Showing papers on "Approximation algorithm published in 1997"

Approximation algorithms for facility location problems (extended abstract)

Abstract: We present new approximation algorithms for several facility location problems. In each facility location problem that we study, there is a set of locations at which we may build a facility (such as a warehouse), where the cost of building at location i is fi; furthermore, there is a set of client locations (such as stores) that require to be serviced by a facility, and if a client at location j is assigned to a facility at location i, a cost of cij is incurred that is proportional to the distance between i and j. The objective is to determine a set of locations at which to open facilities so as to minimize the total facility and assignment costs. In the uncapacitated case, each facility can service an unlimited number of clients, whereas in the capacitated case, each facility can serve, for example, at most u clients. These models and a number of closely related ones have been studied extensively in the Operations Research literature. We shall consider the case in which the distances between locations are non-negative, symmetric and satisfy the triangle inequality. For the uncapacitated facility location, we give a polynomial-time algorithm that finds a solution of cost within a factor of 3.16 of the optimal. This is the first constant performance guarantee known for this problem. We also present approximation algorithms with constant performance guarantees for a number of capacitated models as well as a generalization in which there is a 2-level hierarchy of facilities. Our results are based on the filtering and rounding technique of Lin & Vitter. We also give a randomized variant of this technique that can then be derandomized to yield improved deterministic performance guarantees. shmoys@cs.cornell.edu. School of Operations Research & Industrial Engineering and Department of Computer Science, Cornell University, Ithaca, NY 14853. Research partially supported by NSF grants CCR-9307391 and DMS-9505155 and ONR grant N00014-96-1-0050O. yeva@cs.cornell.edu. Department of Computer Science and School of Operations Research & Industrial Engineering, Cornell University, Ithaca, NY 14853. Research partially supported by NSF grants DMI-9157199 and DMS-9505155 and ONR grant N00014-96-1-0050O. zaardal@cs.ruu.nl. Department of Computer Science, Utrecht University, Utrecht, The Netherlands. Research partially supported by NSF grant CCR-9307391, and by ESPRIT Long Term Research Project No. 20244 (project ALCOM-IT: Algorithms and Complexity in Information Technology).

Scheduling to minimize average completion time: off-line and on-line approximation algorithms

Abstract: In this paper we introduce two general techniques for the design and analysis of approximation algorithms for NP-hard scheduling problems in which the objective is to minimize the weighted sum of the job completion times. For a variety of scheduling models, these techniques yield the first algorithms that are guaranteed to find schedules that have objective function value within a constant factor of the optimum. In the first approach, we use an optimal solution to a linear programming relaxation in order to guide a simple list-scheduling rule. Consequently, we also obtain results about the strength of the relaxation. Our second approach yields on-line algorithms for these problems: in this setting, we are scheduling jobs that continually arrive to be processed and, for each time t, we must construct the schedule until time t without any knowledge of the jobs that will arrive afterwards. Our on-line technique yields constant performance guarantees for a variety of scheduling environments, and in some cases essentially matches the performance of our off-line LP-based algorithms.

Improved approximation algorithms for MAX k-CUT and MAX BISECTION

Abstract: Polynomial-time approximation algorithms with nontrivial performance guarantees are presented for the problems of (a) partitioning the vertices of a weighted graph intok blocks so as to maximize the weight of crossing edges, and (b) partitioning the vertices of a weighted graph into two blocks of equal cardinality, again so as to maximize the weight of crossing edges. The approach, pioneered by Goemans and Williamson, is via a semidefinite programming relaxation.

Primal-dual approximation algorithms for integral flow and multicut in trees

Abstract: We study the maximum integral multicommodity flow problem and the minimum multicut problem restricted to trees. This restriction is quite rich and contains as special cases classical optimization problems such as matching and vertex cover for general graphs. It is shown that both the maximum integral multicommodity flow and the minimum multicut problem are NP-hard and MAX SNP-hard on trees, although the maximum integral flow can be computed in polynomial time if the edges have unit capacity. We present an efficient algorithm that computes a multicut and integral flow such that the weight of the multicut is at most twice the value of the flow. This gives a 2-approximation algorithm for minimum multicut and a 1/2-approximation algorithm for maximum integral multicommodity flow in trees.

