Results 1 - 10 of 33,043

A subexponential lower bound for the Random Facet algorithm for Parity Games

by Oliver Friedmann, Thomas Dueholm Hansen, Uri Zwick
"... Parity Games form an intriguing family of infinite duration games whose solution is equivalent to the solution of important problems in automatic verification and automata theory. They also form a very natural subclass of Deterministic Mean Payoff Games, which in turn is a very natural subclass of t ..."
Abstract - Cited by 6 (5 self) - Add to MetaCart
Matouˇsek, Sharir and Welzl as the Random Facet algorithm. The expected running time of these algorithmsis subexponential in the size of the game, i.e., 2

Errata for: A subexponential lower bound for the Random Facet algorithm for Parity Games

by Oliver Friedmann, Thomas Dueholm, Hansen Uri Zwick , 2014
"... In [Friedmann, Hansen, and Zwick (2011)] and we claimed that the expected number of pivoting steps performed by the Random-Facet algorithm of Kalai and of Matoušek, Sharir, and Welzl is equal to the expected number of pivoting steps performed by Random-Facet∗, a variant of Random-Facet that bases i ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
In [Friedmann, Hansen, and Zwick (2011)] and we claimed that the expected number of pivoting steps performed by the Random-Facet algorithm of Kalai and of Matoušek, Sharir, and Welzl is equal to the expected number of pivoting steps performed by Random-Facet∗, a variant of Random-Facet that bases

Randomized Algorithms

by Rajeev Motwani , 1995
"... Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available, or the simp ..."
Abstract - Cited by 2196 (36 self) - Add to MetaCart
Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available

Randomized Gossip Algorithms

by Stephen Boyd, Arpita Ghosh, Balaji Prabhakar, Devavrat Shah - IEEE TRANSACTIONS ON INFORMATION THEORY , 2006
"... Motivated by applications to sensor, peer-to-peer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in an arbitrarily connected network of nodes. The topology of such networks changes continuously as new nodes join a ..."
Abstract - Cited by 532 (5 self) - Add to MetaCart
distribute the computational burden and in which a node communicates with a randomly chosen neighbor. We analyze the averaging problem under the gossip constraint for an arbitrary network graph, and find that the averaging time of a gossip algorithm depends on the second largest eigenvalue of a doubly

The Quickhull algorithm for convex hulls

by C. Bradford Barber, David P. Dobkin, Hannu Huhdanpaa - ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE , 1996
"... The convex hull of a set of points is the smallest convex set that contains the points. This article presents a practical convex hull algorithm that combines the two-dimensional Quickhull Algorithm with the general-dimension Beneath-Beyond Algorithm. It is similar to the randomized, incremental algo ..."
Abstract - Cited by 713 (0 self) - Add to MetaCart
The convex hull of a set of points is the smallest convex set that contains the points. This article presents a practical convex hull algorithm that combines the two-dimensional Quickhull Algorithm with the general-dimension Beneath-Beyond Algorithm. It is similar to the randomized, incremental

Inducing Features of Random Fields

by Stephen Della Pietra, Vincent Della Pietra, John Lafferty - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
Abstract - Cited by 670 (10 self) - Add to MetaCart
the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random field models and techniques

Data Streams: Algorithms and Applications

by S. Muthukrishnan , 2005
"... In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerg ..."
Abstract - Cited by 533 (22 self) - Add to MetaCart
emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic

Non-Uniform Random Variate Generation

by Luc Devroye , 1986
"... This is a survey of the main methods in non-uniform random variate generation, and highlights recent research on the subject. Classical paradigms such as inversion, rejection, guide tables, and transformations are reviewed. We provide information on the expected time complexity of various algorith ..."
Abstract - Cited by 1021 (26 self) - Add to MetaCart
algorithms, before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

by Yongyue Zhang, Michael Brady, Stephen Smith - IEEE TRANSACTIONS ON MEDICAL. IMAGING , 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limi ..."
Abstract - Cited by 639 (15 self) - Add to MetaCart
-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown

Shallow Parsing with Conditional Random Fields

by Fei Sha, Fernando Pereira , 2003
"... Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluati ..."
Abstract - Cited by 581 (8 self) - Add to MetaCart
Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard
Results 1 - 10 of 33,043