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检索条件"主题词=approximation algorithms"

共 31877 条记录，以下是241-250 订阅

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approximation algorithms for Cellular Networks Planning with Relay Nodes

Approximation Algorithms for Cellular Networks Planning with...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Wang, Shaowei Zhao, Wentao Wang, Chonggang Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Jiangsu Peoples R China InterDigital Commun Corp King Of Prussia PA 19406 USA

ISBN: (纸本)9781467359399;9781467359382

Relay nodes are introduced to the next generation cellular networks to enhance coverage and improve system capacity, leading to a new radio network planning paradigm. In this paper, we study two planning problems for cellular networks with relay nodes: Minimum cost cell planning and budgeted cell planning. The former is to minimize the total installation cost for opening base stations (BSs), including macro BSs and relay nodes, while satisfying all users' traffic demands. The latter is to maximize the number of users with predefined traffic demands under a given budget. Both of the problems are NP-hard. We present approximation algorithms to work out promising solutions to these problems. For the minimum cost cell planning, we develop an O(log W)-approximation algorithm, where W is the maximum capacity of macro BSs. For the budgeted cell planning, we prove that the problem is NP-hard to approximate and give an e-1/3e-1-approximation algorithm for a special case of the problem, which is general enough to meet practical requirements.

关键词： approximation algorithms Cellular networks relay Nondeterministic polynomial-time hard Trunk Node Ganglia system capacity Next generation networks

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approximation algorithms for survivable multicommodity flow problems with applications to network design

Approximation algorithms for survivable multicommodity flow ...

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IEEE INFOCOM 2006 Conference/25th IEEE International Conference on Computer Communications

作者： Todimala, Ajay Ramamurthy, Byrav Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA

ISBN: (纸本)9781424402212

Multicommodity flow (MF) problems have a wide variety of applications in areas such as VLSI circuit design, network design, etc., and are therefore very well studied. The fractional MF problems are polynomial time solvable while integer versions are NP-complete. However, exact algorithms to solve the fractional MF problems have high computational complexity. Therefore approximation algorithms to solve the fractional MF problems have been explored in the literature to reduce their computational complexity. Using these approximation algorithms and the randomized rounding technique, polynomial time approximation algorithms have been explored in the literature. In the design of high-speed networks, such as optical wavelength division multiplexing (WDM) networks, providing survivability carries great significance. Survivability is the ability of the network to recover from failures. It further increases the complexity of network design and presents network designers with more formidable challenges. In this work we formulate the survivable versions of the MF problems. We build approximation algorithms for the survivable multicommodity How (SMF) problems based on the framework of the approximation algorithms for the MF problems presented in [1] and [2]. We discuss applications of the SMF problems to solve survivable routing in capacitated networks.

关键词： multicommodity flow linear programming approximation algorithms network design

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approximation algorithms for the watchman route and zookeeper's problems

Approximation algorithms for the watchman route and zookeepe...

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1st CologneTwente Workshop on Graphs and Combinatorial Optimization (CTW2001)

作者： Tan, XH Tokai Univ Sch High Technol Human Welfare Numazu 4100395 Japan

Given a simple polygon P with n vertices and a starting point s on its boundary, the watchman route problem asks for a shortest route in P through s such that each point in the interior of the polygon can be seen from at least one point along the route. In this paper, we present a simple, linear-time algorithm for computing a watchman route of length at most root2 times that of the shortest watchman route. The best known algorithm for computing the shortest watchman route through s takes 0(n 4) time. In addition, it is too complicated to be suitable in practice. Moreover, our approximation scheme can be applied to the zookeeper's problem, which is a variant of the watchman route problem. (C) 2003 Published by Elsevier B.V.