Algorithms for Vertex Partitioning Problems on Partial k -Trees

Abstract: In this paper, we consider a large class of vertex partitioning problems and apply to them the theory of algorithm design for problems restricted to partial k-trees. We carefully describe the details of algorithms and analyze their complexity in an attempt to make the algorithms feasible as solutions for practical applications. We give a precise characterization of vertex partitioning problems, which include domination, coloring and packing problems, and their variants. Several new graph parameters are introduced as generalizations of classical parameters. This characterization provides a basis for a taxonomy of a large class of problems, facilitating their common algorithmic treatment and allowing their uniform complexity classification. We present a design methodology of practical solution algorithms for generally $\NP$-hard problems when restricted to partial k-trees (graphs with treewidth bounded by k). This "practicality" accounts for dependency on the parameter k of the computational complexity of the resulting algorithms. By adapting the algorithm design methodology on partial k-trees to vertex partitioning problems, we obtain the first algorithms for these problems with reasonable time complexity as a function of treewidth. As an application of the methodology, we give the first polynomial-time algorithm on partial k-trees for computation of the Grundy number.

On approximating the longest path in a graph

Abstract: We consider the problem of approximating the longest path in undirected graphs. In an attempt to pin down the best achievable performance ratio of an approximation algorithm for this problem, we present both positive and negative results. First, a simple greedy algorithm is shown to find long paths in dense graphs. We then consider the problem of finding paths in graphs that are guaranteed to have extremely long paths. We devise an algorithm that finds paths of a logarithmic length in Hamiltonian graphs. This algorithm works for a much larger class of graphs (weakly Hamiltonian), where the result is the best possible. Since the hard case appears to be that of sparse graphs, we also consider sparse random graphs. Here we show that a relatively long path can be obtained, thereby partially answering an open problem of Broderet al. To explain the difficulty of obtaining better approximations, we also prove hardness results. We show that, for any ź 0, finding an approximation of rationź is alsoNP-hard. As evidence toward this conjecture, we show that if any polynomial-time algorithm can approximate the longest path to a ratio of $$2^{O(\log ^{1 - \varepsilon } n)} $$ , for any ź>0, thenNP has a quasi-polynomial deterministic time simulation. The hardness results apply even to the special case where the input consists of bounded degree graphs.

Greed is good: Approximating independent sets in sparse and bounded-degree graphs

Abstract: Theminimum-degree greedy algorithm, or Greedy for short, is a simple and well-studied method for finding independent sets in graphs. We show that it achieves a performance ratio of (Δ+2)/3 for approximating independent sets in graphs with degree bounded by Δ. The analysis yields a precise characterization of the size of the independent sets found by the algorithm as a function of the independence number, as well as a generalization of Turan's bound. We also analyze the algorithm when run in combination with a known preprocessing technique, and obtain an improved $$(2\bar d + 3)/5$$ performance ratio on graphs with average degree $$\bar d$$ , improving on the previous best $$(\bar d + 1)/2$$ of Hochbaum. Finally, we present an efficient parallel and distributed algorithm attaining the performance guarantees of Greedy.

A 7/8-approximation algorithm for MAX 3SAT?

Abstract: We describe a randomized approximation algorithm which takes an instance of MAX 3SAT as input. If the instance-a collection of clauses each of length at most three-is satisfiable, then the expected weight of the assignment found is at least 7/8 of optimal. We provide strong evidence (but not a proof) that the algorithm performs equally well on arbitrary MAX 3SAT instances. Our algorithm uses semidefinite programming and may be seen as a sequel to the MAX CUT algorithm of Goemans and Williamson (1995) and the MAX 2SAT algorithm of Feige and Goemans (1995). Though the algorithm itself is fairly simple, its analysis is quite complicated as it involves the computation of volumes of spherical tetrahedra. Hastad has recently shown that, assuming P/spl ne/NP, no polynomial-time algorithm for MAX 3SAT can achieve a performance ratio exceeding 7/8, even when restricted to satisfiable instances of the problem. Our algorithm is therefore optimal in this sense. We also describe a method of obtaining direct semidefinite relaxations of any constraint satisfaction problem of the form MAX CSP(F), where F is a finite family of Boolean functions. Our relaxations are the strongest possible within a natural class of semidefinite relaxations.

Short Shop Schedules

Abstract: We consider the open shop, job shop, and flow shop scheduling problems with integral processing times. We give polynomial-time algorithms to determine if an instance has a schedule of length at most 3, and show that deciding if there is a schedule of length at most 4 is 𝒩𝒫-complete. The latter result implies that, unless 𝒫 = 𝒩𝒫, there does not exist a polynomial-time approximation algorithm for any of these problems that constructs a schedule with length guaranteed to be strictly less than 5/4 times the optimal length. This work constitutes the first nontrivial theoretical evidence that shop scheduling problems are hard to solve even approximately.

