Least-squares Problem and Linear Programming
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[.] ...
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Least squares estimation of a shift in linear processes
... where B(s) is a standard Brownian motion on [0,1]. 3. The consistency of τ ˆ The proof of consistency is almost standard. Recall how we prove, in general, the con- sistency of an estimator obtained by maximizing an ...
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Updating QR factorization procedure for solution of linear least squares problem with equality constraints
... for linear least squares problem with equality ...constrained problem to unconstrained linear least squares and partition it into a small ...
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A note on the scaled total least squares problem
... total least squares (STLS) problem is a generalization of the total least squares (TLS) ...TLS problem is to find E ∈ R m×n and r ∈ R m solving the problem 聻 The first and ...
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Fuzzy Least Squares Linear Regression Approach to Ascertain Stochastic Demand in the Vehicle Routing Problem
... fuzzy least-squares ...routing problem in which a set of cus- tomers is given, each of which will require service after the a priori decision is ...Fuzzy least squares linear ...
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Bounded perturbation regularization for linear least squares estimation
... the problem of selecting the regularization parameter for linear least- squares ...the linear transformation matrix to improve the singular-value ...the problem is formulated as ...
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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 ...
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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] ...
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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 ...
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The element wise weighted total least squares problem
... a linear measurement error model AX ≈ B, A = A 0 + ˜ A, B = B 0 + ˜ B, A 0 X 0 = B 0 with row-wise independent and non-identically distributed measurement errors A ˜ , B ˜ ...total least-squares ...
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A superfast method for solving Toeplitz linear least squares problems
... The problem with many fast and superfast algorithms is that their numerical per- formance heavily depends on the condition of certain submatrices. They even may breakdown if some of these submatrices are singular. ...
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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 ...
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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 , ...
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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 ...
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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 ...
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The state-of-the-art of preconditioners for sparse linear least-squares problems
... 10.5. Summary tables In Tables II–VI, we present summary data that allows a direct comparison of a particular statistic across the range of methods considered. We remove SPQR and HSL MA97 (for the augmented system) as ...
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Least Squares Solutions of Inconsistent Fuzzy Linear Matrix Equations
... fuzzy linear matrix equations (shown as IFLME) of the form AXB = C for finding its fuzzy least squares solutions is studied in this ...of linear equations and found its fuzzy least ...
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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 ...
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Regularization methods for the solution of a nonlinear least-squares problem in tomography
... posed problem, we will need to present a physical model for the formation of the image, that will be multimaterial and ...the problem will be formulated in terms of non linear least ...
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Current identification in vacuum circuit breakers as a least squares problem*
... inverse problem needs to be solved, allowing the determination of the causes, ...inverse problem to solve for the current reconstruction in the circuit breaker ...the linear algebra framework, we are ...
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