Approximation Algorithms for Minimum K -Cut

Let G=(V,E) be a complete undirected graph, with node set V={v ₁ , . . ., v _n } and edge set E . The edges (v _i ,v _j ) ∈ E have nonnegative weights that satisfy the triangle inequality. Given a set of integers K = { k _i } _i=1 ^p , the minimum K-cut problem is to compute disjoint subsets with sizes { k _i } _i=1 ^p , minimizing the total weight of edges whose two ends are in different subsets. We demonstrate that for any fixed p it is possible to obtain in polynomial time an approximation of at most three times the optimal value. We also prove bounds on the ratio between the weights of maximum and minimum cuts. Received September 4, 1997; revised July 15, 1998.     

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 (c _n +1) \ln n where c _n ln k is the approximation factor for the node weighted Steiner tree problem (currently c _n = 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 ). Received June 22, 1996; revised February 28, 1997.     

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 nonnegative 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. Received December 21, 1998; revised September 24, 1999.     

Approximation Algorithms for a k-Line Center

Given a set P of n points in ℝd and an integer k ≥ 1, let w^* denote the minimum value so that P can be covered by k congruent cylinders of radius w^*. We describe a randomized algorithm that, given P and an ε > 0, computes k cylinders of radius (1 + ε) w^* that cover P. The expected running time of the algorithm is O(n log n), with the constant of proportionality depending on k, d, and ε. We first show that there exists a small ”certificate” Q ⫅ P, whose size does not depend on n, such that for any k congruent cylinders that cover Q, an expansion of these cylinders by a factor of (1 + ε) covers P. We then use a well-known scheme based on sampling and iterated re-weighting for computing the cylinders.     

Approximation Algorithms for Aligning Points

We study the problem of aligning as many points as possible horizontally, vertically, or diagonally, when each point is allowed to be placed anywhere in its own given region. Different shapes of placement regions and different sets of alignment orientations are considered. More generally, we assume that a graph is given on the points, and only the alignments of points that are connected in the graph count. We show that for planar graphs the problem is NP-hard, and we provide an inapproximability result for general graphs. For the case of trees and planar graphs, we give approximation algorithms whose performance depends on the shape of the given regions and the set of orientations. When the orientations are the ones given by the axes and the regions are axis-parallel rectangles, we obtain a polynomial time approximation scheme.     

Near-Linear Time Approximation Algorithms for Curve Simplification

We consider the problem of approximating a polygonal curve P under a given error criterion by another polygonal curve P’ whose vertices are a subset of the vertices of P. The goal is to minimize the number of vertices of P’ while ensuring that the error between P’ and P is below a certain threshold. We consider two different error measures: Hausdorff and Frechet. For both error criteria, we present near-linear time approximation algorithms that, given a parameter ε > 0, compute a simplified polygonal curve P’ whose error is less than ε and size at most the size of an optimal simplified polygonal curve with error ε/2. We consider monotone curves in ℝ² in the case of the Hausdorff error measure under the uniform distance metric and arbitrary curves in any dimension for the Frechet error measure under L_p metrics. We present experimental results demonstrating that our algorithms are simple and fast, and produce close to optimal simplifications in practice.     

Approximation Algorithms for Multi-Criteria Traveling Salesman Problems

We analyze approximation algorithms for several variants of the traveling salesman problem with multiple objective functions. First, we consider the symmetric TSP (STSP) with γ-triangle inequality. For this problem, we present a deterministic polynomial-time algorithm that achieves an approximation ratio of and a randomized approximation algorithm that achieves a ratio of . In particular, we obtain a 2+ε approximation for multi-criteria metric STSP. Then we show that multi-criteria cycle cover problems admit fully polynomial-time randomized approximation schemes. Based on these schemes, we present randomized approximation algorithms for STSP with γ-triangle inequality (ratio ), asymmetric TSP (ATSP) with γ-triangle inequality (ratio ), STSP with weights one and two (ratio 4/3) and ATSP with weights one and two (ratio 3/2). A preliminary version of this work has been presented at the 4th Workshop on Approximation and Online Algorithms (WAOA 2006) (Lecture Notes in Computer Science, vol. 4368, pp. 302–315, 2007). B. Manthey is supported by the Postdoc-Program of the German Academic Exchange Service (DAAD). He is on leave from Saarland University and has done part of the work at the Institute for Theoretical Computer Science of the University of Lübeck supported by DFG research grant RE 672/3 and at the Department of Computer Science at Saarland University.     

Approximation Algorithms with Bounded Performance Guarantees for the Clustered Traveling Salesman Problem

Let G=(V,E) be a complete undirected graph with vertex set V , edge set E , and edge weights l(e) satisfying triangle inequality. The vertex set V is partitioned into clusters V ₁ , . . ., V _k . The clustered traveling salesman problem is to compute a shortest Hamiltonian cycle (tour) that visits all the vertices, and in which the vertices of each cluster are visited consecutively. Since this problem is a generalization of the traveling salesman problem, it is NP-hard. In this paper we consider several variants of this basic problem and provide polynomial time approximation algorithms for them. Received February 13, 1998; revised July 8, 1998.     

