Results 21 to 30 of about 3,698,697 (306)
On the convergence rates of asynchronous iterations
This paper presents a unifying convergence result for asynchronous iterations involving pseudo-contractions in the block-maximum norm. Contrary to previous results which only established asymptotic convergence or studied simplified models of asynchronism, our result allows to bound the convergence rates for both partially and totally asynchronous ...
Feyzmahdavian, Hamid Reza +1 more
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A unified approach for regularizing discretized linear ill‐posed problems
In this paper we deal with regularization approaches for discretized linear ill‐posed problems in Hilbert spaces. As opposite to other contributions concerning this topic the smoothness of the unknown solution is measured with so‐called approximative ...
Torsten Hein
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In this paper, Landweber iteration with a relaxation factor is proposed to solve nonlinear ill-posed integral equations. A compression multiscale Galerkin method that retains the properties of the Landweber iteration is used to discretize the Landweber ...
Rong Zhang, Fanchun Li, Xingjun Luo
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Convergence Rates for Generalized Descents [PDF]
d-descents are permutation statistics that generalize the notions of descents and inversions. It is known that the distribution of d-descents of permutations of length n satisfies a central limit theorem as n goes to infinity. We provide an explicit formula for the mean and variance of these statistics and obtain bounds on the rate of convergence using
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On the convergence rates of pairs of adjacent sequences
In this paper we give a suitable definition for the pairs of adjacent (convergent) sequences of real numbers, we present some two-sided estimations which caracterize the order of convergence to its limits of some of these sequences and we give certain ...
Dorel I. Duca, Andrei Vernescu
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Ensemble Estimation of Information Divergence †
Recent work has focused on the problem of nonparametric estimation of information divergence functionals between two continuous random variables. Many existing approaches require either restrictive assumptions about the density support set or difficult ...
Kevin R. Moon +3 more
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Smooth Function Approximation by Deep Neural Networks with General Activation Functions
There has been a growing interest in expressivity of deep neural networks. However, most of the existing work about this topic focuses only on the specific activation function such as ReLU or sigmoid.
Ilsang Ohn, Yongdai Kim
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GROWTH RATES, QUALITY OF ECONOMIC GROWTH AND CONVERGENCE OF GROWTH RATES [PDF]
The aim of this analysis is to examine methodological possibilities for defining the interdependence of the key categories of production theory. The ultimate goal of this paper is to define relationships pointing to the interdependence of growth rates ...
NADA TRIVIĆ, BOJANA TODIĆ
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On the Rate of Convergence of Greedy Algorithms
In this paper, a new criterion for the evaluation of the theoretical efficiency of a greedy algorithm is suggested. Using this criterion, we prove some results on the rate of convergence of greedy algorithms, which provide expansions. We consider both the case of Hilbert spaces and the more general case of Banach spaces. The new component of this paper
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Rate of convergence of Lawson’s algorithm [PDF]
The algorithm of Charles L. Lawson determines uniform approximations of functions as limits of weighted L 2
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