Node-weighted Steiner Tree Problem Research Articles

To cut or to fill

We present a novel algorithm for simplifying the topology of a 3D shape, which is characterized by the number of connected components, handles, and cavities. Existing methods either limit their modifications to be only cutting or only filling, or take a heuristic approach to decide where to cut or fill. We consider the problem of finding a globally optimal set of cuts and fills that achieve the simplest topology while minimizing geometric changes. We show that the problem can be formulated as graph labelling, and we solve it by a transformation to the Node-Weighted Steiner Tree problem. When tested on examples with varying levels of topological complexity, the algorithm shows notable improvement over existing simplification methods in both topological simplicity and geometric distortions.

A Physarum-Inspired Algorithm for Minimum-Cost Relay Node Placement in Wireless Sensor Networks

Relay node placement, which aims to connect pre-deployed sensor nodes to base stations, is essential in minimizing the costs of wireless sensor networks. In this paper, we formulate the new Node-Weighted Partial Terminal Steiner Tree Problem (NWPTSTP) for minimum-cost relay node placement in two-tiered wireless sensor networks. The objective is to minimize the sum of heterogeneous production and placement costs of relay nodes and the sum of outage probabilities of transmission routes in a routing tree simultaneously. This extends the previous work that considers the costs of relay nodes to be homogeneous. After formulating NWPTSTP for this purpose, we prove that it can be transformed to the existing node-weighted Steiner tree problem. Subsequently, we conduct some theoretical analyses on the emerging Physarum-inspired algorithms to reveal their potential of computing Steiner trees. Based on these analyses, we propose a new Physarum-inspired algorithm for solving NWPTSTP. We conduct computational trials to show that: 1) in comparison to a state-of-the-art approximation algorithm for solving the node-weighted Steiner tree problem, our Physarum-inspired algorithm can produce better solutions in a smaller amount of time; and 2) in comparison to two state-of-the-art relay node placement algorithms, our Physarum-inspired algorithm can design wireless sensor networks with 25% lower relay cost and similar quality of service (specifically, 5% shorter network lifetime, 2% longer delay, and 0% loss of goodput). This indicates the usefulness of our Physarum-inspired algorithm for minimum-cost relay node placement in budget-limited scenarios.

Parameterized Analysis of the Online Priority and Node-Weighted Steiner Tree Problems

In this paper we study the online variant of two well-known Steiner tree problems. In the online setting, the input consists of a sequence of terminals; upon arrival of a terminal, the online algorithm must irrevocably buy a subset of edges and vertices of the graph so as to guarantee the connectivity of the currently revealed part of the input. More precisely, we first study the online node-weighted Steiner tree problem, in which both edges and vertices are weighted, and the objective is to minimize the total cost of edges and vertices in the solution. We then address the online priority Steiner tree problem, in which each edge and each request are associated with a priority value, which corresponds to their bandwidth support and requirement, respectively. Both problems have applications in the domain of multicast network communications and have been studied from the point of view of approximation algorithms. Motivated by the observation that competitive analysis gives very pessimistic and unsatisfactory results when the only relevant parameter is the number of terminals, we introduce an approach based on parameterized analysis of online algorithms. In particular, we base the analysis on additional parameters that help reveal the true complexity of the underlying problem, and allow a much finer classification of online algorithms based on their performance. More specifically, for the online node-weighted Steiner tree problem, we show a tight bound of Θ(max{min{α,k},log k}) on the competitive ratio, where α is the ratio of the maximum node weight to the minimum node weight and k is the number of terminals. For the online priority Steiner tree problem, we show corresponding tight bounds of ${\Theta }(b\log \frac {k}{b})$ , when k > b and Θ(k), when k ≤ b, where b is the number of priority levels and k is the number of terminals. Our main results apply to both deterministic and randomized algorithms, as well as to generalized versions of the problems (i.e., to Steiner forest variants).

