Reduced rank approximation of matrices has hitherto been possible only by unweighted least squares. This paper presents iterative techniques for obtaining such approximations when weights are ...
Journal of Computational Mathematics, Vol. 33, No. 2 (March 2015), pp. 113-127 (15 pages) In this paper, a constrained distributed optimal control problem governed by a firstorder elliptic system is ...
Abstract: Meshless method is a new numerical method on problem for determining solution of differential equation. The moving least squares approximation makes only require least squares approximation ...
A comprehensive Python implementation of the Least Squares Method for data approximation using various basis functions. This project focuses on solving overdetermined systems using different ...
Abstract: Based on the forward selection formula, the relationship between the least squares cost function and the correlation between the training data and the regressors is introduced. A rule to ...
In this paper we present an algorithm to enhance the accuracy of the estimation of the parameters of linear stroke segments in a two-dimensional printed character image. The algorithm achieves high ...
Penalized least squares estimates provide a way to balance fitting the data closely and avoiding excessive roughness or rapid variation. A penalized least squares estimate is a surface that minimizes ...
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