Results 1  10
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New Approximation Techniques for Some Ordering Problems
 IN 9TH ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS
, 1998
"... We describe logarithmic times optimal approximation algorithms for the NPhard graph optimization problems of minimum linear arrangement, minimum containing interval graph, and minimum storagetime product. This improves on the best previous approximation bounds of Even, Naor, Rao, and Schieber for ..."
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Cited by 45 (1 self)
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We describe logarithmic times optimal approximation algorithms for the NPhard graph optimization problems of minimum linear arrangement, minimum containing interval graph, and minimum storagetime product. This improves on the best previous approximation bounds of Even, Naor, Rao, and Schieber
A new approximation technique for divcurl systems
 MATH. COMP
, 2003
"... In this paper, we describe an approximation technique for divcurl systems based in (L 2 (Ω) 3) where Ω is a domain in R 3. We formulate this problem as a general variational problem with different test and trial spaces. The analysis requires the verification of an appropriate infsup condition. This ..."
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Cited by 16 (4 self)
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In this paper, we describe an approximation technique for divcurl systems based in (L 2 (Ω) 3) where Ω is a domain in R 3. We formulate this problem as a general variational problem with different test and trial spaces. The analysis requires the verification of an appropriate infsup condition
A new approximation technique for resourceallocation problems
, 2009
"... Abstract: We develop a rounding method based on random walks in polytopes, which leads to improved approximation algorithms and integrality gaps for several assignment problems that arise in resource allocation and scheduling. In particular, it generalizes the work of Shmoys & Tardos on the gene ..."
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Cited by 9 (4 self)
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Abstract: We develop a rounding method based on random walks in polytopes, which leads to improved approximation algorithms and integrality gaps for several assignment problems that arise in resource allocation and scheduling. In particular, it generalizes the work of Shmoys & Tardos
Approximate Signal Processing
, 1997
"... It is increasingly important to structure signal processing algorithms and systems to allow for trading off between the accuracy of results and the utilization of resources in their implementation. In any particular context, there are typically a variety of heuristic approaches to managing these tra ..."
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Cited by 516 (2 self)
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these tradeoffs. One of the objectives of this paper is to suggest that there is the potential for developing a more formal approach, including utilizing current research in Computer Science on Approximate Processing and one of its central concepts, Incremental Refinement. Toward this end, we first summarize a
TCP Vegas: New techniques for congestion detection and avoidance
 In SIGCOMM
, 1994
"... Vegas is a new implementation of TCP that achieves between 40 and 70 % better throughput, with onefifth to onehalf the losses, as compared to the implementation of TCP in the Reno distributionof BSD Unix. This paper motivates and describes the three key techniques employed by Vegas, and presents th ..."
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Cited by 592 (3 self)
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Vegas is a new implementation of TCP that achieves between 40 and 70 % better throughput, with onefifth to onehalf the losses, as compared to the implementation of TCP in the Reno distributionof BSD Unix. This paper motivates and describes the three key techniques employed by Vegas, and presents
A Guided Tour to Approximate String Matching
 ACM COMPUTING SURVEYS
, 1999
"... We survey the current techniques to cope with the problem of string matching allowing errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining t ..."
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Cited by 584 (38 self)
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We survey the current techniques to cope with the problem of string matching allowing errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining
A Threshold of ln n for Approximating Set Cover
 JOURNAL OF THE ACM
, 1998
"... Given a collection F of subsets of S = f1; : : : ; ng, set cover is the problem of selecting as few as possible subsets from F such that their union covers S, and max kcover is the problem of selecting k subsets from F such that their union has maximum cardinality. Both these problems are NPhar ..."
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Cited by 778 (5 self)
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hard. We prove that (1 \Gamma o(1)) ln n is a threshold below which set cover cannot be approximated efficiently, unless NP has slightly superpolynomial time algorithms. This closes the gap (up to low order terms) between the ratio of approximation achievable by the greedy algorithm (which is (1 \Gamma
Greedy Function Approximation: A Gradient Boosting Machine
 Annals of Statistics
, 2000
"... Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additi ..."
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Cited by 951 (12 self)
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Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed
Property Testing and its connection to Learning and Approximation
"... We study the question of determining whether an unknown function has a particular property or is fflfar from any function with that property. A property testing algorithm is given a sample of the value of the function on instances drawn according to some distribution, and possibly may query the fun ..."
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Cited by 498 (68 self)
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We study the question of determining whether an unknown function has a particular property or is fflfar from any function with that property. A property testing algorithm is given a sample of the value of the function on instances drawn according to some distribution, and possibly may query the function on instances of its choice. First, we establish some connections between property testing and problems in learning theory. Next, we focus on testing graph properties, and devise algorithms to test whether a graph has properties such as being kcolorable or having a aeclique (clique of density ae w.r.t the vertex set). Our graph property testing algorithms are probabilistic and make assertions which are correct with high probability, utilizing only poly(1=ffl) edgequeries into the graph, where ffl is the distance parameter. Moreover, the property testing algorithms can be used to efficiently (i.e., in time linear in the number of vertices) construct partitions of the graph which corre...
Results 1  10
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