A simple approximation algorithm for the weighted matching problem

We present a linear time approximation algorithm with a performance ratio of 1/2 for finding a maximum weight matching in an arbitrary graph. Such a result is already known and is due to Preis [STACS'99, Lecture Notes in Comput. Sci., Vol. 1563, 1999, pp. 259-269]. Our algorithm uses a new approach which is much simpler than the one given by Preis and needs no amortized analysis for its running time.     

A polynomial-time approximation scheme for the two-machine flow shop scheduling problem with an availability constraint

We study the problem of scheduling n preemptable jobs in a two-machine flow shop where the first machine is not available for processing during a given time interval. The objective is to minimize the makespan. We propose a polynomial-time approximation scheme for this problem. The approach is extended to solve the problem in which the second machine is not continuously available.     

A note on the greedy algorithm for the unsplittable flow problem

In a recent paper Chekuri and Khanna improved the analysis of the greedy algorithm for the edge disjoint paths problem and proved the same bounds also for the related uniform capacity unsplittable flow problem. Here we show that their ideas can be used to get the same approximation ratio even for the more general Unsplittable Flow Problem with nonuniform edge capacities.     

A note on graph balancing problems with restrictions

We consider the graph balancing problem of providing orientations to edges in an undirected multi-graph to minimize the maximum load. We first obtain an FPTAS when the multi-graph is restricted to a tree. We also obtain some additional results for other restricted cases by showing equivalencies with related combinatorial problems.     

An approximation algorithm dependent on edge-coloring number for minimum maximal matching problem

We consider the minimum maximal matching problem, which is NP-hard (Yannakakis and Gavril (1980) [18]). Given an unweighted simple graph G=(V,E), the problem seeks to find a maximal matching of minimum cardinality. It was unknown whether there exists a non-trivial approximation algorithm whose approximation ratio is less than 2 for any simple graph. Recently, Z. Gotthilf et al. (2008) [5] presented a -approximation algorithm, where c is an arbitrary constant.In this paper, we present a -approximation algorithm based on an LP relaxation, where χ^′(G) is the edge-coloring number of G. Our algorithm is the first non-trivial approximation algorithm whose approximation ratio is independent of |V|. Moreover, it is known that the minimum maximal matching problem is equivalent to the edge dominating set problem. Therefore, the edge dominating set problem is also -approximable. From edge-coloring theory, the approximation ratio of our algorithm is , where Δ(G) represents the maximum degree of G. In our algorithm, an LP formulation for the edge dominating set problem is used. Fujito and Nagamochi (2002) [4] showed the integrality gap of the LP formulation for bipartite graphs is at least . Moreover, χ^′(G) is Δ(G) for bipartite graphs. Thus, as far as an approximation algorithm for the minimum maximal matching problem uses the LP formulation, we believe our result is the best possible.     

A logarithmic approximation algorithm for the minimum energy consumption broadcast subgraph problem

Motivated by the problem of supporting energy-efficient broadcasting in ad hoc wireless networks, we study the Minimum Energy Consumption Broadcast Subgraph (MECBS) problem. We present the first logarithmic approximation algorithm for the problem which uses an interesting reduction to Node-Weighted Connected Dominating Set.     

An improved approximation ratio for the minimum linear arrangement problem

We observe that combining the techniques of Arora, Rao, and Vazirani, with the rounding algorithm of Rao and Richa yields an -approximation for the minimum-linear arrangement problem. This improves over the O(logn)-approximation of Rao and Richa.     

An improved approximation algorithm for the metric maximum clustering problem with given cluster sizes

The input to the metric maximum clustering problem with given cluster sizes consists of a complete graph G=(V,E) with edge weights satisfying the triangle inequality, and integers c₁,…,cp. The goal is to find a partition of V into disjoint clusters of sizes c₁,…,cp, maximizing the sum of weights of edges whose two ends belong to the same cluster. We describe an approximation algorithms for this problem with performance guarantee that approaches 0.5 when the cluster sizes are large.     

A note on the approximation of mean-payoff games

We consider the problem of designing approximation schemes for the values of mean-payoff games. It was recently shown that (1) mean-payoff with rational weights scaled on [−1,1]$[-1,1]$ admit additive fully-polynomial approximation schemes, and (2) mean-payoff games with positive weights admit relative fully-polynomial approximation schemes. We show that the problem of designing additive/relative approximation schemes for general mean-payoff games (i.e. with no constraint on their edge-weights) is P-time equivalent to determining their exact solution.     

