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with convergence rate

by Francis Bach, Eric Moulines , 2013
"... Non-strongly-convex smooth stochastic approximation ..."
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Non-strongly-convex smooth stochastic approximation

Convergence rates of posterior distributions

by Subhashis Ghosal, Jayanta K. Ghosh, Aad W. Van Der Vaart - Ann. Statist , 2000
"... We consider the asymptotic behavior of posterior distributions and Bayes estimators for infinite-dimensional statistical models. We give general results on the rate of convergence of the posterior measure. These are applied to several examples, including priors on finite sieves, log-spline models, D ..."
Abstract - Cited by 110 (19 self) - Add to MetaCart
We consider the asymptotic behavior of posterior distributions and Bayes estimators for infinite-dimensional statistical models. We give general results on the rate of convergence of the posterior measure. These are applied to several examples, including priors on finite sieves, log-spline models

On the convergence rates of genetic algorithms

by Jun He, Lishan Kang - Theoretical Computer Science , 1999
"... Bounds on the convergent rate is an important problem in the foundations of genetic algorithm. This paper obtained some bounds on the convergent rate of genetic algorithms by Markov chain theory. The main result is that the algorithms convergence in geometric rate under the meaning of probability me ..."
Abstract - Cited by 14 (5 self) - Add to MetaCart
Bounds on the convergent rate is an important problem in the foundations of genetic algorithm. This paper obtained some bounds on the convergent rate of genetic algorithms by Markov chain theory. The main result is that the algorithms convergence in geometric rate under the meaning of probability

On the Component-wise Convergence Rate

by Amr El-bakry, Amr El-bakry, Trond Steihaug, Trond Steihaug
"... In this paper we investigate the convergence rate of a sequence of vectors provided that the convergence rates of the components are known. The result of this investigation is then used to study the m-step convergence rate of sequences. Key Words. convergence rate - Q-factor - multi-step convergence ..."
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In this paper we investigate the convergence rate of a sequence of vectors provided that the convergence rates of the components are known. The result of this investigation is then used to study the m-step convergence rate of sequences. Key Words. convergence rate - Q-factor - multi

Convergence Rates of Markov Chains

by Jeffrey S. Rosenthal , 1995
"... ..."
Abstract - Cited by 77 (4 self) - Add to MetaCart
Abstract not found

Convergence Rate for Consensus with Delays

by Angelia Nedić, Asuman Ozdaglar , 2007
"... We study the problem of reaching a consensus in the values of a distributed system of agents with time-varying connectivity in the presence of delays. We consider a widely studied consensus algorithm, in which at each time step, every agent forms a weighted average of its own value with values recei ..."
Abstract - Cited by 25 (2 self) - Add to MetaCart
received from the neighboring agents. We study an asynchronous operation of this algorithm using delayed agent values. Our focus is on establishing convergence rate results for this algorithm. In particular, we first show convergence to consensus under a bounded delay condition and some connectivity

Convergence rates for regularization with . . .

by Ronny Ramlau, Elena Resmerita , 2010
"... Tikhonov regularization with p-powers of the weighted ℓp norms as penalties, with p ∈ (1, 2), have been employed recently in reconstruction of sparse solutions of ill-posed inverse problems. This paper shows convergence rates for such a regularization with respect to the norm of the weighted space ..."
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Tikhonov regularization with p-powers of the weighted ℓp norms as penalties, with p ∈ (1, 2), have been employed recently in reconstruction of sparse solutions of ill-posed inverse problems. This paper shows convergence rates for such a regularization with respect to the norm of the weighted

CONVERGENCE RATES OF ORTHOGONAL SERIES

by Regression Estimators
"... Abstract. General conditions for convergence rates of nonparametric or-thogonal series estimators of the regression function f(x) = E(Y |X = x) are considered. The estimators are obtained by the least squares method on the basis of a random observation sample (Yi,Xi), i = 1,..., n, where Xi ∈ A ⊂ R ..."
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Abstract. General conditions for convergence rates of nonparametric or-thogonal series estimators of the regression function f(x) = E(Y |X = x) are considered. The estimators are obtained by the least squares method on the basis of a random observation sample (Yi,Xi), i = 1,..., n, where Xi ∈ A

Convergence Rate of Incremental Subgradient Algorithms

by Angelia Nedic, Dimitri Bertsekas - Stochastic Optimization: Algorithms and Applications , 2000
"... We consider a class of subgradient methods for minimizing a convex function that consists of the sum of a large number of component functions. This type of minimization arises in a dual context from Lagrangian relaxation of the coupling constraints of large scale separable problems. The idea is to p ..."
Abstract - Cited by 65 (6 self) - Add to MetaCart
squares problems, such as those arising in the training of neural networks, and it has resulted in a much better practical rate of convergence than the steepest descent method. In this paper, we present convergence results and estimates of the convergence rate of a number of variants of incremental

On the convergence rate of the unscented transformation

by Kwang Woo Ahn , 2011
"... Nonlinear state-space models driven by differential equations have been widely used in science. Their statistical inference generally requires computing the mean and covariance matrix of some nonlinear function of the state variables, which can be done in several ways. For example, such computations ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
by Julier and Uhlmann (1997) to overcome these difficulties, but it lacks of theoretical justification. In this paper, we derive some theoretical properties of the unscented transformation and contrast it with the method of linear approximation. Particularly, we derive the convergence rate of the unscented
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