Página 1 dos resultados de 694 itens digitais encontrados em 0.007 segundos

A study of order based genetic and evolutionary algorithms in combinatorial optimization problems

Rocha, Miguel; Vilela, Carla; Neves, José
Fonte: Springer Publicador: Springer
Tipo: Conferência ou Objeto de Conferência
Publicado em //2000 ENG
Relevância na Pesquisa
36%
In Genetic and Evolutionary Algorithms (GEAs) one is faced with a given number of parameters, whose possible values are coded in a binary alphabet. With Order Based Representations (OBRs) the genetic information is kept by the order of the genes and not by its value. The application of OBRs to the Traveling Salesman Problem (TSP) is a well known technique to the GEA community. In this work one intends to show that this coding scheme can be used as an indirect representation, where the chromosome is the input for the decoder. The behavior of the GEA's operators is compared under benchmarks taken from the Combinatorial Optimization arena. Keywords:Genetic and Evolutionary Algorithms, Order Based Representations.

Self-Concordant Functions for Optimization on Smooth Manifolds

Jiang, Danchi; Moore, John; Ji, Huibo
Fonte: Springer Publicador: Springer
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36%
This paper discusses self-concordant functions on smooth manifolds. In Euclidean space, such functions are utilized extensively as barrier functions in interior-point methods for polynomial time optimization algorithms. Here, the self-concordant function

Particle filtering algorithms for tracking an acoustic source in a reverberant enrironment

Ward, Darren B; Lehmann, Eric; Williamson, Robert
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36%
Traditional acoustic source localization algorithms attempt to find the current location of the acoustic source using data collected at an array of sensors at the current time only. In the presence of strong multipath, these traditional algorithms often e

Dynamic algorithm selection using reinforcement learning

Armstrong, Warren; Christen, Peter; McCreath, Eric; Rendell, Alistair
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
Relevância na Pesquisa
36%
It is often the case that many algorithms exist to solve a single problem, each possessing different performance characteristics. The usual approach in this situation is to manually select the algorithm which has the best average performance. However, thi

The language of search

Huang, Jinbo; Darwiche, Adnan
Fonte: Morgan Kauffman Publishers Publicador: Morgan Kauffman Publishers
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36%
This paper is concerned with a class of algorithms that perform exhaustive search on propositional knowledge bases. We show that each of these algorithms defines and generates a propositional language. Specifically, we show that the trace of a search can

Local convergence properties of fastica and some generalisations

Hueper, Knut; Shen, Hao; Seghouane, Abd-Krim
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
Relevância na Pesquisa
36%
In recent years, algorithms to perform Independent Component Analysis in blind identification, localisation of sources or more general in data analysis have been developed. Prominent example certainly is the socalled FastICA algorithms from the Finnish sc

Model-based Adaptive Algorithms for Time-Varying Communication Channels with Application to Adaptive Multiuser Detection

Krusevac, Zarko; Kennedy, Rodney; Rapajic, Predrag
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
Relevância na Pesquisa
46.05%
This paper shows through theory and simulation the superiority of model-based adaptive algorithms relative to observation-only-based adaptive algorithms, such as LMS and RLS, when applied to tracking time-varying channels. The model-based formulation reve

Algorithms for real time magnetic field tracing and optimization

Blackwell, Boyd; McMillan, B.; Searle, Antony; Gardner, Henry James
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46%
We describe algorithms for tracing vacuum magnetic field lines in complex geometries at interactive speeds (∼50,000 steps/s on a personal computer), and a post-processing perturbation method for optimization of magnetic surface properties. Algorithms in

Symmetries of matrix multiplication algorithms. I

Burichenko, V. P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/08/2015
Relevância na Pesquisa
36.08%
In this work the algorithms of fast multiplication of matrices are considered. To any algorithm there associated a certain group of automorphisms. These automorphism groups are found for some well-known algorithms, including algorithms of Hopcroft, Laderman, and Pan. The automorphism group is isomorphic to $S_3\times Z_2$ and $S_4$ for Hopcroft anf Laderman algorithms, respectively. The studying of symmetry of algorithms may be a fruitful idea for finding fast algorithms, by an analogy with well-known optimization problems for codes, lattices, and graphs. {\em Keywords}: Strassen algorithm, symmetry, fast matrix multiplication.

