Directed Steiner Problems Research Articles

The Complexity Landscape of Fixed-Parameter Directed Steiner Network Problems

Given a directed graph G and a list ( s 1 , t 1 ), …, ( s d , t d ) of terminal pairs, the Directed Steiner Network problem asks for a minimum-cost subgraph of G that contains a directed s i → t i path for every 1≤ i ≤ d . The special case Directed Steiner Tree (when we ask for paths from a root r to terminals t 1 , …, t d ) is known to be fixed-parameter tractable parameterized by the number of terminals, while the special case Strongly Connected Steiner Subgraph (when we ask for a path from every t i to every other t j ) is known to be W[1]-hard parameterized by the number of terminals. We systematically explore the complexity landscape of directed Steiner problems to fully understand which other special cases are FPT or W[1]-hard. Formally, if ℋ is a class of directed graphs, then we look at the special case of Directed Steiner Network where the list ( s 1 , t 1 ), …, ( s d , t d ) of demands form a directed graph that is a member of ℋ. Our main result is a complete characterization of the classes ℋ resulting in fixed-parameter tractable special cases: we show that if every pattern in ℋ has the combinatorial property of being “transitively equivalent to a bounded-length caterpillar with a bounded number of extra edges,” then the problem is FPT, and it is W[1]-hard for every recursively enumerable ℋ not having this property. This complete dichotomy unifies and generalizes the known results showing that Directed Steiner Tree is FPT [Dreyfus and Wagner, Networks 1971], q -Root Steiner Tree is FPT for constant q [Suchý, WG 2016], Strongly Connected Steiner Subgraph is W[1]-hard [Guo et al., SIAM J. Discrete Math. 2011], and Directed Steiner Network is solvable in polynomial-time for constant number of terminals [Feldman and Ruhl, SIAM J. Comput. 2006], and moreover reveals a large continent of tractable cases that were not known before.

Parameterized Complexity of Arc-Weighted Directed Steiner Problems

We start a systematic parameterized computational complexity study of three NP-hard network design problems on arc-weighted directed graphs: directed Steiner tree, strongly connected Steiner subgraph, and directed Steiner network. We investigate their parameterized complexities with respect to the three parameterizations: “number of terminals,” “an upper bound on the size of the connecting network,” and the combination of these two. We achieve several parameterized hardness results as well as some fixed-parameter tractability results, in this way extending previous results of Feldman and Ruhl [SIAM J. Comput., 36 (2006), pp. 543–561].

A distributed dual ascent algorithm for Steiner problems in multicast routing

AbstractMulticast routing problems are often modeled as Steiner Problems in undirected or directed graphs, the latter case being particularly suitable to cases where most of the traffic has a single source. Sequential Steiner heuristics are not convenient in that context, because one cannot assume that a central node has complete information about the topology and the state of a large wide area network. This article introduces a distributed version of a Dual Ascent primal‐dual heuristic, known for its remarkably good practical results, lower and upper bounds, in both undirected and directed Steiner problems. Complexity analysis and experimental results are also presented, showing the efficiency of the proposed algorithm when compared with the best distributed algorithms in the literature. © 2008 Wiley Periodicals, Inc. NETWORKS, 2009

