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The dangers of replication and a solution

by Jim Gray, Pat Helland, Patrick O'Neil , Dennis Shasha - IN PROCEEDINGS OF THE 1996 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA , 1996
"... Update anywhere-anytime-anyway transactional replication has unstable behavior as the workload scales up: a ten-fold increase in nodes and traflc gives a thousand fold increase in deadlocks or reconciliations. Master copy replication (primary copyj schemes reduce this problem. A simple analytic mode ..."
Abstract - Cited by 575 (3 self) - Add to MetaCart
Update anywhere-anytime-anyway transactional replication has unstable behavior as the workload scales up: a ten-fold increase in nodes and traflc gives a thousand fold increase in deadlocks or reconciliations. Master copy replication (primary copyj schemes reduce this problem. A simple analytic

Nonlinear total variation based noise removal algorithms

by Leonid I. Rudin, Stanley Osher, Emad Fatemi , 1992
"... A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the g ..."
Abstract - Cited by 2271 (51 self) - Add to MetaCart
the gradient-projection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t--- ~ 0o the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear

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 core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment

Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming

by M. X. Goemans, D.P. Williamson - Journal of the ACM , 1995
"... We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2-satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds the solution ..."
Abstract - Cited by 1211 (13 self) - Add to MetaCart
We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2-satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds

A Singular Value Thresholding Algorithm for Matrix Completion

by Jian-Feng Cai, Emmanuel J. Candès, Zuowei Shen , 2008
"... This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of reco ..."
Abstract - Cited by 555 (22 self) - Add to MetaCart
of recovering a large matrix from a small subset of its entries (the famous Netflix problem). Off-the-shelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries. This paper develops a simple first-order and easy

A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II

by Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T. Meyarivan , 2000
"... Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing param ..."
Abstract - Cited by 1815 (60 self) - Add to MetaCart
Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing

The CN2 Induction Algorithm

by Peter Clark , Tim Niblett - MACHINE LEARNING , 1989
"... Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple, comprehensib ..."
Abstract - Cited by 890 (6 self) - Add to MetaCart
Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple

A Limited Memory Algorithm for Bound Constrained Optimization

by Richard H. Byrd, Peihuang Lu, Jorge Nocedal, Ciyou Zhu - SIAM JOURNAL ON SCIENTIFIC COMPUTING , 1994
"... An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based ..."
Abstract - Cited by 572 (9 self) - Add to MetaCart
An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based

The weighted majority algorithm

by Nick Littlestone, Manfred K. Warmuth , 1992
"... We study the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be made in each, and the goal of the learner is to make few mistakes. We are interested in the case that the learner has reason to believe that one of some pool of kn ..."
Abstract - Cited by 877 (43 self) - Add to MetaCart
of known algorithms will perform well, but the learner does not know which one. A simple and effective method, based on weighted voting, is introduced for constructing a compound algorithm in such a circumstance. We call this method the Weighted Majority Algorithm. We show that this algorithm is robust

On Spectral Clustering: Analysis and an algorithm

by Andrew Y. Ng, Michael I. Jordan, Yair Weiss - ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS , 2001
"... Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
Abstract - Cited by 1713 (13 self) - Add to MetaCart
in slightly different ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze
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