A importância das palavras-chave dos artigos científicos da área das Ciências Farmacêuticas, depositados no Estudo Geral: estudo comparativo com os termos atribuídos na MEDLINE; The importance of keywords in scientific articles in Pharmaceutical Sciences, submitted in Estudo Geral: a comparative study of terms assigned in MEDLINE

Miguéis, Ana; Neves, Bruno; Silva, Ana Luísa; Trindade, Álvaro; Bernardes, José Augusto
Fonte: Universidade de São Paulo. Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto Publicador: Universidade de São Paulo. Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; ; Formato: application/pdf
Publicado em 20/12/2013 POR
Relevância na Pesquisa
36.19%
Objetivos: Este trabalho tem como objetivo analisar as palavras-chave usadas pelos autores da Universidade de Coimbra, nos artigos publicados na área das Ciências Farmacêuticas, comparando-as com os termos da linguagem documental Medical Subject Headings (MeSH), empregues na análise de conteúdo desses mesmos artigos. No âmbito dos artigos científicos, as palavras-chave expõem a abrangência de um assunto e os seus conceitos principais, que se podem revelar úteis para a indexação em mecanismos de pesquisa ou para a categorização do texto. A investigação sobre a importância e caraterísticas das palavras-chave tem incidido sobre vários aspetos, como o da eficiência na recuperação da informação; o uso para a extração automática a partir de diferentes metodologias e algoritmos; o uso por parte dos autores e editores; mais recentemente, tem sido considerada ainda a sua utilização nos comportamentos de etiquetagem (metatags); e a comparação com os títulos, resumos e textos integrais, o que confirma do interesse que despertam. Mas, na revisão da literatura, foram poucos os trabalhos encontrados que abordassem a comparação das palavras-chave fornecidas pelos autores de artigos científicos e os descritores empregues pelos indexadores...

Convergence of Exponentiated Gradient Algorithms

Hill, Simon; Williamson, Robert
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46%
This paper studies three related algorithms: the (traditional) gradient descent (GD) algorithm, the exponentiated gradient algorithm with positive and negative weights (EG± algorithm), and the exponentiated gradient algorithm with unnormalized positive a

Scalable parallel algorithms for surface fitting and data mining

Christen, Peter; Hegland, Markus; Nielsen, Ole; Roberts, Stephen; Strazdins, Peter; Altas, I
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.03%
This paper presents scalable parallel algorithms for high-dimensional surface fitting and predictive modelling which are used in data mining applications. These algorithms are based on techniques like finite elements, thin plate splines, wavelets and additive models. They all consist of two steps: First, data is read from secondary storage and a linear system is assembled. Secondly, the linear system is solved. The assembly can be done with almost no communication and the size of the linear system is independent of the data size. Thus the presented algorithms are both scalable with the data size and the number of processors.

Approximating the Problem, not the Solution: An Alternative View of Point Set Matching

Caetano, Tiberio; Caelli, Terry
Fonte: Springer Publicador: Springer
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.03%
This work discusses the issue of approximation in point set matching problems. In general, one may have two classes of approximations when tackling a matching problem: a representational approximation, which involves a simplified and suboptimal modeling for the original problem, and algorithmic approximation, which consists in using suboptimal algorithms to infer the assignment. Matching techniques in general have relied on the second approach: to keep a complete model of the original problem and use suboptimal techniques to solve it. In this paper, we show how a technique based on using exact inference in simple graphical models, which is an instance of the first class, can significantly outperform instances of techniques from the second class. We give theoretical insights of why this happens, and experimentally compare our approach with the well-known Shapiro and Brady and Christmas et al. methods, which are exemplars of the second class. We perform experiments with synthetic and real-world data sets, which reveal a significant accuracy improvement of the proposed technique both under point position jitter and size increasing of the point sets. The main conclusion is that techniques based on optimal algorithms with appropriate suboptimal representations may lead to better results than their counterparts which consist in using optimal representations...

Continuous-Time Tracking Algorithms Involving Two-Time-Scale Markov Chains

Yin, George; Zhang, Qing; Moore, John; Liu, Y J
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.03%
This work is concerned with least-mean-squares (LMS) algorithms in continuous time for tracking a time-varying parameter process. A distinctive feature is that the true parameter process is changing at a fast pace driven by a finite-state Markov chain. The states of the Markov chain are divisible into a number of groups. Within each group, the transitions take place rapidly; among different groups, the transitions are infrequent. Introducing a small parameter into the generator of the Markov chain leads to a two-time-scale formulation. The tracking objective is difficult to achieve. Nevertheless, a limit result is derived yielding algorithms for limit systems. Moreover, the rates of variation of the tracking error sequence are analyzed. Under simple conditions, it is shown that a scaled sequence of the tracking errors converges weakly to a switching diffusion. In addition, a numerical example is provided and an adaptive step-size algorithm developed.

