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168
Network Lifetime and Power Assignment in AdHoc Wireless Networks
 IN ESA
, 2003
"... Used for topology control in adhoc wireless networks, Power Assignment is a family of problems, each defined by a certain connectivity constraint (such as strong connectivity) The input consists of a directed complete weighted graph G = (V; c). The power of a vertex u in a directed spanning subgra ..."
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Cited by 53 (4 self)
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Used for topology control in adhoc wireless networks, Power Assignment is a family of problems, each defined by a certain connectivity constraint (such as strong connectivity) The input consists of a directed complete weighted graph G = (V; c). The power of a vertex u in a directed spanning subgraph H is given by pH(u) = maxuv2E(H) c(uv). The power of H is given by p(H) = P u2V pH(u), Power Assignment seeks to minimize p(H) while H satisfies the given connectivity constraint. We
Feedback set problems
 HANDBOOK OF COMBINATORIAL OPTIMIZATION
, 1999
"... ABSTRACT. This paper is a short survey of feedback set problems. It will be published in ..."
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Cited by 53 (1 self)
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ABSTRACT. This paper is a short survey of feedback set problems. It will be published in
Improved Approximation Guarantees for Packing and Covering Integer Programs
 SIAM J. Comput
, 1995
"... Several important NPhard combinatorial optimization problems can be posed as packing/covering integer programs; the randomized rounding technique of Raghavan & Thompson is a powerful tool to approximate them well. We present one elementary unifying property of all these integer programs (IPs), ..."
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Cited by 50 (5 self)
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Several important NPhard combinatorial optimization problems can be posed as packing/covering integer programs; the randomized rounding technique of Raghavan & Thompson is a powerful tool to approximate them well. We present one elementary unifying property of all these integer programs (IPs), and use the FKG correlation inequality to derive an improved analysis of randomized rounding on them. This also yields a pessimistic estimator, thus presenting deterministic polynomialtime algorithms for them with approximation guarantees significantly better than those known. Keywords.Approximation Algorithms, Combinatorial Optimization, Correlation Inequalities, Covering Integer Programs, Derandomization, Integer Programming, Linear Programming, Linear Relaxations, Packing Integer Programs, Positive Correlation, Randomized Rounding, Rounding Theorems. 1 Preliminary versions of this work appeared in the Proc. ACM Symposium on the Theory of Computing, pages 268276, 1995, and as DIMACS Te...
Adaptive blocking: Learning to scale up record linkage
 In Proceedings of the 6th IEEE International Conference on Data Mining (ICDM2006
, 2006
"... Many data mining tasks require computing similarity between pairs of objects. Pairwise similarity computations are particularly important in record linkage systems, as well as in clustering and schema mapping algorithms. Because the number of object pairs grows quadratically with the size of the dat ..."
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Cited by 41 (1 self)
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Many data mining tasks require computing similarity between pairs of objects. Pairwise similarity computations are particularly important in record linkage systems, as well as in clustering and schema mapping algorithms. Because the number of object pairs grows quadratically with the size of the dataset, computing similarity between all pairs is impractical and becomes prohibitive for large datasets and complex similarity functions. Blocking methods alleviate this problem by efficiently selecting approximately similar object pairs for subsequent distance computations, leaving out the remaining pairs as dissimilar. Previously proposed blocking methods require manually constructing an indexbased similarity function or selecting a set of predicates, followed by handtuning of parameters. In this paper, we introduce an adaptive framework for automatically learning blocking functions that are efficient and accurate. We describe two predicatebased formulations of learnable blocking functions and provide learning algorithms for training them. The effectiveness of the proposed techniques is demonstrated on real and simulated datasets, on which they prove to be more accurate than nonadaptive blocking methods. 1
An Extension of the Lovász Local Lemma, and its Applications to Integer Programming
 In Proceedings of the 7th Annual ACMSIAM Symposium on Discrete Algorithms
, 1996
"... The Lov'asz Local Lemma (LLL) is a powerful tool in proving the existence of rare events. We present an extension of this lemma, which works well when the event to be shown to exist is a conjunction of individual events, each of which asserts that a random variable does not deviate much from it ..."
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Cited by 38 (7 self)
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The Lov'asz Local Lemma (LLL) is a powerful tool in proving the existence of rare events. We present an extension of this lemma, which works well when the event to be shown to exist is a conjunction of individual events, each of which asserts that a random variable does not deviate much from its mean. We consider three classes of NPhard integer programs: minimax, packing, and covering integer programs. A key technique, randomized rounding of linear relaxations, was developed by Raghavan & Thompson to derive good approximation algorithms for such problems. We use our extended LLL to prove that randomized rounding produces, with nonzero probability, much better feasible solutions than known before, if the constraint matrices of these integer programs are sparse (e.g., VLSI routing using short paths, problems on hypergraphs with small dimension/degree). We also generalize the method of pessimistic estimators due to Raghavan, to constructivize our packing and covering results. 1
Polynomialtime Learning of Elementary Formal Systems
 Theoretical Computer Science
, 2000
"... An elementary formal system (EFS) is a logic program con sisting of definite clauses whose arguments have patterns instead of firstorder terms. We investigate EFSs for polynomialtime PAClearnability. A definite clause of an EFS is hereditary if every pattern in the body is a subword of a pat ..."