Buy-at-bulk network design

Abstract: The essence of the simplest buy-at-bulk network design problem is buying network capacity "wholesale" to guarantee connectivity from all network nodes to a certain central network switch. Capacity is sold with "volume discount": the more capacity is bought, the cheaper is the price per unit of bandwidth. We provide O(log/sup 2/n) randomized approximation algorithm for the problem. This solves the open problem in Salman et al. (1997). The only previously known solutions were restricted to special cases (Euclidean graphs). We solve additional natural variations of the problem, such as multi-sink network design, as well as selective network design. These problems can be viewed as generalizations of the the Generalized Steiner Connectivity and Prize-collecting salesman (K-MST) problems. In the selective network design problem, some subset of /spl kappa/ wells must be connected to the (single) refinery, so that the total cost is minimized.

Approximation techniques for average completion time scheduling

Abstract: We consider the problem of nonpreemptive scheduling to minimize average (weighted) completion time, allowing for release dates, parallel machines, and precedence constraints. Recent work has led to constant-factor approximations for this problem, based on solving a preemptive or linear programming relaxation and then using the solution to get an ordering on the jobs. We introduce several new techniques which generalize this basic paradigm. We use these ideas to obtain improved approximation algorithms for one-machine scheduling to minimize average completion time with release dates. In the process, we obtain an optimal randomized on-line algorithm for the same problem that beats a lower bound for deterministic on-line algorithms. We consider extensions to the case of parallel machine scheduling, and for this we introduce two new ideas: first, we show that a preemptive one-machine relaxation is a powerful tool for designing parallel machine scheduling algorithms that simultaneously produce good approximations and have small running times; second, we show that a non-greedy {open_quotes}rounding{close_quotes} of the relaxation yields better approximations than a greedy one. We also prove a general theorem relating the value of one-machine relaxations to that of the schedules obtained for the original m-machine problems. This theorem applies even when there are precedencemore » constraints on the jobs. We apply this result to precedence graphs such as in-trees, out-trees, and series- parallel graphs; these are of particular interest in compiler applications that partly motivated our work.« less

Approximating total flow time on parallel machines

Abstract: We consider the problem of optimizing the total flow time of a stream of jobs that are released over time in a multiprocessor setting. This problem is NP-hard even when there are only two machines and preemption is allowed. Although the total (or average) flow time is widely accepted as a good measurement of the overall quality of service, no approximation algorithms were known for this basic scheduling problem. This paper contains two main results. We first prove that when preemption is allowed, Shortest Remaining Processing Time (SRPT) is an O(log(min{nm,P})) approximation algorithm for the total flow time, where n is the number of jobs, m is the number of machines, and P is the ratio between the maximum and the minimum processing time of a job. We also provide an @W(log(nm+P)) lower bound on the (worst case) competitive ratio of any randomized algorithm for the on-line problem in which jobs are known at their release times. Thus, we show that up to a constant factor SRPT is an optimal on-line algorithm. Our second main result addresses the non-preemptive case. We present a general technique that allows to transform any preemptive solution into a non-preemptive solution at the expense of an O(nm) factor in the approximation ratio of the total flow time. Combining this technique with our previous result yields an O(nmlognm) approximation algorithm for this case. We also show an @W(n^1^3^-^@e) lower bound on the approximability of this problem (assuming P NP).

New Approximation Algorithms for the Steiner Tree Problems

Abstract: The Steiner tree problem asks for the shortest tree connecting a given set of terminal points in a metric space. We design new approximation algorithms for the Steiner tree problems using a novel technique of choosing Steiner points in dependence on the possible deviation from the optimal solutions. We achieve the best up to now approximation ratios of 1.644 in arbitrary metric and 1.267 in rectilinear plane, respectively.