Integrality Ratio for Group Steiner Trees and Directed Steiner Trees

The natural relaxation for the group Steiner tree problem, as well as for its generalization, the directed Steiner tree problem, is a flow-based linear programming relaxation. We prove new lower bounds on the integrality ratio of this relaxation. For the group Steiner tree problem, we show that the integrality ratio is $\Omega(\log^2 k)$, where $k$ denotes the number of groups; this holds even for input graphs that are hierarchically well-separated trees, introduced by Bartal [in Proceedings of the 37th Annual IEEE Symposium on Foundations of Computer Science, 1996, pp. 184-193], in which case this lower bound is tight. This also applies for the directed Steiner tree problem. In terms of the number $n$ of vertices, our results for the directed Steiner problem imply an $\Omega(\frac{\log^2 n}{(\log \log n)^2})$ integrality ratio. For both problems, these are the first lower bounds on the integrality ratio that are superlogarithmic in the input size. This exhibits, for the first time, a relaxation of a natural optimization problem whose integrality ratio is known to be superlogarithmic but subpolynomial. Our results and techniques have been used by Halperin and Krauthgamer [in Proceedings of the 35th Annual ACM Symposium on Theory of Computing, 2003, pp. 585-594] to show comparable inapproximability results, assuming that NP has no quasi-polynomial Las Vegas algorithms. We also show algorithmically that the integrality ratio for the group Steiner tree problem is much better for certain families of instances, which helps pinpoint the types of instances (parametrized by optimal solutions to their flow-based relaxations) that appear to be most difficult to approximate.

A greedy approximation algorithm for the group Steiner problem

In the group Steiner problem we are given an edge-weighted graph G = ( V , E , w ) and m subsets of vertices { g i } i = 1 m . Each subset g i is called a group and the vertices in ⋃ i g i are called terminals. It is required to find a minimum weight tree that contains at least one terminal from every group. We present a poly-logarithmic ratio approximation for this problem when the input graph is a tree. Our algorithm is a recursive greedy algorithm adapted from the greedy algorithm for the directed Steiner tree problem [Approximating the weight of shallow Steiner trees, Discrete Appl. Math. 93 (1999) 265–285, Approximation algorithms for directed Steiner problems, J. Algorithms 33 (1999) 73–91]. This is in contrast to earlier algorithms that are based on rounding a linear programming based relaxation for the problem [A polylogarithmic approximation algorithm for the Group Steiner tree problem, J. Algorithms 37 (2000) 66–84, preliminary version in Proceedings of SODA, 1998 pp. 253–259, On directed Steiner trees, Proceedings of SODA, 2002, pp. 59–63]. We answer in positive a question posed in [A polylogarithmic approximation algorithm for the Group Steiner tree problem, J. Algorithms 37 (2000) 66–84, preliminary version in Proceedings of SODA, 1998 pp. 253–259] on whether there exist good approximation algorithms for the group Steiner problem that are not based on rounding linear programs. For every fixed constant ε > 0 , our algorithm gives an O ( ( log ∑ i | g i | ) 1 + ε · log m ) approximation in polynomial time. Approximation algorithms for trees can be extended to arbitrary undirected graphs by probabilistically approximating the graph by a tree. This results in an additional multiplicative factor of O ( log | V | ) in the approximation ratio, where | V | is the number of vertices in the graph. The approximation ratio of our algorithm on trees is slightly worse than the ratio of O ( log ( max i | g i | ) · log m ) provided by the LP based approaches.

The Polymatroid Steiner Problems

The Steiner tree problem asks for a minimum cost tree spanning a given set of terminals S⊂eqV in a weighted graph G = (V,E,c), c:E→ R+. In this paper we consider a generalization of the Steiner tree problem, so called Polymatroid Steiner Problem, in which a polymatroid P = P(V) is defined on V and the Steiner tree is required to span at least one base of P (in particular, there may be a single base S⊂eqV). This formulation is motivated by the following application in sensor networks – given a set of sensors S = {s1,…,s k }, each sensor s i can choose to monitor only a single target from a subset of targets X i , find minimum cost tree spanning a set of sensors capable of monitoring the set of all targets X = X1 ∪ … ∪ X k . The Polymatroid Steiner Problem generalizes many known Steiner tree problem formulations including the group and covering Steiner tree problems. We show that this problem can be solved with the polylogarithmic approximation ratio by a generalization of the combinatorial algorithm of Chekuri et al. (2002). We also define the Polymatroid directed Steiner problem which asks for a minimum cost arborescence connecting a given root to a base of a polymatroid P defined on the terminal set S. We show that this problem can be approximately solved by algorithms generalizing methods of Chekuri et al. (2002).

