Problems Arising With Least Squares Analysis
CiteSeerX — Rank Degeneracy and Least Squares Problems
... The solution of this problem is important in a number of applications. In this paper we shall be chie
y interested in the case where the columns of A represent factors or carriers in a linear model which is to be t to a ...
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BTTB preconditioners for BTTB least squares problems
... Block circulant preconditioners for block systems have been intensively studied, see for instance [2,3,17,18]. Chan and Olkin [12] and Holmgren and Otto [15], in solving noise reduction problems and hyperbolic ...
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Least Squares Problems with Inequality Constraints as Quadratic Constraints
... test problems, in the constrained and unconstrained cases, Figures 4(b)(c), 6(b)(c) and 8(b)(c) each show that the χ 2 estimate gives results as good as the MAP ...solution. Least squares solutions ...
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The conditioning of least squares problems in variational data assimilation
... provide timely forecasts. In this work, we investigate how introducing correlated observation errors affects the condition number of the Hessian and examine the associated speed of convergence of a conjugate gradient ...
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Least squares problems with inequality constraints as quadratic constraints
... equality constraint [ 17 ]. It is difficult to know the active set a priori but algorithms for it include Bounded Variable Least Squares (BVLS) given in [ 20 ]. These methods can be expensive for large-scale ...
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Tikhonov regularization for weighted total least squares problems
... total least squares (RWTLS) ...total least squares problem is based on the Tikhonov regularization [ 1 ...total least squares (TLS) problem [ 2 ], the truncation approach has ...
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GALS – Gradient Analysis by Least Squares
... Panels (i) of Figs. 4 and 5 show the structure velocity obtained in the GALS reconstruction of the B z data which causes the dominant part of the current sheet. Using a large coherence time, T c = 6 s as in Fig. 5, we ...
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Deformation analysis with Total Least Squares
... This study focuses on the use of TLS approach for geode- tic deformation analysis. For comparison a traditional ap- proach, namely similarity transformation has also been ap- plied on the same data set. The big ...
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A superfast method for solving Toeplitz linear least squares problems
... Received 16 May 2001; accepted 10 June 2002 Submitted by D.A. Bini Abstract In this paper we develop a superfast O((m + n) log 2 (m + n)) complexity algorithm to solve a linear least squares problem with an ...
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The state-of-the-art of preconditioners for sparse linear least-squares problems
... individual problems one at a ...are least-squares problems for which SYM-ILDL is able to provide an effective preconditioner but for other problems we were unsuccessful in obtaining the ...
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Numerical methods for least squares problems with application to data assimilation
... 4DVAR problems (ensemble based methods) The aim of this chapter is to present the application of the approach developed in the previ- ous chapter to data assimilation problems ...
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A predictor corrector iterative method for solving linear least squares problems and perturbation error analysis
... error analysis of the proposed method for the minimum norm solution of least squares problems is pro- ...experimental analysis are conducted for the one- dimensional image restoration ...
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Newton Krylov Type Algorithm for Solving Nonlinear Least Squares Problems
... Several authors have studied inexact Newton’s methods for solving NLS problems 11. Xiaofang et al. have introduced stable factorized quassi-Newton methods for solving large-scale NLS 12. Dennis et al. proposed a ...
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Solving polynomial least squares problems via semidefinite programming relaxations
... and control theory, their global optimization has not been dealt with extensively. Recently, solving polynomial SDPs with the use of SDP relaxations has been studied in [9, 10, 17]. The convergence of polynomial SDP ...
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Reducing Communication in Proximal Newton Methods for Sparse Least Squares Problems
... Rutgers University [email protected] ABSTRACT Proximal Newton methods are iterative algorithms that solve l1- regularized least squares problems. Distributed-memory implemen- tation of these ...
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Sparse stretching for solving sparse-dense linear least-squares problems
... LS problems. Stretching for LS problems has been used in the past [1, 2] but, as we have illustrated using some problems with very basic sparsity structures, standard stretching can lead to ...
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Using Perturbed QR Factorizations To Solve Linear Least-Squares Problems
... Both solutions are good, but they are dierent; this is a re ection of the ill conditioning of the problem. 8. Conclusions This paper presented theoretical analysis of certain preconditioned least- square ...
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Discrete ill-posed least-squares problems with a solution norm constraint
... ill-posed least-squares problems with error-contaminated data does not, in general, give meaning- ful results, because propagated error destroys the computed so- ...error-free ...
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V -invariant methods, generalised least squares problems, and the Kalman filter
... Our initial implementation has proved satisfactory in early experiments. In particular, there has been good control over the magnitudes occurring. The information filter has been used previously in problems of ...
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Analysis of SOSTTC-OFDM based on Least Squares Method
... the analysis and the simulation are very close, therefore for the high SNR region, the calculated BER values can be used instead of simulation, which can be time- ...
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