New Approximation Algorithms for Map Labeling with Sliding Labels

In this paper we present approximation algorithm for the following NP-hard map labeling problem: Given a set S of n distinct sites in the plane, one needs to place at each site a uniform square of maximum possible size such that all the squares are along the same direction. This generalizes the classical problem of labeling points with axis-parallel squares and restricts the most general version where the squares can have different orientations. We obtain factor-4 and factor- approximation algorithms for this problem. These algorithms also work for two generalized versions of the problem. We also revisit the problem of labeling each point with maximum uniform axis-parallel square pairs and improve the previous approximation factor of 4 to 3.     

Two Variations of the Minimum Steiner Problem

Given a set S of starting vertices and a set T of terminating vertices in a graph G = (V,E) with non-negative weights on edges, the minimum Steiner network problem is to find a subgraph of G with the minimum total edge weight. In such a subgraph, we require that for each vertex s S and t T, there is a path from s to a terminating vertex as well as a path from a starting vertex to t. This problem can easily be proven NP-hard. For solving the minimum Steiner network problem, we first present an algorithm that runs in time and space that both are polynomial in n with constant degrees, but exponential in |S|+|T|, where n is the number of vertices in G. Then we present an algorithm that uses space that is quadratic in n and runs in time that is polynomial in n with a degree O(max {max {|S|,|T|}–2,min {|S|,|T|}–1}). In spite of this degree, we prove that the number of Steiner vertices in our solution can be as large as |S|+|T|–2. Our algorithm can enumerate all possible optimal solutions. The input graph G can either be undirected or directed acyclic. We also give a linear time algorithm for the special case when min {|S|,|T|} = 1 and max {|S|,|T|} = 2.The minimum union paths problem is similar to the minimum Steiner network problem except that we are given a set H of hitting vertices in G in addition to the sets of starting and terminating vertices. We want to find a subgraph of G with the minimum total edge weight such that the conditions required by the minimum Steiner network problem are satisfied as well as the condition that every hitting vertex is on a path from a starting vertex to a terminating vertex. Furthermore, G must be directed acyclic. For solving the minimum union paths problem, we also present algorithms that have a time and space tradeoff similar to algorithms for the minimum Steiner network problem. We also give a linear time algorithm for the special case when |S| = 1, |T| = 1 and |H| = 2.An extended abstract of part of this paper appears in Hsu et al. (1996).Supported in part by the National Science Foundation under Grants CCR-9309743 and INT-9207212, and by the Office of Naval Research under Grant No. N00014-93-1-0272.Supported in part by the National Science Council, Taiwan, ROC, under Grant No. NSC-83-0408-E-001-021.     

Fast On-Line/Off-Line Algorithms for Optimal Reinforcement of a Network and its Connections with Principal Partition

The problem of computing the strength and performing optimal reinforcement for an edge-weighted graph G(V, E, w) is well-studied. In this paper, we present fast (sequential linear time and parallel logarithmic time) on-line algorithms for optimally reinforcing the graph when the reinforcement material is available continuously on-line. These are the first on-line algorithms for this problem. We invest O(|V|³|E|log|V|) time (equivalent to (|V|) invocations of the fastest known algorithms for optimal reinforcement) in preprocessing the graph before the start of our algorithms. It is shown that the output of our on-line algorithms is as good as that of the off-line algorithms. Thus our algorithms are better than the fastest off-line algorithms in situations when a sequence of more than (|V|) reinforcement problems need to be solved. The key idea is to make use of ideas underlying the theory of Principal Partition of a Graph. Our ideas are easily generalized to the general setting of polymatroid functions. We also present a new efficient algorithm for computation of the Principal Sequence of a graph.     

An Improved Randomized Approximation Algorithm for Max TSP

We present an O(n³)-time randomized approximation algorithm for the maximum traveling salesman problem whose expected approximation ratio is asymptotically , where n is the number of vertices in the input (undirected) graph. This improves the previous best.Part of work done while visiting City University of Hong Kong.     

On minimum <Emphasis Type="Italic">m</Emphasis>-connected <Emphasis Type="Italic">k</Emphasis>-dominating set problem in unit disc graphs

Minimum m-connected k-dominating set problem is as follows: Given a graph G=(V,E) and two natural numbers m and k, find a subset S⊆V of minimal size such that every vertex in V∖S is adjacent to at least k vertices in S and the induced graph of S is m-connected. In this paper we study this problem with unit disc graphs and small m, which is motivated by the design of fault-tolerant virtual backbone for wireless sensor networks. We propose two approximation algorithms with constant performance ratios for m≤2. We also discuss how to design approximation algorithms for the problem with arbitrarily large m. This work was supported in part by the Research Grants Council of Hong Kong under Grant No. CityU 1165/04E, the National Natural Science Foundation of China under Grant No. 70221001, 10531070 and 10771209.     