A Dual Ascent-Based Branch-and-Bound Framework for the Prize-Collecting Steiner Tree and Related Problems

We present a branch-and-bound (B&B) framework for the asymmetric prize-collecting Steiner tree problem (APCSTP). Several well-known network design problems can be transformed to the APCSTP, including the Steiner tree problem (STP), prize-collecting Steiner tree problem (PCSTP), maximum-weight connected subgraph problem (MWCS), and node-weighted Steiner tree problem (NWSTP). The main component of our framework is a new dual ascent algorithm for the rooted APCSTP, which generalizes Wong’s dual ascent algorithm for the Steiner arborescence problem. The lower bounds and dual information obtained from the algorithm are exploited within powerful bound-based reduction tests and for guiding primal heuristics. The framework is complemented by additional alternative-based reduction tests. Extensive computational results on benchmark instances for the PCSTP, MWCS, and NWSTP indicate the framework’s effectiveness, as most instances from literature are solved to optimality within seconds, including most of the (previo...

Improved Approximation Algorithms for (Budgeted) Node-weighted Steiner Problems

Moss and Rabani study constrained node-weighted Steiner tree problems with two independent weight values associated with each node, namely, cost and prize (or penalty). They give an $O(\log n)$-approximation algorithm for the node-weighted prize-collecting Steiner tree problem (PCST)---where the goal is to minimize the cost of a tree plus the penalty of vertices not covered by the tree. They use the algorithm for PCST to obtain a bicriteria $(2, O(\log n))$-approximation algorithm for the budgeted node-weighted Steiner tree problem---where the goal is to maximize the prize of a tree with a given budget for its cost. Their solution may cost up to twice the budget, but collects a factor $\Omega(\frac{1}{\log n})$ of the optimal prize. We improve these results from at least two aspects. Our first main result is a primal-dual $O(\log h)$-approximation algorithm for a more general problem, node-weighted prize-collecting Steiner forest (PCSF), where we have $h$ demands each requesting the connectivity of a pair of vertices. Our algorithm can be seen as a greedy algorithm which reduces the number of demands by choosing a structure with minimum cost-to-reduction ratio. This natural style of argument leads to a much simpler algorithm than that of Moss and Rabani for PCST. Our second main contribution is for the budgeted node-weighted Steiner tree problem, which is also an improvement to the work of Moss and Rabani. In the unrooted case, we improve upon an existing $O(\log^2 n)$-approximation by Guha et al., and present an $O(\log n)$-approximation algorithm without any budget violation. For the rooted case, where a specified vertex has to appear in the solution tree, we improve the bicriteria result of Moss and Rabani to the bicriteria approximation ratio of $(1+\epsilon, O(\log n)/\epsilon^2)$ for any positive (possibly subconstant) $\epsilon$. That is, for any permissible budget violation $1+\epsilon$, we present an algorithm achieving a trade off in the guarantee for the prize. Indeed, we show that this is almost tight for the natural linear-programming relaxation used by us as well as in the previous works.

Constrained node-weighted Steiner tree based algorithms for constructing a wireless sensor network to cover maximum weighted critical square grids

Deploying minimum sensors to construct a wireless sensor network such that critical areas in a sensing field can be fully covered has received much attention recently. In previous studies, a sensing field is divided into square grids, and the sensors can be deployed only in the center of the grids. However, in reality, it is more practical to deploy sensors in any position in a sensing field. Moreover, the number of sensors may be limited due to a limited budget. This motivates us to study the problem of using limited sensors to construct a wireless sensor network such that the total weight of the covered critical square grids is maximized, termed the weighted-critical-square-grid coverage problem, where the critical grids are weighted by their importance. A reduction, which transforms our problem into a graph problem, termed the constrained node-weighted Steiner tree problem, is proposed and used to solve our problem. In addition, three heuristics, including the greedy algorithm (GA), the group-based algorithm (GBA), and the profit-based algorithm (PBA), are proposed for the constrained node-weighted Steiner tree problem. Simulation results show that the proposed reduction with the PBA provides better performance than the others.

An Approximation Algorithm for Constructing Degree-Dependent Node-Weighted Multicast Trees

This paper studies the problem of constructing a minimum-cost multicast tree (or Steiner tree) in which each node is associated with a cost that is dependent on its degree in the multicast tree. The cost of a node may depend on its degree in the multicast tree due to a number of reasons. For example, a node may need to perform various processing for sending messages to each of its neighbors in the multicast tree. Thus, the overhead for processing the messages increases as the number of neighbors increases. This paper devises a novel technique to deal with the degree-dependent node costs and applies the technique to develop an approximation algorithm for the degree-dependent node-weighted Steiner tree problem. The bound on the cost of the tree constructed by the proposed approximation algorithm is derived to be (2ln <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> /2 +1)(W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T*</sub> +B), where k is the size of the set of multicast members, W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T*</sub> is the cost of a minimum-cost Steiner tree T*, and B is related to the degree-dependent node costs. Simulations are carried out to study the performance of the proposed algorithm. A distributed implementation of the proposed algorithm is presented. In addition, the proposed algorithm is generalized to solve the degree-dependent node-weighted constrained forest problem.