Optimal broadcast scheduling in packet radio networks using mean field annealing

Abstract: We present an efficient broadcast scheduling algorithm based on mean field annealing (MFA) neural networks Packet radio (PR) is a technology that applies the packet switching technique to the broadcast radio environment In a PR network, a single high-speed wideband channel is shared by all PR stations When a time-division multi-access protocol is used, the access to the channel by the stations' transmissions must be properly scheduled in both the time and space domains in order to avoid collisions or interferences It is proven that such a scheduling problem is NP-complete Therefore, an efficient polynomial algorithm rarely exists, and a mean field annealing-based algorithm is proposed to schedule the stations' transmissions in a frame consisting of certain number of time slots Numerical examples and comparisons with some existing scheduling algorithms have shown that the proposed scheme can find near-optimal solutions with reasonable computational complexity Both time delay and channel utilization are calculated based on the found schedules

Nearly linear time approximation schemes for Euclidean TSP and other geometric problems

Abstract: We present a randomized polynomial time approximation scheme for Euclidean TSP in R/sup 2/ that is substantially more efficient than our earlier scheme (1996) (and the scheme of Mitchell (1996)). For any fixed c>1 and any set of n nodes in the plane, the new scheme finds a (1+1/c)-approximation to the optimum traveling salesman tour in O(n(logn)/sup O(c)/) time. (Our earlier scheme ran in n/sup O(C)/ time.) For points in R/sup d/ the algorithm runs in O(n(logn)/sup (O(/spl radic/dc)/d-1)) time. This time is polynomial (actually nearly linear) for every fixed c, d. Designing such a polynomial-time algorithm was an open problem (our earlier algorithm (1996) ran in superpolynomial time for d/spl ges/3). The algorithm generalizes to the same set of Euclidean problems handled by the previous algorithm, including Steiner Tree, /spl kappa/-TSP, /spl kappa/-MST, etc, although for /spl kappa/-TSP and /spl kappa/-MST the running time gets multiplied by /spl kappa/. We also use our ideas to design nearly-linear time approximation schemes for Euclidean versions of problems that are known to be in P, such as Minimum Spanning Tree and Min Cost Perfect Matching. All our algorithms can be derandomized, though the running time then increases by O(n/sup d/) in R/sup d/. They also have simple parallel implementations (say, in NC/sup 2/).

Allocating bandwidth for bursty connections

Abstract: In this paper, we undertake the first study of statistical multiplexing from the perspective of approximation algorithms. The basic issue underlying statistical multiplexing is the following: in high-speed networks, individual connections (i.e., communication sessions) are very bursty, with transmission rates that vary greatly over time. As such, the problem of packing multiple connections together on a link becomes more subtle than in the case when each connection is assumed to have a fixed demand. We consider one of the most commonly studied models in this domain: that of two communicating nodes connected by a set of parallel edges, where the rate of each connection between them is a random variable. We consider three related problems: (1) stochastic load balancing, (2) stochastic bin-packing, and (3) stochastic knapsack. In the first problem the number of links is given and we want to minimize the expected value of the maximum load. In the other two problems the link capacity and an allowed overflow probability p are given, and the objective is to assign connections to links, so that the probability that the load of a link exceeds the link capacity is at most p. In binpacking we need to assign each connection to a link using as few links as possible. In the knapsack problem each connection has a value, and we have only one link. The problem is to accept as many connections as possible. For the stochastic load balancing problem we give an O(1)-approximation algorithm for arbitrary random variables. For the other two problems we have algorithms restricted to on-off sources (the most common special case studied in the statistical multiplexing literature), with a somewhat weaker range of performance guarantees. A standard approach that has emerged for dealing with probabilistic resource requirements is the notion of effective bandwidth—this is a means of associating a fixed demand with a bursty connection that “represents” its distribution as closely as possible. Our approximation algorithms make use of the standard definition of effective bandwidth and also a new one that we introduce; the performance guarantees are based on new results showing that a combination of these measures can be used to provide bounds on the optimal solution.

Maximum Agreement Subtree in a Set of Evolutionary Trees: Metrics and Efficient Algorithms

Abstract: The maximum agreement subtree approach is one method of reconciling different evolutionary trees for the same set of species. An agreement subtree enables choosing a subset of the species for whom the restricted subtree is equivalent (under a suitable definition) in all given evolutionary trees. Recently, dynamic programming ideas were used to provide polynomial time algorithms for finding a maximum homeomorphic agreement subtree of two trees. Generalizing these methods to sets of more than two trees yields algorithms that are exponential in the number of trees. Unfortunately, it turns out that in reality one is usually presented with more than two trees, sometimes as many as thousands of trees. In this paper we prove that the maximum homeomorphic agreement subtree problem is $\cal{NP}$-complete for three trees with unbounded degrees. We then show an approximation algorithm of time O(kn5) for choosing the species that are not in a maximum agreement subtree of a set of k trees. Our approximation is guaranteed to provide a set that is no more than 4 times the optimum solution. While the set of evolutionary trees may be large in practice, the trees usually have very small degrees, typically no larger than three. We develop a new method for finding a maximum agreement subtree of k trees, of which one has degree bounded by d. This new method enables us to find a maximum agreement subtree in time O(knd + 1+ n2d).

