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TL;DR: An implementation of Dantzig et al.'s method is discussed that is suitable for TSP instances having 1,000,000 or more cities, and used as a step towards understanding the applicability and limits of the general cutting-plane method in large-scale applications.
Abstract: Dantzig, Fulkerson, and Johnson (1954) introduced the cutting-plane method as a means of attacking the traveling salesman problem; this method has been applied to broad classes of problems in combinatorial optimization and integer programming. In this paper we discuss an implementation of Dantzig et al.'s method that is suitable for TSP instances having 1,000,000 or more cities. Our aim is to use the study of the TSP as a step towards understanding the applicability and limits of the general cutting-plane method in large-scale applications.
157 citations
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TL;DR: This work gives approximation algorithms and inapproximability results for a class of movement problems that involve planning the coordinated motion of a large collection of objects to achieve a global property of the network while minimizing the maximum or average movement.
Abstract: We give approximation algorithms and inapproximability results for a class of movement problems. In general, these problems involve planning the coordinated motion of a large collection of objects (representing anything from a robot swarm or firefighter team to map labels or network messages) to achieve a global property of the network while minimizing the maximum or average movement. In particular, we consider the goals of achieving connectivity (undirected and directed), achieving connectivity between a given pair of vertices, achieving independence (a dispersion problem), and achieving a perfect matching (with applications to multicasting). This general family of movement problems encompasses an intriguing range of graph and geometric algorithms, with several real-world applications and a surprising range of approximability. In some cases, we obtain tight approximation and inapproximability results using direct techniques (without use of PCP), assuming just that P ≠ NP.
156 citations
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TL;DR: This paper demonstrates that the MG and the SpaceSaving summaries for heavy hitters are indeed mergeable or can be made mergeable after appropriate modifications, and provides the best known randomized streaming bound for ε-approximate quantiles that depends only on ε, of size O(1 overε log 3/21 over ε).
Abstract: We study the mergeability of data summaries. Informally speaking, mergeability requires that, given two summaries on two data sets, there is a way to merge the two summaries into a single summary on the union of the two data sets, while preserving the error and size guarantees. This property means that the summaries can be merged in a way like other algebraic operators such as sum and max, which is especially useful for computing summaries on massive distributed data. Several data summaries are trivially mergeable by construction, most notably all the sketches that are linear functions of the data sets. But some other fundamental ones like those for heavy hitters and quantiles, are not (known to be) mergeable. In this paper, we demonstrate that these summaries are indeed mergeable or can be made mergeable after appropriate modifications. Specifically, we show that for e-approximate heavy hitters, there is a deterministic mergeable summary of size O(1/e) for e-approximate quantiles, there is a deterministic summary of size O(1 over e log(en))that has a restricted form of mergeability, and a randomized one of size O(1 over e log 3/21 over e) with full mergeability. We also extend our results to geometric summaries such as e-approximations and ekernels.We also achieve two results of independent interest: (1) we provide the best known randomized streaming bound for e-approximate quantiles that depends only on e, of size O(1 over e log 3/21 over e, and (2) we demonstrate that the MG and the SpaceSaving summaries for heavy hitters are isomorphic.
156 citations
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11 Apr 2011
TL;DR: A general framework for evaluation and optimization of methods for diversifying query results is described, and the first thorough experimental evaluation of the various diversification techniques implemented in a common framework is presented.
Abstract: In this paper we describe a general framework for evaluation and optimization of methods for diversifying query results. In these methods, an initial ranking candidate set produced by a query is used to construct a result set, where elements are ranked with respect to relevance and diversity features, i.e., the retrieved elements should be as relevant as possible to the query, and, at the same time, the result set should be as diverse as possible. While addressing relevance is relatively simple and has been heavily studied, diversity is a harder problem to solve. One major contribution of this paper is that, using the above framework, we adapt, implement and evaluate several existing methods for diversifying query results. We also propose two new approaches, namely the Greedy with Marginal Contribution (GMC) and the Greedy Randomized with Neighborhood Expansion (GNE) methods. Another major contribution of this paper is that we present the first thorough experimental evaluation of the various diversification techniques implemented in a common framework. We examine the methods' performance with respect to precision, running time and quality of the result. Our experimental results show that while the proposed methods have higher running times, they achieve precision very close to the optimal, while also providing the best result quality. While GMC is deterministic, the randomized approach (GNE) can achieve better result quality if the user is willing to tradeoff running time.
156 citations
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14 Dec 1997TL;DR: A stochastic model for dialogue systems based on the Markov decision process is introduced, showing that the problem of dialogue strategy design can be stated as an optimization problem, and solved by a variety of methods, including the reinforcement learning approach.
Abstract: We introduce a stochastic model for dialogue systems based on the Markov decision process. Within this framework we show that the problem of dialogue strategy design can be stated as an optimization problem, and solved by a variety of methods, including the reinforcement learning approach. The advantages of this new paradigm include objective evaluation of dialogue systems and their automatic design and adaptation. We show some preliminary results on learning a dialogue strategy for an air travel information system.
156 citations
Authors
Showing all 1881 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yoshua Bengio | 202 | 1033 | 420313 |
Scott Shenker | 150 | 454 | 118017 |
Paul Shala Henry | 137 | 318 | 35971 |
Peter Stone | 130 | 1229 | 79713 |
Yann LeCun | 121 | 369 | 171211 |
Louis E. Brus | 113 | 347 | 63052 |
Jennifer Rexford | 102 | 394 | 45277 |
Andreas F. Molisch | 96 | 777 | 47530 |
Vern Paxson | 93 | 267 | 48382 |
Lorrie Faith Cranor | 92 | 326 | 28728 |
Ward Whitt | 89 | 424 | 29938 |
Lawrence R. Rabiner | 88 | 378 | 70445 |
Thomas E. Graedel | 86 | 348 | 27860 |
William W. Cohen | 85 | 384 | 31495 |
Michael K. Reiter | 84 | 380 | 30267 |