Broadcasting in multi-radio multi-channel wireless networks using simplicial complexes

We consider the broadcasting problem in multi-radio multi-channel ad hoc networks. The objective is to minimize the total cost of the network-wide broadcast, where the cost can be of any form that is summable over all the transmissions (e.g., the transmission and reception energy, the price for accessing a specific channel). Our technical approach is based on a simplicial complex model that allows us to capture the broadcast nature of the wireless medium and the heterogeneity across radios and channels. Specifically, we show that broadcasting in multi-radio multi-channel ad hoc networks can be formulated as a minimum spanning problem in simplicial complexes. We establish the NP-completeness of the minimum spanning problem and propose two approximation algorithms with order-optimal performance guarantee. The first approximation algorithm converts the minimum spanning problem in simplical complexes to a minimum connected set cover (MCSC) problem. The second algorithm converts it to a node-weighted Steiner tree problem under the classic graph model. These two algorithms offer tradeoffs between performance and time-complexity. In a broader context, this work appears to be the first that studies the minimum spanning problem in simplicial complexes and weighted MCSC problem.

New approximations for minimum-weighted dominating sets and minimum-weighted connected dominating sets on unit disk graphs

Given a node-weighted graph, the minimum-weighted dominating set ( MWDS) problem is to find a minimum-weighted vertex subset such that, for any vertex, it is contained in this subset or it has a neighbor contained in this set. And the minimum-weighted connected dominating set ( MWCDS) problem is to find a MWDS such that the graph induced by this subset is connected. In this paper, we study these two problems on a unit disk graph. A (4 + ε )-approximation algorithm for an MWDS based on a dynamic programming algorithm for a Min-Weight Chromatic Disk Cover is presented. Meanwhile, we also propose a (1 + ε )-approximation algorithm for the connecting part by showing a polynomial-time approximation scheme for a Node-Weighted Steiner Tree problem when the given terminal set is c-local and thus obtain a (5 + ε )-approximation algorithm for an MWCDS.

Node-weighted Steiner tree approximation in unit disk graphs

Given a graph G=(V,E) with node weight w:V→R+ and a subset S⊆V, find a minimum total weight tree interconnecting all nodes in S. This is the node-weighted Steiner tree problem which will be studied in this paper. In general, this problem is NP-hard and cannot be approximated by a polynomial time algorithm with performance ratio aln n for any 0<a<1 unless NP⊆DTIME(nO(log n)), where n is the number of nodes in s. In this paper, we are the first to show that even though for unit disk graphs, the problem is still NP-hard and it has a polynomial time constant approximation. We present a 2.5ρ-approximation where ρ is the best known performance ratio for polynomial time approximation of classical Steiner minimum tree problem in graphs. As a corollary, we obtain that there is a polynomial time (9.875+e)-approximation algorithm for minimum weight connected dominating set in unit disk graphs, and also there is a polynomial time (4.875+e)-approximation algorithm for minimum weight connected vertex cover in unit disk graphs.

A RELAX-AND-CUT ALGORITHM FOR THE KNAPSACK NODE WEIGHTED STEINER TREE PROBLEM

The Knapsack Node Weighted Steiner Tree Problem (KNWSTP) is a generalization of the Steiner Tree Problem on graphs, which takes into account the classical cost function defined on the edges, as well as a prize function defined on the vertices and a limit on the size of the solution. It has several applications to network design. We propose an exact branch-and-bound algorithm for this problem, based on a relax-and-cut approach: the algorithm relaxes an exponential family of generalized subtour elimination constraints and takes into account only the violated ones as the computation proceeds. The performance of the algorithm has been tested on a wide set of benchmark problems, up to three hundred vertices, whose structure reflects the features of the most likely applications (sparse graphs with Euclidean costs) and covers different cases with respect to the prize function (only positive, or both positive and negative prizes) and the weight threshold.

