linear least squares
Linear least squares localization in sensor networks
... Linear least squares localization in sensor networks Wang EURASIP Journal on Wireless Communications and Networking (2015) 2015 51 DOI 10 1186/s13638 015 0298 1 RESEARCH Open Access Linear least squar[.] ...
7
Bounded perturbation regularization for linear least squares estimation
... for linear least- squares ...the linear transformation matrix to improve the singular-value ...-regularized least squares problem, with the unknown regularizer related to the ...
12
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 ...
17
The state-of-the-art of preconditioners for sparse linear least-squares problems
... of linear least-squares software includes the test examples PDE1, IMDB, GLRD17–21, NotreDame actors, TF17–19 and wheel 601, since these challenge many of the methods we have considered ...
41
Linear least squares estimation of the first order moving average parameter
... of squares function which avoids the nonlinear nature of estimating the ¿rst order moving average parameter and provides a closed form of the ...the linear least squares estimator is proved ...
25
Some Insight into the Generalized Linear Least Squares Parameter Adjustment Methodology
... generalized linear least squares parameter adjustment procedure have been discussed and ...a linear function of the parameters and the equivalence of the simultaneous adjustment of the ...
7
Communication efficient distributed weighted non linear least squares estimation
... Abstract The paper addresses design and analysis of communication-efficient distributed algorithms for solving weighted non-linear least squares problems in multi-agent networks. Communication ...
15
Sparse stretching for solving sparse-dense linear least-squares problems
... SPARSE-DENSE LINEAR LEAST-SQUARES PROBLEMS JENNIFER SCOTT ∗ AND MIROSLAV T˚ UMA † ...Large-scale linear least-squares problems arise in a wide range of practical ...the ...
21
A Regularized Interior-Point Method for Constrained Linear Least Squares
... minimize x ∈R n ,w ∈R m c T x + 1 2 ∥Ax − d∥ 2 + 1 2 ρ ∥x − x k ∥ 2 + 1 2 δ ∥w + y k ∥ 2 subject to Bx + δw = b, x ≥ 0, (1.2) where ρ > 0 and δ > 0 are regularization parameters, x k and y k are the current ...
91
Numerical investigations of linear least squares methods for derivative estimation
... for least squares estimates of function gradients are ...a least squares problem using a truncated Taylor ...the least squares ...
14
Using Perturbed QR Factorizations To Solve Linear Least-Squares Problems
... a sparse QR factorization of a low-rank perturbation ˆ A of A. More pre- cisely, we show that the R factor of ˆ A is an eective preconditioner for the least-squares problem min x kAx−bk 2 , when solved ...
27
Coordinate and subspace optimization methods for linear least squares with non-quadratic regularization
... To overcome this difficulty, the parallel coordinate descent (PCD) algorithm is proposed. Rather than updating each coordinate sequentially, the whole set of coordinates is updated simultaneously at the current point z k ...
22
Least squares estimation of a shift in linear processes
... The asymptotic distribution for the change point estimator is obtained when the mag- nitude of shift is small. It is shown that serial correlation affects the variance of the change point estimator via the sum of the ...
31
The widely linear quaternion recursive total least squares
... total least squares (TLS) is known to yield a better approximate and robust solution to systems of linear equations, when the variables of both sides are contaminated by noise ...total least ...
5
On convex least squares estimation when the truth is linear
... Groeneboom, Jongbloed and Wellner ( 2001b ) show that under the convexity constraint, the least squares estimator (LSE) can be used to estimate both a density and a regression function..[r] ...
40
THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
... LINEAR REGRESSION LINEAR REGRESSION is a powerfull tool for studying fundamental relationships between two (or more) RVs Y and X. The method is based on the method of least squares. Let’s ...
16
No penalty no tears : least squares in high dimensional linear models
... Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample ...involving least squares ...
27
1 Simple Linear Regression I Least Squares Estimation
... S P A C E 0 3 6 9 1 2 Figure 6: Plot of coffee data, fitted equation, and the line y = 515.4167 These three pieces are called the total, error, and model sums of squares, respectively. We denote them as SS yy , ...
70
10 Regression, including Least-Squares Linear and Logistic Regression
... [Apparently, least-squares linear regression was first posed and solved in 1801 by the great mathematician Carl Friedrich Gauss, who used least-squares regression to predict the ...
5