Principal Components Tracking Algorithms for Synchronization and Channel Identification in UWB systems

Zhang, Jian (Andrew); Abhayapala, Thushara; Kennedy, Rodney
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
Relevância na Pesquisa
45.95%
In this paper, we investigate the reduced rank shift invariant techniques in the application of synchronization and channel identification of UWB signals. The proposed reduced rank techniques can track the principal components automatically and reduce the computational complexity significantly by transforming the generalized eigen-problem in an original high dimensional space to a lower dimensional space depending on the number of desired principal signals. Technical details in the application, including the operations of sampling, fast Fourier transform (FFT) and the capture of synchronization delay, are given. Experiments show the performance is only slightly inferior to the general full rank algorithms.

Energy-efficient aggregate query evaluation in sensor networks

Tu, Meggita(Zhuoyuan); Liang, Weifa
Fonte: Springer Publicador: Springer
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.03%
Sensor networks, consisting of sensor devices equipped with energy-limited batteries, have been widely used for surveillance and monitoring environments. Data collected by the sensor devices needs to be extracted and aggregated for a wide variety of purposes. Due to the serious energy constraint imposed on such a network, it is a great challenge to perform aggregate queries efficiently. This paper considers the aggregate query evaluation in a sensor network database with the objective to prolong the network lifetime. We first propose an algorithm by introducing a node capability concept that balances the residual energy and the energy consumption at each node so that the network lifetime is prolonged. We then present an improved algorithm to reduce the total network energy consumption for a query by allowing group aggregation. We finally evaluate the performance of the two proposed algorithms against the existing algorithms through simulations. The experimental results show that the proposed algorithms outperform the existing algorithms significantly in terms of the network lifetime.

Graphical models for graph matching: Approximate models and optimal algorithms

Caelli, Terry; Caetano, Tiberio
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.03%
Comparing scene, pattern or object models to structures in images or determining the correspondence between two point sets are examples of attributed graph matching. In this paper we show how such problems can be posed as one of inference over hidden Markov random fields. We review some well known inference methods studied over past decades and show how the Junction Tree framework from Graphical Models leads to algorithms that outperform traditional relaxation-based ones.

Very fast parallel algorithms for approximate edge coloring

Han, Yijie; Liang, Weifa; Shen, Xiaojun
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.03%
This paper presents very fast parallel algorithms for approximate edge coloring. Let log(1)n=logn,log(k)n=log(log(k-1)n), and log*(n)=min{k|log(k)n<1}. It is shown that a graph with n vertices and m edges can be edge colored with (2⌈log1/4log*(n)⌉) c�

The Geometry of Weighted Low-Rank Approximations

Manton, Jonathan; Mahony, Robert; Hua, Yingbo
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.05%
The low-rank approximation problem is to approximate optimally, with respect to some norm, a matrix by one of the same dimension but smaller rank. It is known that under the Frobenius norm, the best low-rank approximation can be found by using the singular value decomposition (SVD). Although this is no longer true under weighted norms in general, it is demonstrated here that the weighted low-rank approximation problem can be solved by finding the subspace that minimizes a particular cost function. A number of advantages of this parameterization over the traditional parameterization are elucidated. Finding the minimizing subspace is equivalent to minimizing a cost function on the Grassmann manifold. A general framework for constructing optimization algorithms on manifolds is presented and it is shown that existing algorithms in the literature are special cases of this framework. Within this framework, two novel algorithms (a steepest descent algorithm and a Newton-like algorithm) are derived for solving the weighted low-rank approximation problem. They are compared with other algorithms for low-rank approximation as well as with other algorithms for minimizing a cost function on a Grassmann manifold.

Minimizing Energy and Maximizing Network Lifetime Multicasting in Wireless Ad Hoc Networks

Liang, Weifa
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
Relevância na Pesquisa
36.05%
Most mobile nodes in a wireless ad hoc network are powered by energy limited batteries, the limited battery lifetime imposes a constraint on the network performance. Therefore, energy efficiency is paramount of importance in the design of routing protocols for the applications in such a network, and efficient operations are critical to enhance the network lifetime. In this paper we consider energy-efficient routing for the minimizing energy and maximizing network lifetime multicast problem in ad hoc networks. We aim to construct a multicast tree rooted at the source and spanning the destination nodes such that the minimum residual battery energy (also referred to the network lifetime) among the nodes in the network is maximized and the total transmission energy consumption is minimized. Due to the NP-hardness of the concerned problem, all previously proposed algorithms for it are heuristic algorithms, and there is little known about the analytical performance of these algorithms in terms of approximation ratios. We here focus on devising approximation algorithms for the problem with provably guaranteed approximation ratios. Specifically, we present an approximation algorithm for finding a multicast tree such that the total transmission energy consumption is no more than γ times of the optimum...