A computational algorithm for optimal control problems with control and terminal inequality constraints involving first boundary-value problems of parabolic type is presented. The convergence properties are also studied.
A computational algorithm for optimal control problems with control and terminal inequality constraints involving first boundary-value problems of parabolic type is presented. The convergence properties are also studied.
Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the natur...
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Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations that grows with the dimension of the problem and with the complexity of the data behind it. Several techniques are available toward accelerating the convergence of these Monte Carlo algorithms, either at the exploration level (as in tempering, Hamiltonian Monte Carlo and partly deterministic methods) or at the exploitation level (with Rao-Blackwellization and scalable methods). This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) algorithms and Computational Methods > algorithms Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods
In this paper, we consider a convex optimal control problem involving a class of linear hyperbolic partial differential systems. A computational algorithm which generates minimizing sequences of controls is devised. T...
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In this paper, we consider a convex optimal control problem involving a class of linear hyperbolic partial differential systems. A computational algorithm which generates minimizing sequences of controls is devised. The convergence properties of the algorithm are investigated.
The mean-squared error estimator was used by stardard BP algorithms. Therefore this algorithms might get trapped in some local minimum,become slow convergence and be sensitive to initial weight values etc. In this pap...
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ISBN:
(纸本)7505338900
The mean-squared error estimator was used by stardard BP algorithms. Therefore this algorithms might get trapped in some local minimum,become slow convergence and be sensitive to initial weight values etc. In this paper, a kind of new robust BP mathematical theory which based on Lagrangian multiplier method and several kinds of robust error estimators is investigated in detail. Robust BP algorithms are obtained. Expriment illastrate: our algorithms not only converge fast and are less sensitive to initial weight values, but also can overcome the inflence of ''outlier''. algorithms are robust for little noise percturbation and gross error.
This paper considers an optimal control problem involving linear, hyperbolic partial differential equations. A first-order strong variational technique is used to obtain an algorithm for solving the optimal control pr...
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In this paper, we describe a new primal-dual path-following method to solve a convex quadratic program (QP). The derived algorithm is based on new techniques for finding a new class of search directions similar to the...
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In this paper, we describe a new primal-dual path-following method to solve a convex quadratic program (QP). The derived algorithm is based on new techniques for finding a new class of search directions similar to the ones developed in a recent paper by Darvay for linear programs. We prove that the short-update algorithm finds an epsilon-solution of (QP) in a polynomial time.
The penalty methods constitute a family of particularly interesting algorithms of the two points of view: the simpleness of principle and the practical efficiency. They are considered as ill-conditioned because the pe...
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The penalty methods constitute a family of particularly interesting algorithms of the two points of view: the simpleness of principle and the practical efficiency. They are considered as ill-conditioned because the penalty parameter r(k) tends to infinity, as k tends infinity. It is what led to us to introduce what we call augmented Lagrangian to regularize the solutions. Namely, to avoid the numerical instability of the classical penalty method. Another favors of the augmented Lagrangian is that by presence of the term lambda(i) g(i), the exact solution of the problem can be determined without making aim rk towards the infinity, contrary to the penalty method, where it has the effect of diverting the packaging of the problem to be resolved. The using of augmented Lagrangian is considered as an improvement of the penalty methods. It avoids having to use too big parameters of penalties. Besides, the fact of adding the quadratic term r(k)(g(+))(2) k in the Lagrangian will improve the properties of convergence of the algorithms of duality in this paper. In this paper, we study some augmented Lagrangian algorithms applied to convex nondiff erentiable optimization problems and prove their convergence.
We propose a probabilistic setting in which we study the probability law of the Rajaraman and Ullman \textit{RU} algorithm and a modified version of it denoted by \textit{RUM}. These algorithms aim at estimating the s...
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We propose a probabilistic setting in which we study the probability law of the Rajaraman and Ullman \textit{RU} algorithm and a modified version of it denoted by \textit{RUM}. These algorithms aim at estimating the similarity index between huge texts in the context of the web. We give a foundation of this method by showing, in the ideal case of carefully chosen probability laws, the exact similarity is the mathematical expectation of the random similarity provided by the algorithm. Some extensions are given. \noindent \textbf{Résumé.} Nous proposons un cadre probabilistique dans lequel nous étudions la loi de probabilité de l'algorithme de Rajaraman et Ullman \textit{RU} ainsi qu'une version modifiée de cet algorithme notée \textit{RUM}. Ces alogrithmes visent à estimer l'indice de la similarité entre des textes de grandes tailles dans le contexte du Web. Nous donnons une base de validité de cette méthode en montrant que pour des lois de probabilités minutieusement choisies, la similarité exacte est l'espérance mathématique de la similarité aléatoire donnée par l'algorithme \textit{RUM}. Des généralisations sont abordées.
The mean-squared error estimator was used by stardard BP *** this algorithms might get trapped in some local minimum,become slow convergence and be sensitive to initial weight values *** this paper,a kind of new robus...
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The mean-squared error estimator was used by stardard BP *** this algorithms might get trapped in some local minimum,become slow convergence and be sensitive to initial weight values *** this paper,a kind of new robust BP mathematical theory which based on Lagrangian multiplier method and several kinds of robust error estimators is investigated in *** BP algorithms are *** illastrate:our algorithms not only converge fast and are less sensitive to initial weight values,but also can overcome the inflence of " outlier",algorithms are robust for little noise percturbation and gross error.
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