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Cited by 38 (8 self)
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An elementary formal system (EFS) is a logic program con sisting of definite clauses whose arguments have patterns instead of firstorder terms. We investigate EFSs for polynomialtime PAClearnability. A definite clause of an EFS is hereditary if every pattern in the body is a subword of a pattern in the head. With this new notion, we show that HEFS(ra, k, t, r) is polynomialtime learnable, which is the class of languages definable by EFSs consisting of at most ra hereditary definite clauses with predicate symbols of arity at most r, where k and t bound the number of variable occurrences in the head and the number of atoms in the body, respectively. The class defined by all finite unions of EFSs in HEFS(ra, k, t, r) is also polynomialtime learnable. We also show an interesting series of NClearnable classes of EFSs. As hardness results, the class of regular pattern languages is shown not polynomialtime learnable unless RP=NP. Furthermore, the related problem of deciding whether there is a common subsequence which is consistent with given positive and negative examples is shown NPcomplete.
The Set Covering Machine
, 2002
"... We extend the classical algorithms of Valiant and Haussler for learning compact conjunctions and disjunctions of Boolean attributes to allow features that are constructed from the data and to allow a tradeoff between accuracy and complexity. The result is a generalpurpose learning machine, suitabl ..."
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Cited by 35 (8 self)
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We extend the classical algorithms of Valiant and Haussler for learning compact conjunctions and disjunctions of Boolean attributes to allow features that are constructed from the data and to allow a tradeoff between accuracy and complexity. The result is a generalpurpose learning machine, suitable for practical learning tasks, that we call the set covering machine. We present a version of the set covering machine that uses datadependent balls for its set of features and compare its performance with the support vector machine. By extending a technique pioneered by Littlestone and Warmuth, we bound its generalization error as a function of the amount of data compression it achieves during training. In experiments with realworld learning tasks, the bound is shown to be extremely tight and to provide an effective guide for model selection.
A Formal Framework For The Study Of Task Allocation In MultiRobot Systems
, 2003
"... Despite more than a decade of experimental work in multirobot systems, important theoretical aspects of multirobot coordination mechanisms have, to date, been largely untreated. To address this issue, we focus on the problem of multirobot task allocation (MRTA). Most work on MRTA has been ad hoc ..."
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Cited by 33 (5 self)
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Despite more than a decade of experimental work in multirobot systems, important theoretical aspects of multirobot coordination mechanisms have, to date, been largely untreated. To address this issue, we focus on the problem of multirobot task allocation (MRTA). Most work on MRTA has been ad hoc and empirical, with many coordination architectures having been proposed and validated in a proofofconcept fashion, but infrequently analyzed. With the goal of bringing objective grounding to this important area of research, we present a formal study of MRTA problems. A domainindependent taxonomy of MRTA problems is given, and it is shown how many such problems can be viewed as instances of other, wellstudied, optimization problems. We demonstrate how relevant theory from operations research and combinatorial optimization can be used for analysis and greater understanding of existing approaches to task allocation, and show how the same theory can be used in the synthesis of new approaches.
Computing NearOptimal Solutions to Combinatorial Optimization Problems
 IN COMBINATORIAL OPTIMIZATION, DIMACS SERIES IN DISCRETE MATHEMATICS AND THEORETICAL COMPUTER SCIENCE
, 1995
"... In the past few years, there has been significant progress in our understanding of the extent to which nearoptimal solutions can be efficiently computed for NPhard combinatorial optimization problems. This paper surveys these recent developments, while concentrating on the advances made in the ..."
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Cited by 32 (0 self)
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In the past few years, there has been significant progress in our understanding of the extent to which nearoptimal solutions can be efficiently computed for NPhard combinatorial optimization problems. This paper surveys these recent developments, while concentrating on the advances made in the design and analysis of approximation algorithms, and in particular, on those results that rely on linear programming and its generalizations.
Exact Learning of Discretized Geometric Concepts
 SIAM J. COMPUT
, 1998
"... We first present an algorithm that uses membership and equivalence queries to exactly identify a discretized geometric concept defined by the union of m axisparallel boxes in ddimensional discretized Euclidean space where each coordinate can have n discrete values. This algorithm receives at ..."
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Cited by 30 (11 self)
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We first present an algorithm that uses membership and equivalence queries to exactly identify a discretized geometric concept defined by the union of m axisparallel boxes in ddimensional discretized Euclidean space where each coordinate can have n discrete values. This algorithm receives at most<F3.474e+05> md<F3.835e+05> counterexamples and uses time and membership queries polynomial in<F3.474e+05> m<F3.835e+05> and log<F3.474e+05> n<F3.835e+05> for any constant<F3.474e+05><F3.835e+05> d. Furthermore, all equivalence queries can be formulated as the union of<F3.474e+05><F3.835e+05><F3.474e+05> O(md log m) axisparallel boxes. Next, we show how to extend our algorithm to efficiently learn, from only equivalence queries, any discretized geometric concept generated from any number of halfspaces with any number of known (to the learner) slopes...