• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 39,197
Next 10 →

New spectral methods for ratio cut partition and clustering

by Lars Hagen, Andrew B. Kahng - IEEE TRANS. ON COMPUTER-AIDED DESIGN , 1992
"... Partitioning of circuit netlists is important in many phases of VLSI design, ranging from layout to testing and hardware simulation. The ratio cut objective function [29] has received much attention since it naturally captures both min-cut and equipartition, the two traditional goals of partitionin ..."
Abstract - Cited by 296 (17 self) - Add to MetaCart
of partitioning. In this paper, we show that the second smallest eigenvalue of a matrix derived from the netlist gives a provably good approx-imation of the optimal ratio cut partition cost. We also dem-onstrate that fast Lanczos-type methods for the sparse sym-metric eigenvalue problem are a robust basis

A Fast Marching Level Set Method for Monotonically Advancing Fronts

by J. A. Sethian - PROC. NAT. ACAD. SCI , 1995
"... We present a fast marching level set method for monotonically advancing fronts, which leads to an extremely fast scheme for solving the Eikonal equation. Level set methods are numerical techniques for computing the position of propagating fronts. They rely on an initial value partial differential eq ..."
Abstract - Cited by 630 (24 self) - Add to MetaCart
We present a fast marching level set method for monotonically advancing fronts, which leads to an extremely fast scheme for solving the Eikonal equation. Level set methods are numerical techniques for computing the position of propagating fronts. They rely on an initial value partial differential

A Fast Algorithm for Particle Simulations

by L. Greengard, V. Rokhlin , 1987
"... this paper to the case where the potential (or force) at a point is a sum of pairwise An algorithm is presented for the rapid evaluation of the potential and force fields in systems involving large numbers of particles interactions. More specifically, we consider potentials of whose interactions a ..."
Abstract - Cited by 1152 (19 self) - Add to MetaCart
are Coulombic or gravitational in nature. For a the form system of N particles, an amount of work of the order O(N 2 ) has traditionally been required to evaluate all pairwise interactions, un- F5F far 1 (F near 1F external ), less some approximation or truncation method is used. The algorithm of the present

Fast subsequence matching in time-series databases

by Christos Faloutsos, M. Ranganathan, Yannis Manolopoulos - PROCEEDINGS OF THE 1994 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA , 1994
"... We present an efficient indexing method to locate 1-dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequence into a small set of multidimensional rectangles in feature space ..."
Abstract - Cited by 533 (24 self) - Add to MetaCart
We present an efficient indexing method to locate 1-dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequence into a small set of multidimensional rectangles in feature

A fast iterative shrinkage-thresholding algorithm with application to . . .

by Amir Beck, Marc Teboulle , 2009
"... We consider the class of Iterative Shrinkage-Thresholding Algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods is attractive due to its simplicity, however, they are also known to converge quite slowly. In this paper we present a Fast Iterat ..."
Abstract - Cited by 1058 (9 self) - Add to MetaCart
We consider the class of Iterative Shrinkage-Thresholding Algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods is attractive due to its simplicity, however, they are also known to converge quite slowly. In this paper we present a Fast

Fast Planning Through Planning Graph Analysis

by Avrim L. Blum, Merrick L. Furst - ARTIFICIAL INTELLIGENCE , 1995
"... We introduce a new approach to planning in STRIPS-like domains based on constructing and analyzing a compact structure we call a Planning Graph. We describe a new planner, Graphplan, that uses this paradigm. Graphplan always returns a shortest possible partial-order plan, or states that no valid pla ..."
Abstract - Cited by 1171 (3 self) - Add to MetaCart
by Graphplan are quite sensible. Since searches made by this approach are fundamentally different from the searches of other common planning methods, they provide a new perspective on the planning problem.

A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood

by Stéphane Guindon, Olivier Gascuel , 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
Abstract - Cited by 2182 (27 self) - Add to MetaCart
The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements

Fast and robust fixed-point algorithms for independent component analysis

by Aapo Hyvärinen - IEEE TRANS. NEURAL NETW , 1999
"... Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s informat ..."
Abstract - Cited by 884 (34 self) - Add to MetaCart
Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s

The Omega Test: a fast and practical integer programming algorithm for dependence analysis

by William Pugh - Communications of the ACM , 1992
"... The Omega testi s ani nteger programmi ng algori thm that can determi ne whether a dependence exi sts between two array references, and i so, under what condi7: ns. Conventi nalwi[A m holds thati nteger programmiB techni:36 are far too expensi e to be used for dependence analysi6 except as a method ..."
Abstract - Cited by 522 (15 self) - Add to MetaCart
The Omega testi s ani nteger programmi ng algori thm that can determi ne whether a dependence exi sts between two array references, and i so, under what condi7: ns. Conventi nalwi[A m holds thati nteger programmiB techni:36 are far too expensi e to be used for dependence analysi6 except as a method

Fast and accurate short read alignment with Burrows-Wheeler transform

by Heng Li, Richard Durbin - BIOINFORMATICS, 2009, ADVANCE ACCESS , 2009
"... Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hashtable based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to a ..."
Abstract - Cited by 2096 (24 self) - Add to MetaCart
Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hashtable based methods has been developed, including MAQ, which is accurate, feature rich and fast enough
Next 10 →
Results 1 - 10 of 39,197
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University