Linear Least-Squares
Linear least squares localization in sensor networks
... rescue. Linear least squares (LLS) estimation is a sub-optimum but low-complexity localization algorithm based on measurements of location-related ...
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Communication efficient distributed weighted non linear least squares estimation
... We consider distributed non-linear least squares estima- tion in networked systems. The networked system con- sidered consists of heterogeneous networked entities or agents where the inter-agent ...
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Updating QR factorization procedure for solution of linear least squares problem with equality constraints
... factorization of the small subproblem in order to obtain the solution of our considered problem. Numerical experiments are provided which illustrated the accuracy of the pre- sented algorithm. We also showed that the ...
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A Linear Least Squares Fit Mapping Method for Information Retrieval From Natural Language Texts
... A LINEAR LEAST SQUARES FIT MAPPING METHOD FOR INFORMATION RETRIEVAL FROM NATURAL LANGUAGE TEXTS A LINEAR LEAST SQUARES FIT MAPPING METHOD FOR INFORMATION RETRIEVAL FROM NATURAL LANGUAGE TEXTS YIMING Y[.] ...
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A predictor corrector iterative method for solving linear least squares problems and perturbation error analysis
... predictor-corrector iterative method of convergence order p = 45 requiring 10 matrix by matrix multiplications per iteration is proposed for computing the Moore–Penrose inverse of a nonzero matrix of rank = r. ...
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Characterising the Human Auditory System using a Linear Least Squares System Identification Approach
... limitations—not least of which the lack of neurophysiological interpretability—and it was suggested that forward modelling and response detection might provide a useful ...
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New Evidence on Linear Regression and Treatment Effect Heterogeneity
... in linear least squares regression is studied by Angrist (1998) and Humphreys (2009), and both of these papers consider a saturated model for covariates, ...underlying linear regression are ...
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CROSSES BETWEEN INSERTION AND POINT MUTATIONS IN Λ GENE cI: STIMULATION OF NEIGHBORING RECOMBINATION BY HETEROLOGY
... T h e data are not described well by linear expressions: linear least squares fits extrapolate to nonzero frequencies at the position of the insertion (theoretically [r] ...
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Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method
... a linear least-squares solver in ...dense linear algebra li- braries can be used; however, in high-dimensional systems or for a large lag, the storage requirements can be prohibitive ...the ...
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Consistent least squares fitting of ellipsoids
... The OLS estimation of the ellipsoid parameters from noisy measurements of points on its boundary is a nonlinear least squares problem. An indirect, suboptimal approach was used that transforms the ellipsoid ...
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On the generalization of linear least mean squares estimation to quantum systems with non-commutative outputs
... of linear least mean squares estimators for the non-commutative outputs by temporarily ex- cluding the physical realizability ...mean squares estimator should satisfy a linear dynamics ...
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Least Squares Solutions of Generalized Sylvester Equation with Xi Satisfies Different Linear Constraint
... a least squares solu- tion group X 1 , X 2 , X 3 , X 4 with X i satisfying different linear constraint can be obtained within finite iteration steps in the absence of round off ...
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Functional Analysis of Chemometric Data
... partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this ...
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Continuous Iteratively Reweighted Least Squares Algorithm for Solving Linear Models by Convex Relaxation
... In this paper, we improve that the algorithm calls continuous iteratively re- weighted least squares algorithm for solving REAPER (3), and under a weaker assumption on the data set, we can prove the ...
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Floating point error analysis of recursive least squares and least means squares adaptive filters
... This sequence is a zero mean white independent random process which has a variance related to signal statistics, the weight vector covariance, and the floating point errorso The calculat[r] ...
27
Least squares approximations of power series
... classical least squares solutions in C[ − 1, 1] in terms of linear combinations of ul- traspherical polynomials are extended in order to estimate power series on ( − 1, ...
20
Image magnification by least squares surfaces
... two linear interpolation methods is that, in the magnification ratio, artificial blocks and visual effects are undesirable, but edges are preserved at an acceptable ...
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Deformation analysis with Total Least Squares
... In classical approach, transformation parameters are es- timated by the LS adjustment of the observation equations where only the observations are considered as stochastic. However, in some cases, design matrix elements ...
7
Overview of total least squares methods
... total least squares problems [12], the data matrix A B is ...total least squares problem formulation is ex- tended [31] with the additional constraint that the structure of the data matrix A B ...
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Least squares-based iterative identification methods for linear-in-parameters systems using the decomposition technique
... the least squares based iterative (LSI) method, this paper presents a decom- position based LSI (D-LSI) algorithm for identifying linear-in-parameters systems and an interval- varying D-LSI algorithm ...
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