
and its all kinds of variations have been studied extensively during the last
decades, for which a host of efficient exact and heuristic algorithms have been
proposed. Meanwhile, motivated by realistic applications, the minimum spanning
tree problem in stochastic network has attracted considerable attention of
researchers, with respect to which stochastic and robust spanning tree models
and related algorithms have been continuingly developed. However, all of them
would be either too restricted by the types of the edge weight random variables
or computationally intractable, especially in large-scale networks. In this
paper, we introduce a target-based distributionally robust optimization
framework to solve the minimum spanning tree problem in stochastic graphs where
the probability distribution function of the edge weight is unknown but some
statistical information could be utilized to prevent the optimal solution from
being too conservative. We propose two exact algorithms to solve it, based on
Benders decomposition framework and a modified classical greedy algorithm of
MST problem (Prim algorithm),respectively. Compared with the NP-hard stochastic
and robust spanning tree problems,The proposed target-based distributionally
robust minimum spanning tree problem enjoys more satisfactory algorithmic
aspect and robustness, when faced with uncertainty in input data
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