关键词： computational geometry approximation algorithms watchman route problem zookeeper's problem reflection principle

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approximation algorithms for Restless Bandit Problems

Approximation Algorithms for Restless Bandit Problems

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20th Annual ACM-SIAM Symposium on Discrete algorithms

作者： Guha, Sudipto Munagala, Kamesh Shi, Peng Univ Penn Dept Comp & Informat Sci Philadelphia PA 19104 USA Duke Univ Dept Comp Sci Durham NC 27708 USA Duke Univ Durham NC 27708 USA

ISBN: (纸本)9780898716801

In this paper, we consider the restless bandit problem, which is one of the most well-studied generalizations of the celebrated stochastic multi-armed bandit problem in decision theory. In its ultimate generality, the restless bandit problem is known to be PSPACE-Hard to approximate to any non-trivial factor, and little progress has been made on this problem despite its significance in modeling activity allocation under uncertainty. We make progress on this problem by showing that for an interesting and general subclass that we term MONOTONE bandits, a surprisingly simple and intuitive greedy policy yields a factor 2 approximation. Such greedy policies are termed index policies, and are popular due to their simplicity and their optimality for the stochastic multi-armed bandit problem. The MONOTONE bandit problem strictly generalizes the stochastic multi-armed bandit problem, and naturally models multi-project scheduling where the state of a project becomes increasingly uncertain when the project is not scheduled. We develop several novel techniques in the design and analysis of the index policy. Our algorithm proceeds by introducing a novel "balance" constraint to the dual of a well-known LP relaxation to the restless bandit problem. This is followed by a structural characterization of the optimal solution by using both the exact primal as well as dual complementary slackness conditions. This yields an interpretation of the dual variables as potential functions from which we derive the index policy and the associated analysis.

关键词： approximation algorithms

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approximation algorithms for edge-dilation k-center problems 8th

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8th Scandinavian Workshop on Algorithm Theory

作者： Könemann, J Li, YJ Parekh, O Sinha, A Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)3540438661

In an ideal point-to-point network, any node would simply choose a path of minimum latency to send packets to any other node;however, the distributed nature and the increasing size of modern communication networks may render such a solution infeasible, as it requires each node to store global information concerning the network. Thus it may be desirable to endow only a small subset of the nodes with global routing capabilites, which gives rise to the following graph-theoretic problem. Given an undirected graph G = (V, E), a metric iota on the edges, and an integer k, a k-center is a set Pi subset of or equal to V of size k and an assignment pi(v), that maps each node to a unique element in Pi. We let d(pi) (u, v) denote the length of the shortest path from u to v passing through pi(u), and pi(v), and let diota (u, v) be the length of the shortest u, v-path in G. We then refer to dpi (u, v)/diota (u, v) as the stretch of the pair (u, v). We let the stretch of a k-center solution 17 be the maximum stretch of any pair of nodes u, v is an element of V. The minimum edge-dilation k-center problem is that of finding a k-center of minimum stretch. We obtain combinatorial approximation algorithms with constant factor performance guarantees for this problem and variants in which the centers are capacitated or nodes may be assigned to more than one center. We also show that there can be no 5/4 - is an element of approximation for any is an element of > 0 unless P = NP.

关键词： approximation algorithms

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approximation algorithms for Sorting Permutations by Fragmentation-Weighted Operations 5th

Approximation Algorithms for Sorting Permutations by Fragmen...

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5th International Conference on algorithms for Computational Biology (AlCoB)

作者： Alexandrino, Alexsandro Oliveira Lintzmayer, Carla Negri Dias, Zanoni Univ Estadual Campinas UNICAMP Inst Comp Av Albert Einstein 1251 Campinas SP Brazil Fed Univ ABC UFABC Ctr Math Computat & Cognit Av Estados 5001 Santo Andre Brazil

ISBN: (纸本)9783319919386;9783319919379

Rearrangements are mutations that affect large portions of a genome. When comparing two genomes, one wants to find a sequence of rearrangements that transforms one into another. When we use permutations to represent the genomes, this reduces to the problem of sorting a permutation using some sequence of rearrangements. The traditional approach is to find a sequence of minimum length. However, some studies show that some rearrangements are more likely to disturb an individual, and so a weighted approach is closer to the real evolutionary process. Most weighted approaches consider that the cost of a rearrangement can be related to its type or to the number of elements affected by it. This work introduces a new type of cost function, which is related to the amount of fragmentation caused by a rearrangement. We present some results about lower and upper bounds for the fragmentation-weighted problems and the relation between the unweighted and the fragmentation-weighted approach. Our main results are 2-approximation algorithms for 5 versions of the fragmentation-weighted problem involving reversals and transpositions events.