Models and heuristics for a minimum arborescence problem

AbstractThe Minimum Arborescence problem (MAP) consists of finding a minimum cost arborescence in a directed graph. This problem is NP‐Hard and is a generalization of two well‐known problems: the Minimum Spanning Arborescence Problem (MSAP) and the Directed Node Weighted Steiner Tree Problem (DNWSTP). We start the model presentation in this paper by describing four models for the MSAP (including two new ones, using so called “connectivity” constraints which forbid disconnected components) and we then describe the changes induced on the polyhedral structure of the problem by the removal of the spanning property. Only two (the two new ones) of the four models for the MSAP remain valid when the spanning property is removed. We also describe a multicommodity flow reformulation for the MAP that differs from well‐known multicommodity flow reformulations in the sense that the flow conservation constraints at source and destination are replaced by inequalities. We show that the linear programming relaxation of this formulation is equivalent to the linear programming relaxation of the best of the two previous valid formulations and we also propose two Lagrangean relaxations based on the multicommodity flow reformulation. From the upper bound perspective, we describe a constructive heuristic as well as a local search procedure involving the concept of key path developed earlier for the Steiner Tree Problem. Numerical experiments taken from instances with up to 400 nodes are used to evaluate the proposed methods. © 2007 Wiley Periodicals, Inc. NETWORKS, 2008

Approximation Algorithms for Connected Dominating Sets

The dominating set problem in graphs asks for a minimum size subset of vertices with the following property: each vertex is required to be either in the dominating set, or adjacent to some vertex in the dominating set. We focus on the related question of finding a connected dominating set of minimum size, where the graph induced by vertices in the dominating set is required to be connected as well. This problem arises in network testing, as well as in wireless communication. Two polynomial time algorithms that achieve approximation factors of 2H(Δ)+2 and H(Δ)+2 are presented, where Δ is the maximum degree and H is the harmonic function. This question also arises in relation to the traveling tourist problem, where one is looking for the shortest tour such that each vertex is either visited or has at least one of its neighbors visited. We also consider a generalization of the problem to the weighted case, and give an algorithm with an approximation factor of (cn+1) \ln n where cn ln k is the approximation factor for the node weighted Steiner tree problem (currently cn = 1.6103 ). We also consider the more general problem of finding a connected dominating set of a specified subset of vertices and provide a polynomial time algorithm with a (c+1) H(Δ) +c-1 approximation factor, where c is the Steiner approximation ratio for graphs (currently c = 1.644 ).

An exact algorithm for the node weighted Steiner tree problem

The Node Weighted Steiner Tree Problem (NW-STP) is a generalization of the Steiner Tree Problem. A lagrangean heuristic presented in EngevallS: StrLBN: 98, and based on the work in Lucena: 92, solves the problem by relaxing an exponential family of generalized subtour elimination constraints and taking into account only the violated ones as the computation proceeds. In EngevallS: StrLBN: 98 the computational results refer to complete graphs up to one hundred vertices. In this paper, we present a branch-and-bound algorithm based on this formulation. Its performance on the instances from the literature confirms the effectiveness of the approach. The experimentation on a newly generated set of benchmark problems, more similar to the real-world applications, shows that the approach is still valid, provided that suitable refinements on the bounding procedures and a preprocessing phase are introduced. The algorithm solves to optimality all of the considered instances up to one thousand vertices, with the exception of 11 hard instances, derived from the literature of a similar problem, the Prize Collecting Steiner Tree Problem.

Identifying Regulatory Subnetworks for a Set of Genes

High throughput genomic/proteomic strategies, such as microarray studies, drug screens, and genetic screens, often produce a list of genes that are believed to be important for one or more reasons. Unfortunately it is often difficult to discern meaningful biological relationships from such lists. This study presents a new bioinformatic approach that can be used to identify regulatory subnetworks for lists of significant genes or proteins. We demonstrate the utility of this approach using an interaction network for yeast constructed from BIND, TRANSFAC, SCPD, and chromatin immunoprecipitation (ChIP)-Chip data bases and lists of genes from well known metabolic pathways or differential expression experiments. The approach accurately rediscovers known regulatory elements of the heat shock response as well as the gluconeogenesis, galactose, glycolysis, and glucose fermentation pathways in yeast. We also find evidence supporting a previous conjecture that approximately half of the enzymes in a metabolic pathway are transcriptionally co-regulated. Finally we demonstrate a previously unknown connection between GAL80 and the diauxic shift in yeast.