An improved approximation algorithm for two-machine flow shop scheduling with an availability constraint

We study the problem of scheduling n jobs in a two-machine flow shop where the second machine is not available for processing during a given time interval. A resumable scenario is assumed, i.e., if a job cannot be finished before the down period it is continued after the machine becomes available again. The objective is to minimize the makespan. The best fast approximation algorithm for this problem guarantees a relative worst-case error bound of 4/3. We present an improved algorithm with a relative worst-case error bound of 5/4.     

An approximation algorithm for sequential rectangle placement

We present a constant factor, polynomial time approximation algorithm for the problem of scheduling a sequence of rectangles on a matrix. The approximation is on the area covered by the rectangles, and a rectangle is placed on the matrix only if all its preceding rectangles in the sequence were already placed.     

A note on improving the performance of approximation algorithms for radiation therapy

The segment minimization problem consists of representing an integer matrix as the sum of the fewest number of integer matrices each of which have the property that the non-zeroes in each row are consecutive. This has direct applications to an effective form of cancer treatment. Using several insights, we extend previous results to obtain constant-factor improvements in the approximation guarantees. We show that these improvements yield better performance by providing an experimental evaluation of all known approximation algorithms using both synthetic and real-world clinical data. Our algorithms are superior for 76% of instances and we argue for their utility alongside the heuristic approaches used in practice.     

A load-balanced parallel sorting algorithm for shared-nothing architectures

With the popularity of parallel database machines based on the shared-nothing architecture, it has become important to find external sorting algorithms which lead to a load-balanced computation, i.e., balanced execution, communication and output. If during the course of the sorting algorithm each processor is equally loaded, parallelism is fully exploited. Similarly, balanced communication will not congest the network traffic. Since sorting can be used to support a number of other relational operations (joins, duplicate elimination, building indexes etc.) data skew produced by sorting can further lead to execution skew at later stages of these operations. In this paper we present a load-balanced parallel sorting algorithm for shared-nothing architectures. It is a multiple-input multiple-output algorithm with four stages, based on a generalization of Batcher's odd-even merge. At each stage then keys are evenly distributed among thep processors (i.e., there is no final sequential merge phase) and the distribution of keys between stages ensures against network congestion. There is no assumption made on the key distribution and the algorithm performs equally well in the presence of duplicate keys. Hence our approach always guarantees its performance, as long asn is greater thanp ³, which is the case of interest for sorting large relations. In addition, processors can be added incrementally. Recommended by: Patrick Valduriez     

A randomized approximation algorithm for computing bucket orders

We show that a simple randomized algorithm has an expected constant factor approximation guarantee for fitting bucket orders to a set of pairwise preferences.     

An adjustable linear time parallel algorithm for maximum weight bipartite matching

We present a parallel algorithm for finding a maximum weight matching in general bipartite graphs with an adjustable time complexity of using O(nmax(2ω,4+ω)) processing elements for ω?1. Parameter ω is not bounded. This is the fastest known strongly polynomial parallel algorithm to solve this problem. This is also the first adjustable parallel algorithm for the maximum weight bipartite matching problem in which the execution time can be reduced by an unbounded factor. We also present a general approach for finding efficient parallel algorithms for the maximum matching problem.     

An approximation algorithm for the three-machine scheduling problem with the routes given by the same partial order

The paper considers a three-machine shop scheduling problem to minimize the makespan, in which the route of a job should be feasible with respect to a machine precedence digraph with three nodes and one arc. For this NP-hard problem that is related to the classical flow shop and open shop models, we present a simple 1.5-approximation algorithm and an improved 1.4-approximation algorithm.     

An improved lower bound on approximation algorithms for the Closest Substring problem

The Closest Substring problem (the CSP problem) is a basic NP-hard problem in the study of computational biology. It is known that the problem has polynomial time approximation schemes. In this paper, we prove that unless the Exponential Time Hypothesis fails, the CSP problem has no polynomial time approximation schemes of running time f(1/ε)n^o(1/ε) for any function f. This essentially excludes the possibility that the CSP problem has a practical polynomial time approximation scheme even for moderate values of the error bound ε. As a consequence, it is unlikely that the study of approximation schemes for the CSP problem in the literature would lead to practical approximation algorithms for the problem for small error bound ε.     

On approximation algorithms for the terminal Steiner tree problem

The terminal Steiner tree problem is a special version of the Steiner tree problem, where a Steiner minimum tree has to be found in which all terminals are leaves. We prove that no polynomial time approximation algorithm for the terminal Steiner tree problem can achieve an approximation ratio less than (1−o(1))lnn unless NP has slightly superpolynomial time algorithms. Moreover, we present a polynomial time approximation algorithm for the metric version of this problem with a performance ratio of 2ρ, where ρ denotes the best known approximation ratio for the Steiner tree problem. This improves the previously best known approximation ratio for the metric terminal Steiner tree problem of ρ+2.