关键词： Genome rearrangements Sorting permutations approximation algorithms

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approximation algorithms for Submodular Multiway Partition

Approximation Algorithms for Submodular Multiway Partition

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52nd Annual IEEE Symposium on Foundations of Computer Science (FOCS)

作者： Chekuri, Chandra Ene, Alina Univ Illinois Dept Comp Sci Urbana IL 61801 USA

ISBN: (纸本)9780769545714

We study algorithms for the SUBMODULAR MULTIWAY PARTITION problem (SUB-MP). An instance of SUB-MP consists of a finite ground set V, a subset S = {s(1,) s(2,) ..., s(k)} subset of V of k elements called terminals, and a non-negative submodular set function f : 2(V) -> R+ on V provided as a value oracle. T P he goal is to partition V into k sets A(1), ...., A(k) to minimize Sigma(k)(i=1) f(A(i)) such that for 1 <= i <= k, s(i) is an element of A(i). SUB-MP generalizes some well-known problems such as the MULTIWAY CUT problem in graphs and hypergraphs, and the NODE-WEIGHED MULTIWAY CUT problem in graphs. SUB-MP for arbitrary submodular functions (instead of just symmetric functions) was considered by Zhao, Nagamochi and Ibaraki [29]. Previous algorithms were based on greedy splitting and divide and conquer strategies. In recent work [5] we proposed a convex-programming relaxation for SUB-MP based on the Lovasz-extension of a submodular function and showed its applicability for some special cases. In this paper we obtain the following results for arbitrary submodular functions via this relaxation. A 2-approximation for SUB-MP. This improves the (k - 1)-approximation from [29]. A (1.5-1/k)-approximation for SUB-MP when f is symmetric. This improves the 2(1 - 1/k)-approximation from [23], [29].

关键词： Algorithm design and analysis approximation algorithms approximation methods Partitioning algorithms Polynomials Resource management Vectors approximation algorithms approximation theory convex programming convex-programming relaxation divide and conquer

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approximation algorithms for the Team Orienteering Problem 38

Approximation Algorithms for the Team Orienteering Problem

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39th IEEE International Conference on Computer Communications (IEEE INFOCOM)

作者： Xu, Wenzheng Xu, Zichuan Peng, Jian Liang, Weifa Liu, Tang Jia, Xiaohua Das, Sajal K. Sichuan Univ Coll Comp Sci Chengdu 510006 Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Peoples R China Australian Natl Univ Res Sch Comp Sci Canberra ACT 0200 Australia Sichuan Normal Univ Coll Comp Sci Chengdu 610068 Sichuan Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

ISBN: (纸本)9781728164120

In this paper we study a team orienteering problem, which is to find service paths for multiple vehicles in a network such that the profit sum of serving the nodes in the paths is maximized, subject to the cost budget of each vehicle. This problem has many potential applications in IoT and smart cities, such as dispatching energy-constrained mobile chargers to charge as many energy-critical sensors as possible to prolong the network lifetime. In this paper, we first formulate the team orienteering problem, where different vehicles are different types, each node can be served by multiple vehicles, and the profit of serving the node is a submodular function of the number of vehicles serving it. We then propose a novel (1 - (1/e)1/2+epsilon)-approximation algorithm for the problem, where epsilon is a given constant with 0 < epsilon <= 1 and e is the base of the natural logarithm. In particular, the approximation ratio is no less than 0.32 when epsilon = 0.5. In addition, for a special team orienteering problem with the same type of vehicles and the profits of serving a node once and multiple times being the same, we devise an improved approximation algorithm. Finally, we evaluate the proposed algorithms with simulation experiments, and the results of which are very promising. Precisely, the profit sums delivered by the proposed algorithms are approximately 12.5% to 17.5% higher than those by existing algorithms.