Approximation Algorithms for Access Network Design

Abstract. We consider the problem of designing a minimum cost access network to carry traffic from a set of endnodes to a core network. Trunks are available in K types reflecting economies of scale . A trunk type with a high initial overhead cost has a low cost per unit bandwidth and a trunk type with a low overhead cost has a high cost per unit bandwidth. We formulate the problem as an integer program. We first use a primal—dual approach to obtain a solution whose cost is within O(K ² ) of optimal. Typically the value of K is small. This is the first combinatorial algorithm with an approximation ratio that is polynomial in K and is independent of the network size and the total traffic to be carried. We also explore linear program rounding techniques and prove a better approximation ratio of O(K) . Both bounds are obtained under weak assumptions on the trunk costs. Our primal—dual algorithm is motivated by the work of Jain and Vazirani on facility location [7]. Our rounding algorithm is motivated by the facility location algorithm of Shmoys et al. [12].     

Approximation Algorithms for Quickest Spanning Tree Problems

Let $G=(V,E)$ be an undirected multigraph with a special vertex ${\it root} \in V$, and where each edge $e \in E$ is endowed with a length $l(e) \geq 0$ and a capacity $c(e) > 0$. For a path $P$ that connects $u$ and $v$, the {\it transmission time} of $P$ is defined as $t(P)=\mbox{\large$\Sigma$}_{e \in P} l(e) + \max_{e \in P}\!{(1 / c(e))}$. For a spanning tree $T$, let $P_{u,v}^T$ be the unique $u$--$v$ path in $T$. The {\sc quickest radius spanning tree problem} is to find a spanning tree $T$ of $G$ such that $\max _{v \in V} t(P^T_{root,v})$ is minimized. In this paper we present a 2-approximation algorithm for this problem, and show that unless $P =NP$, there is no approximation algorithm with a performance guarantee of $2 - \epsilon$ for any $\epsilon >0$. The {\sc quickest diameter spanning tree problem} is to find a spanning tree $T$ of $G$ such that $\max_{u,v \in V} t(P^T_{u,v})$ is minimized. We present a ${3 \over 2}$-approximation to this problem, and prove that unless $P=NP$ there is no approximation algorithm with a performance guarantee of ${3 \over 2}-\epsilon$ for any $\epsilon >0$.     

Approximation Algorithms for Quickest Spanning Tree Problems

Let $G=(V,E)$ be an undirected multigraph with a special vertex ${\it root} \in V$, and where each edge $e \in E$ is endowed with a length $l(e) \geq 0$ and a capacity $c(e) > 0$. For a path $P$ that connects $u$ and $v$, the {\it transmission time} of $P$ is defined as $t(P)=\mbox{\large$\Sigma$}_{e \in P} l(e) + \max_{e \in P}\!{(1 / c(e))}$. For a spanning tree $T$, let $P_{u,v}^T$ be the unique $u$--$v$ path in $T$. The {\sc quickest radius spanning tree problem} is to find a spanning tree $T$ of $G$ such that $\max _{v \in V} t(P^T_{root,v})$ is minimized. In this paper we present a 2-approximation algorithm for this problem, and show that unless $P =NP$, there is no approximation algorithm with a performance guarantee of $2 - \epsilon$ for any $\epsilon >0$. The {\sc quickest diameter spanning tree problem} is to find a spanning tree $T$ of $G$ such that $\max_{u,v \in V} t(P^T_{u,v})$ is minimized. We present a ${3 \over 2}$-approximation to this problem, and prove that unless $P=NP$ there is no approximation algorithm with a performance guarantee of ${3 \over 2}-\epsilon$ for any $\epsilon >0$.     

Approximation Algorithms for a Capacitated Network Design Problem

We study a capacitated network design problem with applications in local access network design. Given a network, the problem is to route flow from several sources to a sink and to install capacity on the edges to support the flow at minimum cost. Capacity can be purchased only in multiples of a fixed quantity. All the flow from a source must be routed in a single path to the sink. This NP-hard problem generalizes the Steiner tree problem and also more effectively models the applications traditionally formulated as capacitated tree problems. We present an approximation algorithm with performance ratio (ρ_ST + 2) where ρ_ST is the performance ratio of any approximation algorithm for the minimum Steiner tree problem. When all sources have unit demand, the ratio improves to ρ_ST + 1) and, in particular, to 2 when all nodes in the graph are sources.     

