41,551 research outputs found
A push–relabel approximation algorithm for approximating the minimum-degree MST problem and its generalization to matroids
AbstractIn the minimum-degree minimum spanning tree (MDMST) problem, we are given a graph G, and the goal is to find a minimum spanning tree (MST) T, such that the maximum degree of T is as small as possible. This problem is NP-hard and generalizes the Hamiltonian path problem. We give an algorithm that outputs an MST of degree at most 2Δopt (G)+o(Δopt (G)), where Δopt (G) denotes the degree of the optimal tree. This result improves on a previous result of Fischer [T. Fischer, Optimizing the degree of minimum weight spanning trees. Technical Report 14853, Dept. of Computer Science, Cornell University, Ithaca, NY, 1993] that finds an MST of degree at most bΔopt (G)+logbn, for any b>1.The MDMST problem is a special case of the following problem: given a k-ary hypergraph G=(V,E) and weighted matroid M with E as its ground set, find a minimum-cost basis (MCB) T of M such that the degree of T in G is as small as possible. Our algorithm immediately generalizes to this problem, finding an MCB of degree at most k2Δopt (G,M)+O(kkΔopt (G,M)).We use the push–relabel framework developed by Goldberg [A. V. Goldberg, A new max-flow algorithm, Technical Report MIT/LCS/TM-291, Massachusetts Institute of Technology, 1985 (Technical Report)] for the maximum-flow problem. To our knowledge, this is the first use of the push–relabel technique in an approximation algorithm for an NP-hard problem.The MDMST problem is closely connected to the bounded-degree minimum spanning tree (BDMST) problem. Given a graph G and degree bound B on its nodes, the BDMST problem is to find a minimum cost spanning tree among the spanning trees with maximum degree B. Previous algorithms for this problem by Könemann and Ravi [J. Könemann, R. Ravi, A matter of degree: Improved approximation algorithms for degree-bounded minimum spanning trees, SIAM Journal on Computing 31(6) (2002) 1783–1793; J. Könemann, R. Ravi, Primal-dual meets local search: Approximating MST’s with nonuniform degree bounds, in: Proceedings of the Thirty-Fifth ACM Symposium on Theory of Computing, 2003, pp. 389–395] and by Chaudhuri et al. [K. Chaudhuri, S. Rao, S. Riesenfeld, K. Talwar, What would Edmonds do? Augmenting paths and witnesses for bounded degree MSTs, in: Proceedings of APPROX/RANDOM, 2005, pp. 26–39] incur a near-logarithmic additive error in the degree. We give the first BDMST algorithm that approximates both the degree and the cost to within a constant factor of the optimum. These results generalize to the case of nonuniform degree bounds
Caminos Pareto-eficientes en redes: aplicaciones
The problem of finding the path in a network connecting two given nodes,
source and sink, with minimum possible cost is known in the literature as
the shortest path problem (SPP) and it is a core model that lies at the
heart of network optimization. The reason is the wide range of its practical
applications and the large amount of interesting generalizations that can be
considered, among them, the analysis of the multiobjective shortest path
problem. This last model allows us to find how to send a given product
between two specified nodes of a network as quickly, as cheaply and as reliable as possible taking into account the so-called Pareto optimal paths.
The text presented here consists of two chapters. The first one is devoted
to the introduction of some notation and properties related to networks where each arc is associated to just one cost value. The formulation of the SPP as a Mathematical Programming model is considered as well as its corresponding dual problem. The complementary slackness theorem provides us with a characterization of any basic feasible solution as a spanning tree in the original network. A sufficient optimality condition is also derived from this relation. Finally, the Dijkstra algorithm is studied as well as other specific algorithms that use special properties on acyclic networks.