Steiner type problems for digraphs that are locally semicomplete or extended semicomplete

AbstractWe consider the following three problems: (P1) Let D be a strong digraph and let X be a non‐empty subset of its vertices. Find a strong subdigraph D′ of D which contains all vertices of X and has as few arcs as possible. This problem is also known under the name the directed Steiner problem. (P2) Let D be a strong digraph with real‐valued costs on the vertices. Find a strong subdigraph of D of minimum cost. (P3) Let D be a strong digraph with real‐valued costs on the vertices. Find a cycle of minimum cost in D. All three problems are NP‐hard for general digraphs. The second problem generalizes the problem of finding a smallest strong subdigraph (measured in the number of vertices) which covers a given non‐empty set X of vertices. We describe polynomial algorithms for the problems (P1) and (P2) in the case when D is either locally semicomplete or extended semicomplete and for (P3) in the case of locally semicomplete digraphs. A polynomial algorithm for (P3) in the case of extended semicomplete digraphs was given by Bang‐Jensen et al., 2002. © 2003 Wiley Periodicals, Inc. J Graph Theory 44: 193–207, 2003

Approximation Algorithms for Directed Steiner Problems

We give the first nontrivial approximation algorithms for the Steiner tree problem and the generalized Steiner network problem on general directed graphs. These problems have several applications in network design and multicast routing. For both problems, the best ratios known before our work were the trivial O(k)-approximations. For the directed Steiner tree problem, we design a family of algorithms that achieves an approximation ratio of i(i−1)k1/i in time O(nik2i) for any fixed i>1, where k is the number of terminals. Thus, an O(kϵ) approximation ratio can be achieved in polynomial time for any fixed ϵ>0. Setting i=logk, we obtain an O(log2k) approximation ratio in quasi-polynomial time. For the directed generalized Steiner network problem we give an algorithm that achieves an approximation ratio of O(k2/3log1/3k), where k is the number of pairs of vertices that are to be connected. Related problems including the group Steiner tree problem, the set TSP problem, and several others in both directed and undirected graphs can be reduced in an approximation preserving fashion to the directed Steiner tree problem. Thus, we obtain the first nontrivial approximations to those as well. All these problems are known to be as hard as the Set cover problem to approximate.

Directed Steiner problems with connectivity constraints

We present a generalization of the Steiner problem in a directed graph. Given nonnegative weights on the arcs, the problem is to find a minimum weight subset F of the arc set such that the subgraph induced by F contains a given number of arc-disjoint directed paths from a certain root node to each given terminal node. Some applications of the problem are discussed and properties of associated polyhedra are studied. Results from a cutting plane algorithm are reported.

A Comparison of Two Simulated Annealing Algorithms Applied to the Directed Steiner Problem on Networks

The well-known Steiner problem on networks is an NP-complete problem for which there are many deterministic algorithms and heuristics. In this paper a new approach to the directed version of this problem is made by applying the ideas of statistical mechanics through the use of the method of simulated annealing. Two different types of cooling algorithms are tailored to the Directed Steiner Problem. Computations are done on a test bed of 480 random graphs in order to study the performance of these algorithms. In addition to the quality of the final answers, the study looks at the distribution of first occurrences during the execution of the cooling schedules of answers that are within 3% of the optimum. The study suggests that these near-optimal answers appear according to an approximately exponential distribution and that they can be obtained in polynomial time. Experiments also indicate that for random graphs annealing usually gives near-optimal answers in less time than does branch and bound. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Some generalizations of the steiner problem in graphs

AbstractThe Steiner Problem in Graphs (SP) is the problem of finding a set of edges with minimum total weight which connects a given subset of nodes in an edge‐weighted (undirected) graph. In the more general Node‐weighted Steiner Problem (NSP) also node weights are considered. A restricted minimum spanning tree model is adjusted for the NSP as well as for the Steiner Forest Problem, a newly introduced generalization. The NSP is related to the Directed Steiner Problem. Reduction tests for the SP, reducing the size of the problem graph, are adapted for these generalizations and some new tests are developed.