Efficient point coverage in wireless sensor networks

We study minimum-cost sensor placement on a bounded 3D sensing field, R, which comprises a number of discrete points that may or may not be grid points. Suppose we have ℓ types of sensors available with different sensing ranges and different costs. We want to find, given an integer σ ≥ 1, a selection of sensors and a subset of points to place these sensors such that every point in R is covered by at least σ sensors and the total cost of the sensors is minimum. This problem is known to be NP-hard. Let k_i denote the maximum number of points that can be covered by a sensor of the ith type. We present in this paper a polynomial-time approximation algorithm for this problem with a proven approximation ratio . In applications where the distance of any two points has a fixed positive lower bound, each k_i is a constant, and so we have a polynomial-time approximation algorithms with a constant guarantee. While γ may be large, we note that it is only a worst-case upper bound. In practice the actual approximation ratio is small, even on randomly generated points that do not have a fixed positive minimum distance between them. We provide a number of numerical results for comparing approximation solutions and optimal solutions, and show that the actual approximation ratios in these examples are all less than 3, even though γ is substantially larger. This research was supported in part by NSF under grant CCF-04080261 and by NSF of China under grant 60273062.     

Labelling algorithms for paired-domination problems in block and interval graphs

Let G=(V,E) be a graph without isolated vertices. A set S⊆V is a paired-dominating set if every vertex in V−S is adjacent to a vertex in S and the subgraph induced by S contains a perfect matching. The paired-domination problem is to determine the paired-domination number, which is the minimum cardinality of a paired-dominating set. Motivated by a mistaken algorithm given by Chen, Kang and Ng (Discrete Appl. Math. 155:2077–2086, 2007), we present two linear time algorithms to find a minimum cardinality paired-dominating set in block and interval graphs. In addition, we prove that paired-domination problem is NP-complete for bipartite graphs, chordal graphs, even for split graphs.     

Approximating the Independence Number and the Chromatic Number in Expected Polynomial Time

The independence number of a graph and its chromatic number are known to be hard to approximate. Due to recent complexity results, unless coRP = NP, there is no polynomial time algorithm which approximates any of these quantities within a factor of n ^1– for graphs on n vertices.We show that the situation is significantly better for the average case. For every edge probability p = p(n) in the range n ^–1/2+ p 3/4, we present an approximation algorithm for the independence number of graphs on n vertices, whose approximation ratio is O((np)^1/2/log n) and whose expected running time over the probability space G(n, p) is polynomial. An algorithm with similar features is described also for the chromatic number.A key ingredient in the analysis of both algorithms is a new large deviation inequality for eigenvalues of random matrices, obtained through an application of Talagrand's inequality.     

Multi-phase Algorithms for Throughput Maximization for Real-Time Scheduling

We consider the problem of off-line throughput maximization for job scheduling on one or more machines, where each job has a release time, a deadline and a profit. Most of the versions of the problem discussed here were already treated by Bar-Noy et al. (Proc. 31st ACM STOC, 1999, pp. 622–631; http://www.eng.tau.ac.il/amotz/). Our main contribution is to provide algorithms that do not use linear programming, are simple and much faster than the corresponding ones proposed in Bar-Noy et al. (ibid., 1999), while either having the same quality of approximation or improving it. More precisely, compared to the results of in Bar-Noy et al. (ibid., 1999), our pseudo-polynomial algorithm for multiple unrelated machines and all of our strongly-polynomial algorithms have better performance ratios, all of our algorithms run much faster, are combinatorial in nature and avoid linear programming. Finally, we show that algorithms with better performance ratios than 2 are possible if the stretch factors of the jobs are bounded; a straightforward consequence of this result is an improvement of the ratio of an optimal solution of the integer programming formulation of the JISP2 problem (see Spieksma, Journal of Scheduling, vol. 2, pp. 215–227, 1999) to its linear programming relaxation.     

Broadcast Routing with Minimum Wavelength Conversion in WDM Optical Networks

We consider dynamic routing of broadcast connections in WDM optical networks. Given the current network state, we want to find a minimum set of network nodes S such that a broadcast routing using only the nodes in S as wavelength conversion nodes can be found. This ensures that the average conversion delay from the source to all destinations is minimized. We refer to the problem as Broadcast Conversion Node Selection (BCNS) problem. We prove that BCNS has no polynomial-time approximation with performance ratio ln n for < 1 unless NPDTIME(n^{O(log log n)}) where n is the number of vertices in the input graph. We present a greedy approximation algorithm for BCNS that achieves approximation ratio 2+ln n. Simulation results show that the algorithm performs very well in practice, obtaining optimal solutions in most of the cases.     

