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非线性互补问题的一种全局收敛的显式光滑Newton方法 总被引:2,自引:0,他引:2
本针对Po函数非线性互补问题,给出了一种显式光滑Newton方法,该方法将光滑参数μ进行显式迭代而不依赖于Newton方向的搜索过程,并在适当的假设条件下,证明了算法的全局收敛性。 相似文献
3.
L. Aggoun 《Mathematical and Computer Modelling》2002,36(11-13)
In this paper, finite-dimensional recursive filters for space-time Markov random fields are derived. These filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the parameters of the model. 相似文献
4.
Oracle inequality is a relatively new statistical tool for the analysis of nonparametric adaptive estimates. Oracle is a good pseudo-estimate that is based on both data and an underlying estimated curve. An oracle inequality shows how well an adaptive estimator mimics the oracle for a particular underlying curve. The most advanced oracle inequalities have been recently obtained by Cavalier and Tsybakov (2001) for Stein type blockwise estimates used in filtering a signal from a stationary white Gaussian process. The authors also conjecture that a similar result can be obtained for Efromovich–Pinsker (EP) type blockwise estimators where their approach, based on Stein's formula for risk calculation, does not work. This article proves the conjecture and extends it upon more general models which include not stationary and dependent processes. Other possible extensions, a discussion of practical implications and a numerical study are also presented. 相似文献
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Bjorn Fritzell 《Journal of voice》1992,6(2)
Inverse filtering is a noninvasive method of producing a glottogram thought to reflect the vibratory motions of the vocal fold. The flow glottogram provides information related to the type of phonation, the sound pressure level, the regularity of vocal fold vibrations, and to the presence or absence of vocal fold closure. Limited data, speech samples, filter tuning, and lack of unanimity on waveform display and interpretation have contributed to slow application of the techniques to the clinical population. The author argues the technique has utility in both diagnosis and treatment 相似文献
8.
Many recent algorithmic approaches involve the construction of a differential equation model for computational purposes, typically
by introducing an artificial time variable. The actual computational model involves a discretization of the now time-dependent differential system, usually
employing forward Euler. The resulting dynamics of such an algorithm is then a discrete dynamics, and it is expected to be
“close enough” to the dynamics of the continuous system (which is typically easier to analyze) provided that small – hence
many – time steps, or iterations, are taken. Indeed, recent papers in inverse problems and image processing routinely report
results requiring thousands of iterations to converge. This makes one wonder if and how the computational modeling process
can be improved to better reflect the actual properties sought.
In this article we elaborate on several problem instances that illustrate the above observations. Algorithms may often lend
themselves to a dual interpretation, in terms of a simply discretized differential equation with artificial time and in terms
of a simple optimization algorithm; such a dual interpretation can be advantageous. We show how a broader computational modeling
approach may possibly lead to algorithms with improved efficiency.
AMS subject classification (2000) 65L05, 65M32, 65N21, 65N22, 65D18 相似文献
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A. Budhiraja 《Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques》2003,39(6):2318-941
In this work we study connections between various asymptotic properties of the nonlinear filter. It is assumed that the signal has a unique invariant probability measure. The key property of interest is expressed in terms of a relationship between the observation σ field and the tail σ field of the signal, in the stationary filtering problem. This property can be viewed as the permissibility of the interchange of the order of the operations of maximum and countable intersection for certain σ-fields. Under suitable conditions, it is shown that the above property is equivalent to various desirable properties of the filter such as
- (a) uniqueness of invariant measure for the signal,
- (b) uniqueness of invariant measure for the pair (signal, filter),
- (c) a finite memory property of the filter,
- (d) a property of finite time dependence between the signal and observation σ fields and
- (e) asymptotic stability of the filter.