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Greedy sparsityconstrained optimization
 in Signals, Systems and Computers (ASILOMAR), 2011 Conference Record of the Forty Fifth Asilomar Conference on, IEEE, 2011
"... Abstract—Finding optimal sparse solutions to estimation problems, particularly in underdetermined regimes has recently gained much attention. Most existing literature study linear models in which the squared error is used as the measure of discrepancy to be minimized. However, in many applications d ..."
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Cited by 20 (4 self)
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Abstract—Finding optimal sparse solutions to estimation problems, particularly in underdetermined regimes has recently gained much attention. Most existing literature study linear models in which the squared error is used as the measure of discrepancy to be minimized. However, in many applications
Approximate inference and constrained optimization
 In 19th UAI
, 2003
"... Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these algorithms correspond to extrema of the Bethe and Kikuchi free energy (Yedidia et al., 2001). However, belief propagation does not always con ..."
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Cited by 62 (9 self)
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converge, which motivates approaches that explicitly minimize the Kikuchi/Bethe free energy, such as CCCP (Yuille, 2002) and UPS (Teh and Welling, 2002). Here we describe a class of algorithms that solves this typically nonconvex constrained minimization problem through a sequence of convex constrained
Transliteration as constrained optimization
 In Proc. EMNLP
, 2008
"... This paper introduces a new method for identifying namedentity (NE) transliterations in bilingual corpora. Recent works have shown the advantage of discriminative approaches to transliteration: given two strings (ws, wt) in the source and target language, a classifier is trained to determine if wt ..."
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Cited by 18 (4 self)
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is the transliteration of ws. This paper shows that the transliteration problem can be formulated as a constrained optimization problem and thus take into account contextual dependencies and constraints among character bigrams in the two strings. We further explore several methods for learning the objective function
Neural Geometry for Constrained Optimization
"... Many engineering problems require the online solution of constrained optimization problems. This paper proposes, as an original contribution, particular neural architectures, called neural solids, whose interconnections represent the required constraints. The first neural solid presented here is th ..."
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Many engineering problems require the online solution of constrained optimization problems. This paper proposes, as an original contribution, particular neural architectures, called neural solids, whose interconnections represent the required constraints. The first neural solid presented here
PDECONSTRAINED OPTIMIZATION PROBLEMS
"... Abstract. We investigate the use of a preconditioning technique for solving linear systems of saddle point type arising from the application of an inexact Gauss–Newton scheme to PDEconstrained optimization problems with a hyperbolic constraint. The preconditioner is of block triangular form and inv ..."
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Abstract. We investigate the use of a preconditioning technique for solving linear systems of saddle point type arising from the application of an inexact Gauss–Newton scheme to PDEconstrained optimization problems with a hyperbolic constraint. The preconditioner is of block triangular form
Constrained Optimalities in Query Personalization
 In Proceedings of the ACM International Conference on Management of Data, SIGMOD
, 2005
"... Personalization is a powerful mechanism that helps users to cope with the abundance of information on the Web. Database query personalization achieves this by dynamically constructing queries that return results of high interest to the user. This, however, may conflict with other constraints on the ..."
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Cited by 16 (4 self)
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CQP as a family of constrained optimization problems, where each time one of the parameters of concern is optimized while the others remain within the bounds of range constraints. Taking into account some key (exact or approximate) properties of these parameters, we map CQP to a state search problem
Distributed Constrained Optimization
"... We demonstrate a new framework for analyzing and controlling distributed systems, by solving constrained optimization problems with an algorithm based on that framework. The framework is an informationtheoretic extension of conventional fullrationality game theory to allow bounded rational agents. ..."
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Cited by 1 (0 self)
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We demonstrate a new framework for analyzing and controlling distributed systems, by solving constrained optimization problems with an algorithm based on that framework. The framework is an informationtheoretic extension of conventional fullrationality game theory to allow bounded rational agents
Turbo decoding as constrained optimization
 in 43rd Allerton Conference on Communication, Control, and Computing
, 2005
"... The turbo decoder was not originally introduced as a solution to an optimization problem. This has made explaining just why the turbo decoder performs as well as it does very difficult. Many authors have attempted to explain both the performance and convergence of the decoder, with varied success. I ..."
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Cited by 2 (2 self)
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. In this document we show that the turbo decoder admits an exact interpretation as an iterative method (nonlinear block Gauss Seidel iteration) attempting to find a solution to a particular intuitively pleasing constrained optimization problem. In particular the turbo decoder is trying to find the maximum
Parallel Multisplittings for Constrained Optimization
, 1994
"... . The philosophy of multisplitting methods is the replacement of a largescale linear or nonlinear problem by a set of smaller subproblems, each of which can be solved locally and independently in parallel by taking advantage of welltested sequential algorithms. Because of this formulation most com ..."
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Cited by 1 (1 self)
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both serial and parallel are reported which demonstrate its efficiency and which also show that it compares favorably to our earlier parameterdependent approach. Key words. Parallel algorithms, multisplitting, constrained optimization. Computing Reviews. D.1.3, G.1.6 1. Introduction We consider
Nonconvex Constrained Optimization
, 2007
"... Fast nonlinear programming methods following the allatonce approach usually employ Newton’s method for solving linearized KarushKuhnTucker (KKT) systems. In nonconvex problems, the Newton direction is only guaranteed to be a descent direction if the Hessian of the Lagrange function is positive d ..."
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in optimization methods is verified on an interior point method applied to the CUTE and COPS test problems, on largescale 3D PDEconstrained optimal control problems, as well as 3D
Results 11  20
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