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).A preliminary version of this paper appeared in ISAAC 2004     

Partitioning a weighted partial order

The problem of partitioning a partially ordered set into a minimum number of chains is a well-known problem. In this paper we study a generalization of this problem, where we not only assume that the chains have bounded size, but also that a weight w _i is given for each element i in the partial order such that w _i ≤w _j if i ≺ j. The problem is then to partition the partial order into a minimum-weight set of chains of bounded size, where the weight of a chain equals the weight of the heaviest element in the chain. We prove that this problem is -hard, and we propose and analyze lower bounds for this problem. Based on these lower bounds, we exhibit a 2-approximation algorithm, and show that it is tight. We report computational results for a number of real-world and randomly generated problem instances.     

On Split-Coloring Problems

We study a new coloring concept which generalizes the classical vertex coloring problem in a graph by extending the notion of stable sets to split graphs. First of all, we propose the packing problem of finding the split graph of maximum size where a split graph is a graph G = (V,E) in which the vertex set V can be partitioned into a clique K and a stable set S. No condition is imposed on the edges linking vertices in S to the vertices in K. This maximum split graph problem gives rise to an associated partitioning problem that we call the split-coloring problem. Given a graph, the objective is to cover all his vertices by a least number of split graphs. Definitions related to this new problem are introduced. We mention some polynomially solvable cases and describe open questions on this area. An erratum to this article is available at .     

Safe sets in graphs: Graph classes and structural parameters

A safe set of a graph \(G=(V,E)\) is a non-empty subset S of V such that for every component A of G[S] and every component B of \(G[V {\setminus } S]\), we have \(|A| \ge |B|\) whenever there exists an edge of G between A and B. In this paper, we show that a minimum safe set can be found in polynomial time for trees. We then further extend the result and present polynomial-time algorithms for graphs of bounded treewidth, and also for interval graphs. We also study the parameterized complexity. We show that the problem is fixed-parameter tractable when parameterized by the solution size. Furthermore, we show that this parameter lies between the tree-depth and the vertex cover number. We then conclude the paper by showing hardness for split graphs and planar graphs.     

On Approximate Graph Colouring and MAX-k-CUT Algorithms Based on the θ-Function

The problem of colouring a k-colourable graph is well-known to be NP-complete, for k 3. The MAX-k-CUT approach to approximate k-colouring is to assign k colours to all of the vertices in polynomial time such that the fraction of `defect edges' (with endpoints of the same colour) is provably small. The best known approximation was obtained by Frieze and Jerrum (1997), using a semidefinite programming (SDP) relaxation which is related to the Lovász -function. In a related work, Karger et al. (1998) devised approximation algorithms for colouring k-colourable graphs exactly in polynomial time with as few colours as possible. They also used an SDP relaxation related to the -function.In this paper we further explore semidefinite programming relaxations where graph colouring is viewed as a satisfiability problem, as considered in De Klerk et al. (2000). We first show that the approximation to the chromatic number suggested in De Klerk et al. (2000) is bounded from above by the Lovász -function. The underlying semidefinite programming relaxation in De Klerk et al. (2000) involves a lifting of the approximation space, which in turn suggests a provably good MAX-k-CUT algorithm. We show that of our algorithm is closely related to that of Frieze and Jerrum; thus we can sharpen their approximation guarantees for MAX-k-CUT for small fixed values of k. For example, if k = 3 we can improve their bound from 0.832718 to 0.836008, and for k = 4 from 0.850301 to 0.857487. We also give a new asymptotic analysis of the Frieze-Jerrum rounding scheme, that provides a unifying proof of the main results of both Frieze and Jerrum (1997) and Karger et al. (1998) for k 0.     

A refined algorithm for maximum independent set in degree-4 graphs

The maximum independent set problem is one of the most important problems in theoretical analysis on time and space complexities of exact algorithms. Theoretical improvement on upper bounds on time complexity to solve this problem in low-degree graphs can lead to an improvement on that to the problem in general graphs. In this paper, we derive an upper bound \(O^*(1.1376^n)\) on the time complexity of a polynomial-space algorithm that solves the maximum independent set problem in an n-vertex graph with degree bounded by 4, improving all previous upper bounds on the time complexity of exact algorithms to this problem. Our algorithm is a branch-and-reduce algorithm and analyzed by using the measure-and-conquer method. To make an amortized analysis of the running time bound, we use an idea of “shift” to save some decrease of the measure from good branches to bad branches. Our algorithm first deals with small vertex cuts and vertices of degree \({\ge }5\), which may be created in our algorithm even if the input graph has maximum degree 4, then eliminates cycles of length 3 and 4 containing degree-4 vertices, and finally branches on degree-4 vertices. We invoke an exact algorithm for this problem in graphs with maximum degree 3 directly when the graph has no vertices of degree \({\ge }4\). Branching on degree-4 vertices on special local structures will be the bottleneck case, and we carefully design rules of choosing degree-4 vertices to branch on so that the resulting instances after branching decrease the measure effectively in the next step.     

