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An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

by Ingrid Daubechies, Michel Defrise, Christine De Mol , 2008
"... ..."
Abstract - Cited by 747 (9 self) - Add to MetaCart
Abstract not found

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

SNOPT: An SQP Algorithm For Large-Scale Constrained Optimization

by Philip E. Gill, Walter Murray, Michael A. Saunders , 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
Abstract - Cited by 597 (24 self) - Add to MetaCart
Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first

Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

by N. Srinivas, Kalyanmoy Deb - Evolutionary Computation , 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
Abstract - Cited by 539 (5 self) - Add to MetaCart
the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms(GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a

Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization

by Carlos M. Fonseca, Peter J. Fleming , 1993
"... The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
Abstract - Cited by 633 (15 self) - Add to MetaCart
The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified

Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems

by Mário A. T. Figueiredo, Robert D. Nowak, Stephen J. Wright - IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING , 2007
"... Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a spa ..."
Abstract - Cited by 539 (17 self) - Add to MetaCart
Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a

A training algorithm for optimal margin classifiers

by Bernhard E. Boser, et al. - PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY , 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
Abstract - Cited by 1865 (43 self) - Add to MetaCart
-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.

Fibonacci Heaps and Their Uses in Improved Network optimization algorithms

by Michael L. Fredman, Robert Endre Tarjan , 1987
"... In this paper we develop a new data structure for implementing heaps (priority queues). Our structure, Fibonacci heaps (abbreviated F-heaps), extends the binomial queues proposed by Vuillemin and studied further by Brown. F-heaps support arbitrary deletion from an n-item heap in qlogn) amortized tim ..."
Abstract - Cited by 739 (18 self) - Add to MetaCart
time and all other standard heap operations in o ( 1) amortized time. Using F-heaps we are able to obtain improved running times for several network optimization algorithms. In particular, we obtain the following worst-case bounds, where n is the number of vertices and m the number of edges

Optimization Flow Control, I: Basic Algorithm and Convergence

by Steven H. Low, David E. Lapsley - IEEE/ACM TRANSACTIONS ON NETWORKING , 1999
"... We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using gradient projection algorithm. In thi ..."
Abstract - Cited by 694 (64 self) - Add to MetaCart
We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using gradient projection algorithm

Dynamic programming algorithm optimization for spoken word recognition

by Hiroaki Sakoe, Seibi Chiba - IEEE TRANSACTIONS ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING , 1978
"... This paper reports on an optimum dynamic programming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using timewarping function. Then, two time-normalized distance definitions, ded symmetric and asymmetric forms, are der ..."
Abstract - Cited by 788 (3 self) - Add to MetaCart
words in different The effective slope constraint characteristic is qualitatively analyzed, and the optimum slope constraint condition is determined through experiments. The optimized algorithm is then extensively subjected to experimentat comparison with various DP-algorithms, previously applied
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