Improved approximation algorithms for unsplittable flow problems

Abstract: In the single-source unsplittable flow problem we are given a graph G, a source vertex s and a set of sinks t/sub 1/, ..., t/sub k/ with associated demands. We seek a single s-t/sub i/ flow path for each commodity i so that the demands are satisfied and the total flow routed across any edge e is bounded by its capacity c/sub e/. The problem is an NP-hard variant of max flow and a generalization of single-source edge-disjoint paths with applications to scheduling, load balancing and virtual-circuit routing problems. In a significant development, Kleinberg gave recently constant-factor approximation algorithms for several natural optimization versions of the problem. In this paper we give a generic framework, that yields simpler algorithms and significant improvements upon the constant factors. Our framework, with appropriate subroutines applies to all optimization versions previously considered and treats in a unified manner directed and undirected graphs.

A series of approximation algorithms for the acyclic directed steiner tree problem

Abstract: Given an acyclic directed network, a subsetS of nodes (terminals), and a rootr, theacyclic directed Steiner tree problem requires a minimum-cost subnetwork which contains paths fromr to each terminal. It is known that unlessNP⊆DTIME[npolylogn] no polynomial-time algorithm can guarantee better than (lnk)/4-approximation, wherek is the number of terminals. In this paper we give anO(kź)-approximation algorithm for any ź>0. This result improves the previously knownk-approximation.

Comparative study of stochastic algorithms for system optimization based on gradient approximations

Abstract: Stochastic approximation (SA) algorithms can be used in system optimization problems for which only noisy measurements of the system are available and the gradient of the loss function is not. This type of problem can be found in adaptive control, neural network training, experimental design, stochastic optimization, and many other areas. This paper studies three types of SA algorithms in a multivariate Kiefer-Wolfowitz setting, which uses only noisy measurements of the loss function (i.e., no loss function gradient measurements). The algorithms considered are: the standard finite-difference SA (FDSA) and two accelerated algorithms, the random directions SA (RDSA) and the simultaneous-perturbation SA (SPSA). RDSA and SPSA use randomized gradient approximations based on (generally) far fewer function measurements than FDSA in each Iteration. This paper describes the asymptotic error distribution for a class of RDSA algorithms, and compares the RDSA, SPSA, and FDSA algorithms theoretically (using mean-square errors computed from asymptotic distributions) and numerically. Based on the theoretical and numerical results, SPSA is the preferable algorithm to use.

Global optimization using local information with applications to flow control

Abstract: Flow control in high speed networks requires distributed routers to make fast decisions based only on local information in allocating bandwidth to connections. While most previous work on this problem focuses on achieving local objective functions, in many cases it may be necessary to achieve global objectives such as maximizing the total flow. This problem illustrates one of the basic aspects of distributed computing: achieving global objectives using local information. Papadimitriou and Yannakakis (1993) initiated the study of such problems in a framework of solving positive linear programs by distributed agents. We take their model further, by allowing the distributed agents to acquire more information over time. We therefore turn attention to the tradeoff between the running time and the quality of the solution to the linear program. We give a distributed algorithm that obtains a (1+/spl epsiv/) approximation to the global optimum solution and runs in a polylogarithmic number of distributed rounds. While comparable in running time, our results exhibit a significant improvement on the logarithmic ratio previously obtained by Awerbuch and Azar (1994). Our algorithm, which draws from techniques developed by Luby and Nisan (1993) is considerably simpler than previous approximation algorithms for positive linear programs, and thus may have practical value in both centralized and distributed settings.

A survey of approximately optimal solutions to some covering and packing problems

Abstract: We survey approximation algorithms for some well-known and very natural combinatorial optimization problems, the minimum set covering, the minimum vertex covering, the maximum set packing, and maximum independent set problems; we discuss their approximation performance and their complexity. For already known results, any time we have conceived simpler proofs than those already published, we give these proofs, and, for the rest, we cite the simpler published ones. Finally, we discuss how one can relate the approximability behavior (from both a positive and a negative point of view) of vertex covering to the approximability behavior of a restricted class of independent set problems.