The second chapter extends the hypotheses considered in the previous
one to a multiobjective context. The continuous formulation of the problem
of finding the Pareto optimal paths is compared with its discrete version
and some properties are stated. A generalization of the Dijkstra algorithm
is proposed in order to find the whole set of Pareto optimal solutions. This
procedure allows us to determine that the optimal set of paths corresponds
with a set of adjacent trees. This property is very important in order to
generate the Pareto optimal set of solutions and is the basis of a new improved algorithm that, starting with a given optimal tree, explores its adjacent trees and finds those Pareto optimal ones by using the duality conditions of the complementary slackness theorem presented in the chapter one.Universidad de Sevilla. Grado en Matemática
A New Dynamic Programming Approach for Spanning Trees with Chain Constraints and Beyond
Short spanning trees subject to additional constraints are important building
blocks in various approximation algorithms. Especially in the context of the
Traveling Salesman Problem (TSP), new techniques for finding spanning trees
with well-defined properties have been crucial in recent progress. We consider
the problem of finding a spanning tree subject to constraints on the edges in
cuts forming a laminar family of small width. Our main contribution is a new
dynamic programming approach where the value of a table entry does not only
depend on the values of previous table entries, as it is usually the case, but
also on a specific representative solution saved together with each table
entry. This allows for handling a broad range of constraint types.
In combination with other techniques -- including negatively correlated
rounding and a polyhedral approach that, in the problems we consider, allows
for avoiding potential losses in the objective through the randomized rounding
-- we obtain several new results. We first present a quasi-polynomial time
algorithm for the Minimum Chain-Constrained Spanning Tree Problem with an
essentially optimal guarantee. More precisely, each chain constraint is
violated by a factor of at most , and the cost is no larger than
that of an optimal solution not violating any chain constraint. The best
previous procedure is a bicriteria approximation violating each chain
constraint by up to a constant factor and losing another factor in the
objective. Moreover, our approach can naturally handle lower bounds on the
chain constraints, and it can be extended to constraints on cuts forming a
laminar family of constant width.
Furthermore, we show how our approach can also handle parity constraints (or,
more precisely, a proxy thereof) as used in the context of (Path) TSP and one
of its generalizations, and discuss implications in this context.Comment: A short version of this work appeared in the proceedings of the 30th
annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2019
Bicriteria Network Design Problems
We study a general class of bicriteria network design problems. A generic
problem in this class is as follows: Given an undirected graph and two
minimization objectives (under different cost functions), with a budget
specified on the first, find a <subgraph \from a given subgraph-class that
minimizes the second objective subject to the budget on the first. We consider
three different criteria - the total edge cost, the diameter and the maximum
degree of the network. Here, we present the first polynomial-time approximation
algorithms for a large class of bicriteria network design problems for the
above mentioned criteria. The following general types of results are presented.
First, we develop a framework for bicriteria problems and their
approximations. Second, when the two criteria are the same %(note that the cost
functions continue to be different) we present a ``black box'' parametric
search technique. This black box takes in as input an (approximation) algorithm
for the unicriterion situation and generates an approximation algorithm for the
bicriteria case with only a constant factor loss in the performance guarantee.
Third, when the two criteria are the diameter and the total edge costs we use a
cluster-based approach to devise a approximation algorithms --- the solutions
output violate both the criteria by a logarithmic factor. Finally, for the
class of treewidth-bounded graphs, we provide pseudopolynomial-time algorithms
for a number of bicriteria problems using dynamic programming. We show how
these pseudopolynomial-time algorithms can be converted to fully
polynomial-time approximation schemes using a scaling technique.Comment: 24 pages 1 figur
Balancing Minimum Spanning and Shortest Path Trees
This paper give a simple linear-time algorithm that, given a weighted
digraph, finds a spanning tree that simultaneously approximates a shortest-path
tree and a minimum spanning tree. The algorithm provides a continuous
trade-off: given the two trees and epsilon > 0, the algorithm returns a
spanning tree in which the distance between any vertex and the root of the
shortest-path tree is at most 1+epsilon times the shortest-path distance, and
yet the total weight of the tree is at most 1+2/epsilon times the weight of a
minimum spanning tree. This is the best tradeoff possible. The paper also
describes a fast parallel implementation.Comment: conference version: ACM-SIAM Symposium on Discrete Algorithms (1993
Robust optimization with incremental recourse
In this paper, we consider an adaptive approach to address optimization
problems with uncertain cost parameters. Here, the decision maker selects an
initial decision, observes the realization of the uncertain cost parameters,
and then is permitted to modify the initial decision. We treat the uncertainty
using the framework of robust optimization in which uncertain parameters lie
within a given set. The decision maker optimizes so as to develop the best cost
guarantee in terms of the worst-case analysis. The recourse decision is
``incremental"; that is, the decision maker is permitted to change the initial
solution by a small fixed amount. We refer to the resulting problem as the
robust incremental problem. We study robust incremental variants of several
optimization problems. We show that the robust incremental counterpart of a
linear program is itself a linear program if the uncertainty set is polyhedral.
