Results 11 to 20 of about 2,028,925 (265)
Least-squares Solutions of Linear Differential Equations
Mathematics, 2017 This study shows how to obtain least-squares solutions to initial and boundary value problems to nonhomogeneous linear differential equations with nonconstant coefficients of any order.
Mortari, Daniele
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Quantum Regularized Least Squares [PDF]
Quantum, 2023 Linear regression is a widely used technique to fit linear models and finds widespread applications across different areas such as machine learning and statistics.
Shantanav Chakraborty +2 more
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IEEE Access, 2021 Two M-decomposed based identification algorithms are proposed for large-scale systems in this study. Since the least squares algorithms involve matrix inversion calculation, they can be inefficient for large-scale systems whose information matrices are ...
Yuejiang Ji, Lixin Lv
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Projected Least-Squares Quantum Process Tomography [PDF]
Quantum, 2022 We propose and investigate a new method of quantum process tomography (QPT) which we call projected least squares (PLS). In short, PLS consists of first computing the least-squares estimator of the Choi matrix of an unknown channel, and subsequently ...
Trystan Surawy-Stepney +3 more
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Comparison between the Conventional Partial Least Squares (Pls) and the Robust Partial Least Squares (Rpls-Sem) Through Winsorization Approach [PDF]
Journal of Information Technology Management, 2022 This study compared the performance of the partial least squares-structural equation modelling (PLS-SEM) and the robust partial least squares -structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used
GholamReza Zandi +3 more
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GM(1,1;λ) with Constrained Linear Least Squares
Axioms, 2021 The only parameters of the original GM(1,1) that are generally estimated by the ordinary least squares method are the development coefficient a and the grey input b.
Ming-Feng Yeh, Ming-Hung Chang
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Unbiased Least-Squares Modelling
Mathematics, 2020 In this paper we analyze the bias in a general linear least-squares parameter estimation problem, when it is caused by deterministic variables that have not been included in the model.
Marta Gatto, Fabio Marcuzzi
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Image magnification by least squares surfaces [PDF]
Iranian Journal of Numerical Analysis and Optimization, 2017 Image magnification is one of the current issues of image processing in which keeping the quality and structure of images is the main concern. In im- age magnification, it is necessary to insert information in extra pixels.
Ali Mohammad Esmaili Zaini +2 more
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AIMS Mathematics, 2021 In this paper, the upper bounds for two kinds of normiwse condition numbers are derived for $ \min\limits_{x}\|(A\otimes B)x-b\|_2 $ when the coefficient matrix is of rank deficient.
Lingsheng Meng, Limin Li
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Nonlinear least squares method
Communications, 1999 The paper deals with a comparison of linear and nonlinear least squares approximation. Its aim is to show that the well known transformations of nonlinear dependencies on linear dependencies do not always give exact results.
Jaromír Máca, Bohus Leitner
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