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排序方式: 共有332条查询结果,搜索用时 31 毫秒
1.
Adrian Doicu Franz Schreier Michael Hess 《Journal of Quantitative Spectroscopy & Radiative Transfer》2004,83(1):47-61
In this paper we present different inversion algorithms for nonlinear ill-posed problems arising in atmosphere remote sensing. The proposed methods are Landweber's method (LwM), the iteratively regularized Gauss-Newton method, and the conventional and regularizing Levenberg-Marquardt method. In addition, some accelerated LwMs and a technique for smoothing the Levenberg-Marquardt solution are proposed. The numerical performance of the methods is studied by means of simulations. Results are presented for an inverse problem in atmospheric remote sensing, i.e., temperature sounding with an airborne uplooking high-resolution far-infrared spectrometer. 相似文献
2.
In the present work we consider the problem of interpolating scattered data using radial basis functions (RBF). In general, it is well known that this leads to a discrete linear inverse problem that needs to be regularized in order to provide a meaningful solution. The work focuses on a metric-regularization approach, based on a new class of RBF, called anisotropic RBF. The work provides theoretical justifications for the regularization approach and it considers a suitable proposal for the metric, supporting it by numerical examples. 相似文献
3.
An adaptive pruning algorithm for the discrete L-curve criterion 总被引:1,自引:0,他引:1
Per Christian Hansen Toke Koldborg Jensen Giuseppe Rodriguez 《Journal of Computational and Applied Mathematics》2007
We describe a robust and adaptive implementation of the L-curve criterion. The algorithm locates the corner of a discrete L-curve which is a log–log plot of corresponding residual norms and solution norms of regularized solutions from a method with a discrete regularization parameter (such as truncated SVD or regularizing CG iterations). Our algorithm needs no predefined parameters, and in order to capture the global features of the curve in an adaptive fashion, we use a sequence of pruned L-curves that correspond to considering the curves at different scales. We compare our new algorithm to existing algorithms and demonstrate its robustness by numerical examples. 相似文献
4.
Henry Wolkowicz 《Mathematical Programming》1980,19(1):32-60
The cones of directions of constancy are used to derive: new as well as known optimality conditions; weakest constraint qualifications; and regularization techniques, for the convex programming problem. In addition, the badly behaved set of constraints, i.e. the set of constraints which causes problems in the Kuhn—Tucker theory, is isolated and a computational procedure for checking whether a feasible point is regular or not is presented.This research was supported by the National Research Council of Canada and le Gouvernement du Quebec and is part of the author's Ph.D. Dissertation done at McGill University, Montreal, Que., under the guidance of Professor S. Zlobec. 相似文献
5.
We consider a Cauchy problem for the modified Helmholtz equation. A conditional stability estimate is proved. Two order optimal regularization methods and error estimates are investigated. Numerical experiment shows that these methods work well. 相似文献
6.
Over the last couple of years molecular imaging has been rapidly developed to study physiological and pathological processes in vivo at the cellular and molecular levels. Among molecular imaging modalities, optical imaging stands out for its unique advantages, especially performance and cost-effectiveness. Bioluminescence tomography (BLT) is an emerging optical imaging mode with promising biomedical advantages. In this survey paper, we explain the biomedical significance of BLT, summarize theoretical results on the analysis and numerical solution of a diffusion based BLT model, and comment on a few extensions for the study of BLT. 相似文献
7.
Rudolf Beran 《Annals of the Institute of Statistical Mathematics》2008,60(4):843-864
The unknown matrix M is the mean of the observed response matrix in a multivariate linear model with independent random errors. This paper constructs
regularized estimators of M that dominate, in asymptotic risk, least squares fits to the model and to specified nested submodels. In the first construction,
the response matrix is expressed as the sum of orthogonal components determined by the submodels; each component is replaced
by an adaptive total least squares fit of possibly lower rank; and these fits are then summed. The second, lower risk, construction
differs only in the second step: each orthogonal component is replaced by a modified Efron-Morris fit before summation. Singular
value decompositions yield computable formulae for the estimators and their asymptotic and estimated risks. In the asymptotics,
the row dimension of M tends to infinity while the column dimension remains fixed. Convergences are uniform when signal-to-noise ratio is bounded.
This research was supported in part by National Science Foundation Grant DMS 0404547. 相似文献
8.
As it is not possible to obtain an accurate point spread function (PSF) in remote sensing imaging, classic deconvolution methods such as Wiener filtering often introduce strong noise and ringing artifacts, which contaminate the restored images. In this paper, we modify the standard Richarson-Lucy (RL) algorithm with a piecewise local regularization term and combine it with residual deconvolution method. Experimental results show that it is effective in suppressing negative effects, and images with rich details and sharp edges are obtained. 相似文献
9.
We solve a general variational inequality problem in a finite-dimensional setting, where only approximation sequences are known instead of exact values of the cost mapping and feasible set. We suggest to utilize a sequence of solutions of auxiliary problems based on a penalty method. Its convergence is attained without concordance of penalty and approximation parameters under mild coercivity type conditions. We also show that the regularized version of the penalty method enables us to further weaken the coercivity condition. 相似文献
10.
《复变函数与椭圆型方程》2012,57(4):285-301
Let U be the open unit disc of the complex plane. We explicitly construct the best pointwise approximation for determining a function in the Hardy space H p (U) from measured data at a countable set of points in U whenever the data is exact. A regularization scheme is given to deal with the case of nonexact data. Moreover, optimal error estimates are also studied. 相似文献