Hence, it is solvable in polynomial time. We establish the NP-hardness for
robust incremental linear programming for the case of a discrete uncertainty
set. We show that the robust incremental shortest path problem is NP-complete
when costs are chosen from a polyhedral uncertainty set, even in the case that
only one new arc may be added to the initial path. We also address the
complexity of several special cases of the robust incremental shortest path
problem and the robust incremental minimum spanning tree problem
Setting Parameters by Example
We introduce a class of "inverse parametric optimization" problems, in which
one is given both a parametric optimization problem and a desired optimal
solution; the task is to determine parameter values that lead to the given
solution. We describe algorithms for solving such problems for minimum spanning
trees, shortest paths, and other "optimal subgraph" problems, and discuss
applications in multicast routing, vehicle path planning, resource allocation,
and board game programming.Comment: 13 pages, 3 figures. To be presented at 40th IEEE Symp. Foundations
of Computer Science (FOCS '99
Weighted Min-Cut: Sequential, Cut-Query and Streaming Algorithms
Consider the following 2-respecting min-cut problem. Given a weighted graph
and its spanning tree , find the minimum cut among the cuts that contain
at most two edges in . This problem is an important subroutine in Karger's
celebrated randomized near-linear-time min-cut algorithm [STOC'96]. We present
a new approach for this problem which can be easily implemented in many
settings, leading to the following randomized min-cut algorithms for weighted
graphs.
* An -time sequential algorithm:
This improves Karger's and bounds when the input graph is not extremely
sparse or dense. Improvements over Karger's bounds were previously known only
under a rather strong assumption that the input graph is simple [Henzinger et
al. SODA'17; Ghaffari et al. SODA'20]. For unweighted graphs with parallel
edges, our bound can be improved to .
* An algorithm requiring cut queries to compute the min-cut of
a weighted graph: This answers an open problem by Rubinstein et al. ITCS'18,
who obtained a similar bound for simple graphs.
* A streaming algorithm that requires space and
passes to compute the min-cut: The only previous non-trivial exact min-cut
algorithm in this setting is the 2-pass -space algorithm on simple
graphs [Rubinstein et al., ITCS'18] (observed by Assadi et al. STOC'19).
In contrast to Karger's 2-respecting min-cut algorithm which deploys
sophisticated dynamic programming techniques, our approach exploits some cute
structural properties so that it only needs to compute the values of cuts corresponding to removing pairs of tree edges, an
operation that can be done quickly in many settings.Comment: Updates on this version: (1) Minor corrections in Section 5.1, 5.2;
(2) Reference to newer results by GMW SOSA21 (arXiv:2008.02060v2), DEMN
STOC21 (arXiv:2004.09129v2) and LMN 21 (arXiv:2102.06565v1
On Minimum Average Stretch Spanning Trees in Polygonal 2-trees
A spanning tree of an unweighted graph is a minimum average stretch spanning
tree if it minimizes the ratio of sum of the distances in the tree between the
end vertices of the graph edges and the number of graph edges. We consider the
problem of computing a minimum average stretch spanning tree in polygonal
2-trees, a super class of 2-connected outerplanar graphs. For a polygonal
2-tree on vertices, we present an algorithm to compute a minimum average
stretch spanning tree in time. This algorithm also finds a
minimum fundamental cycle basis in polygonal 2-trees.Comment: 17 pages, 12 figure
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