Efficient algorithms for shared backup allocation in networks with partial information

We study efficient algorithms for establishing reliable connections with bandwidth guarantees in communication networks. In the normal mode of operation, each connection uses a primary path to deliver packets from the source to the destination. To ensure continuous operation in the event of an edge failure, each connection uses a set of backup bridges, each bridge protecting a portion of the primary path. To meet the bandwidth requirement of the connection, a certain amount of bandwidth must be allocated the edges of the primary path, as well as on the backup edges. In this paper, we focus on minimizing the amount of required backup allocation by sharing backup bandwidth among different connections. We consider efficient sharing schemes that require only partial information about the current state of the network. Specifically, the only information available for each edge is the total amount of primary allocation and the cost of allocating backup bandwidth on this edge. We consider the problem of finding a minimum cost backup allocation together with a set of bridges for a given primary path. We prove that this problem is NP-hard and present an approximation algorithm whose performance is within of the optimum, where n is the number of edges in the primary path. We also consider the problem of finding both a primary path and backup allocation of minimal total cost. A preliminary version of this paper appears in the Proceedings of 13th Annual European Symposium on Algorithms - ESA 2005, Mallorca, Spain. J. (Seffi) Naor: This research is supported in part by a foundational and strategical research grant from the Israeli Ministry of Science, and by a US-Israel BSF Grant 2002276.     

Performance Analysis and Improvement for Some Linear On-Line Bin-Packing Algorithms

The study on one-dimensional bin packing problem may bring about many important applications such as multiprocessor scheduling, resource allocating, real-world planning and packing. Harmonic algorithms (including H _K, RH, etc.) for bin packing have been famous for their linear-time and on-line properties for a long time. This paper profoundly analyzes the average-case performance of harmonic algorithms for pieces of i.i.d. sizes, provides the average-case performance ratio of H _K under (0,d] (d 1) uniform distribution and the average-case performance ratio of RH under (0,1] uniform distribution. We also finished the discussion of the worst-case performance ratio of RH. Moreover, we propose a new improved version of RH that obtains better worst- and average-case performance ratios.     

On the inapproximability of the exemplar conserved interval distance problem of genomes

In this paper we present two main results about the inapproximability of the exemplar conserved interval distance problem of genomes. First, we prove that it is NP-complete to decide whether the exemplar conserved interval distance between any two genomes is zero or not. This result implies that the exemplar conserved interval distance problem does not admit any approximation in polynomial time, unless P=NP. In fact, this result holds, even when every gene appears in each of the given genomes at most three times. Second, we strengthen the first result under a weaker definition of approximation, called weak approximation. We show that the exemplar conserved interval distance problem does not admit any weak approximation within a super-linear factor of , where m is the maximal length of the given genomes. We also investigate polynomial time algorithms for solving the exemplar conserved interval distance problem when certain constrains are given. We prove that the zero exemplar conserved interval distance problem of two genomes is decidable in polynomial time when one genome is O(log n)-spanned. We also prove that one can solve the constant-sized exemplar conserved interval distance problem in polynomial time, provided that one genome is trivial.     

Approximation and Exact Algorithms for RNA Secondary Structure Prediction and Recognition of Stochastic Context-free Languages

For a basic version (i.e., maximizing the number of base-pairs) of the RNA secondary structure prediction problem and the construction of a parse tree for a stochastic context-free language, O(n³) time algorithms were known. For both problems, this paper shows slightly improved O(n³(log log n)^1/2/(log n)^1/2) time exact algorithms, which are obtained by combining Valiant's algorithm for context-free recognition with fast funny matrix multiplication. Moreover, this paper shows an O(n^2.776 + (1/)^O(1)) time approximation algorithm for the former problem and an O(n^2.976 log n + (1/)^O(1)) time approximation algorithm for the latter problem, each of which has a guaranteed approximation ratio 1 – for any positive constant , where the absolute value of the logarithm of the probability is considered as an objective value in the latter problem. The former algorithm is obtained from a non-trivial modification of the well-known O(n³) time dynamic programming algorithm, and the latter algorithm is obtained by combining Valiant's algorithm with approximate funny matrix multiplication. Several related results are shown too.