关键词： Multiple vehicle scheduling team orienteering problem approximation algorithms submodular function

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approximation algorithms for Online Weighted Rank Function Maximization under Matroid Constraints

Approximation Algorithms for Online Weighted Rank Function M...

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39th International Colloquium on Automata, Languages, and Programming (ICALP)

作者： Buchbinder, Niv Naor, Joseph (Seffi) Ravi, R. Singh, Mohit Open Univ Israel Dept Comp Sci Raanana Israel Dept Comp Sci Techn Haifa Israel Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA USA McGill Univ Redmond & Sch Comp Sci Microsoft Res Montreal PQ H3A 2T5 Canada

ISBN: (纸本)9783642315947

Consider the following online version of the submodular maximization problem under a matroid constraint. We are given a set of elements over which a matroid is defined. The goal is to incrementally choose a subset that remains independent in the matroid over time. At each time, a new weighted rank function of a different matroid (one per time) over the same elements is presented;the algorithm can add a few elements to the incrementally constructed set, and reaps a reward equal to the value of the new weighted rank function on the current set. The goal of the algorithm as it builds this independent set online is to maximize the sum of these (weighted rank) rewards. As in regular online analysis, we compare the rewards of our online algorithm to that of an offline optimum, namely a single independent set of the matroid that maximizes the sum of the weighted rank rewards that arrive over time. This problem is a natural extension of two well-studied streams of earlier work: the first is on online set cover algorithms (in particular for the max coverage version) while the second is on approximately maximizing submodular functions under a matroid constraint. In this paper, we present the first randomized online algorithms for this problem with poly-logarithmic competitive ratio. To do this, we employ the LP formulation of a scaled reward version of the problem. Then we extend a weighted-majority type update rule along with uncrossing properties of tight sets in the matroid polytope to find an approximately optimal fractional LP solution. We use the fractional solution values as probabilities for a online randomized rounding algorithm. To show that our rounding produces a sufficiently large reward independent set, we prove and use new covering properties for randomly rounded fractional solutions in the matroid polytope that may be of independent interest.

关键词： approximation algorithms

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approximation algorithms for probabilistic k-center clustering 20

Approximation algorithms for probabilistic k-center clusteri...

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20th IEEE International Conference on Data Mining (ICDM)

作者： Alipour, Sharareh IPM Inst Res Fundamental Sci Sch Comp Sci Tehran Iran

ISBN: (纸本)9781728183169

Uncertainty about data appears in many real world applications and an important issue is how to manage, analyze and solve optimization problems over such data. An important tool for data analysis is clustering. When the data set is uncertain, we can model them as a set of probabilistic points each formalized as a probability distribution function which describes the possible locations of the points. In this paper, we study k-center problem for probabilistic points in a general metric space. First we present a fast greedy approximation algorithm that builds k centers using a farthest-first traversal in k iterations. This algorithm improves the previous approximation factor of the unrestricted assigned k center problem from 10 (see III) to 6. Next we restrict the centers to be selected from all the probabilistic locations of the given points and we show that an optimal solution for this restricted setting is a 2-approximation factor solution for an optimal solution of the assigned k-center problem with expected distance assignment. Using this idea, we improve the approximation factor of the unrestricted assigned k-center problem to 4 by increasing the running time. The algorithm also runs in polynomial time when k is a constant. Additionally, we implement our algorithms on three real data sets. The experimental results show that in practice the approximation factors of our algorithms are better than in theory for these data sets. Also we compare the results of our algorithm with the previous works and discuss about the achieved results.

关键词： k-center problem clustering uncertain data approximation algorithms

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