The General Steiner Tree-Star problem

The Steiner tree problem is defined as follows—given a graph G=( V, E) and a subset X⊂ V of terminals, compute a minimum cost tree that includes all nodes in X. Furthermore, it is reasonable to assume that the edge costs form a metric. This problem is NP-hard and has been the study of many heuristics and algorithms. We study a generalization of this problem, where there is a “switch” cost in addition to the cost of the edges. Switches are placed at internal nodes of the tree (essentially, we may assume that all non-leaf nodes of the Steiner tree have a switch). The cost for placing a switch may vary from node to node. A restricted version of this problem, where the terminal set X cannot be connected to each other directly but only via the Steiner nodes V⧹ X, is referred to as the Steiner Tree-Star problem. The General Steiner Tree-Star problem does not require the terminal set and Steiner node set to be disjoint. This generalized problem can be reduced to the node weighted Steiner tree problem, for which algorithms with performance guarantees of Θ(ln n) are known. However, such approach does not make use of the fact that the edge costs form a metric. In this paper we derive approximation algorithms with small constant factors for this problem. We show two different polynomial time algorithms with approximation factors of 5.16 and 5.

Approximation Algorithms for Degree-Constrained Minimum-Cost Network-Design Problems

We study network-design problems with two different design objectives: the total cost of the edges and nodes in the network and the maximum degree of any node in the network. A prototypical example is the degree-constrained node-weighted Steiner tree problem: We are given an undirected graph G(V,E), with a non-negative integral function d that specifies an upper bound d(v) on the degree of each vertex v∈V in the Steiner tree to be constructed, nonnegative costs on the nodes, and a subset of k nodes called terminals. The goal is to construct a Steiner tree T containing all the terminals such that the degree of any node v in T is at most the specified upper bound d(v) and the total cost of the nodes in T is minimum. Our main result is a bicriteria approximation algorithm whose output is approximate in terms of both the degree and cost criteria—the degree of any node v∈V in the output Steiner tree is O(d(v)log k) and the cost of the tree is O(log k) times that of a minimum-cost Steiner tree that obeys the degree bound d(v) for each node v. Our result extends to the more general problem of constructing one-connected networks such as generalized Steiner forests. We also consider the special case in which the edge costs obey the triangle inequality and present simple approximation algorithms with better performance guarantees.

A strong lower bound for the Node Weighted Steiner Tree Problem

In this paper, we study the Node Weighted Steiner Tree Problem (NSP). This problem is a generalization of the Steiner tree problem in the sense that vertex weights are considered. Given an undirected graph, the problem is to find a tree that spans a subset of the vertices and is such that the total edge cost minus the total vertex weight is minimized. We present a new formulation of NSP and derive a Lagrangean bound which used together with a heuristic procedure solves the NSP. Computational results are reported on a large set of test problems and optimality is proven for all the generated instances. © 1998 John Wiley & Sons, Inc. Networks 31: 11–17, 1998

A strong lower bound for the Node Weighted Steiner Tree Problem

In this paper, we study the Node Weighted Steiner Tree Problem (NSP). This problem is a generalization of the Steiner tree problem in the sense that vertex weights are considered. Given an undirected graph, the problem is to find a tree that spans a subset of the vertices and is such that the total edge cost minus the total vertex weight is minimized. We present a new formulation of NSP and derive a Lagrangean bound which used together with a heuristic procedure solves the NSP. Computational results are reported on a large set of test problems and optimality is proven for all the generated instances.

A Branch and Cut Algorithm for a Steiner Tree-Star Problem

This paper deals with a Steiner tree-star problem that is a special case of the degree constrained node-weighted Steiner tree problem. This problem arises in the context of designing telecommunications networks for digital data service, provided by regional telephone companies. In this paper, we develop an effective branch and cut procedure coupled with a suitable separation procedure that identifies violated facets for fractional solutions to the relaxed formulation. Computational results indicate that large problem instances with up to 200 nodes can be solved within acceptable time bounds.