Partitioning a Square into Rectangles: NP-Completeness and Approximation Algorithms

   Abstract. In this paper we deal with two geometric problems arising from heterogeneous parallel computing: how to partition the unit square into p rectangles of given area s ₁ , s ₂ , . . . ,s _p (such that Σ _i=1 ^p s _i = 1 ), so as to minimize either (i) the sum of the p perimeters of the rectangles or (ii) the largest perimeter of the p rectangles? For both problems, we prove NP-completeness and we introduce a 7/4 -approximation algorithm for (i) and a

Approximation Algorithms for a Capacitated Network Design Problem

We study a capacitated network design problem with applications in local access network design. Given a network, the problem is to route flow from several sources to a sink and to install capacity on the edges to support the flow at minimum cost. Capacity can be purchased only in multiples of a fixed quantity. All the flow from a source must be routed in a single path to the sink. This NP-hard problem generalizes the Steiner tree problem and also more effectively models the applications traditionally formulated as capacitated tree problems. We present an approximation algorithm with performance ratio (_ST + 2) where _ST is the performance ratio of any approximation algorithm for the minimum Steiner tree problem. When all sources have unit demand, the ratio improves to _ST + 1) and, in particular, to 2 when all nodes in the graph are sources.     

Approximation Algorithms for the Fixed-Topology Phylogenetic Number Problem

In the l -phylogeny problem, one wishes to construct an evolutionary tree for a set of species represented by characters, in which each state of each character induces no more than l connected components. We consider the fixed-topology version of this problem for fixed-topologies of arbitrary degree. This version of the problem is known to be NP -complete for l ≥ 3 even for degree-3 trees in which no state labels more than l+1 leaves (and therefore there is a trivial l + 1 phylogeny). We give a 2 -approximation algorithm for all l ≥ 3 for arbitrary input topologies and we give an optimal approximation algorithm that constructs a 4 -phylogeny when a 3 -phylogeny exists. Dynamic programming techniques, which are typically used in fixed-topology problems, cannot be applied to l -phylogeny problems. Our 2 -approximation algorithm is the first application of linear programming to approximation algorithms for phylogeny problems. We extend our results to a related problem in which characters are polymorphic. Received June 9, 1997; revised March 13, 1998.     

Approximation algorithms for the Fault-Tolerant Facility Placement problem

In the Fault-Tolerant Facility Placement problem (FTFP) we are given a set of customers C, a set of sites F, and distances between customers and sites. We assume that the distances satisfy the triangle inequality. Each customer j has a demand rj and each site may open an unbounded number of facilities. The objective is to minimize the total cost of opening facilities and connecting each customer j to rj different open facilities. We present two LP-rounding algorithms for FTFP. The first algorithm achieves an approximation ratio of 4. The second algorithm uses the method of filtering to improve the ratio to 3.16.     

基于遗传算法学习聚类算法的中心个数

无导师聚类算法的目标是将一个数据集划分为若干个类,使得类内相似性尽可能大且类间相似性尽可能小。聚类过程中对数据集合分割成多少个类是一个很难确定的问题,目前还没有较好的解决方法。文章使用遗传算法对无导师聚类K-均值(K-means)算法中中心个数K值进行学习,实现了使用遗传算法进行聚类中心个数的确定,旨在提供一种选择中心参数个数的方法。通过对UCI机器学习数据库中的7个数据库进行实验,证实此方法是比较有效的。     

Experimental Analysis of Approximation Algorithms for the Vertex Cover and Set Covering Problems

Several approximation algorithms with proven performance guarantees have been proposed to find approximate solutions to classical combinatorial optimization problems. However, theoretical results may not reflect the experimental performance of the proposed algorithms. As a consequence, a question arises: how “far” from the theoretically proved performance are the experimental results? We conduct a controlled empirical study of approximation algorithms for the Vertex Cover and the Set Covering Problems. Many authors have proposed approximation algorithms for those problems. Our main goal is to better understand their strengths, weaknesses, and operation. Although we implement more than one algorithm to find feasible solutions to either problems, this work does not emphasize competition between them. The quality of the solutions related to the theoretical performance guarantees are analyzed instead. The computational experiments showed that the proven performance guarantees of all tested algorithms did not forecast well the empirical performance.     

Linear-Time Approximation Schemes for Scheduling Malleable Parallel Tasks

Abstract. A malleable parallel task is one whose execution time is a function of the number of (identical) processors alloted to it. We study the problem of scheduling a set of n independent malleable tasks on a fixed number of parallel processors, and propose an approximation scheme that for any fixed ε > 0 , computes in O(n) time a non-preemptive schedule of length at most (1+ε) times the optimum.     

挑选聚类算法的网格连通图方法

每一种聚类算法都有其适合处理的特定分布的数据集.为了给未知分布数据集挑选合适的聚类算法,提出了一种挑选聚类算法的网格连通图方法 SCGG.SCGG通过对数据潜在类结构的分析,若含有环形结构类则选择层次聚类的单连接算法对数据聚类,否则选择k-means算法.实验显示该方法十分的有效,能够挑选到合适的聚类算法对数据聚类.