نتایج جستجو برای: least squares boundary residual method

تعداد نتایج: 2145619  

Journal: :Journal of the Computational Structural Engineering Institute of Korea 2012

Journal: :iranian journal of numerical analysis and optimization 0
maryam mojarrab faezeh toutounian

lsmr (least squares minimal residual) is an iterative method for the solution of the linear system of equations and leastsquares problems. this paper presents a block version of the lsmr algorithm for solving linear systems with multiple right-hand sides. the new algorithm is based on the block bidiagonalization and derived by minimizing the frobenius norm of the resid ual matrix of normal equa...

2008
G. R. Liu Y. T. Gu

(2000) Meshless local Petrov–Galerkin (MLPG) method in combination with finite element and boundary element approaches. Abstract The Meshless Local Petrov-Galerkin (MLPG) method is an effective truly meshless method for solving partial differential equations using Moving Least Squares (MLS) interpolants. It is, however, computationally expensive for some problems. A coupled MLPG/Finite Element ...

2004
Michael Merritt Yin Zhang

We study an interior-point gradient method for solving a class of so-called totally nonnegative least squares problems. At each iteration, the method decreases the residual norm along a diagonally scaled negative gradient direction with a special scaling. We establish the global convergence of the method, and present some numerical examples to compare the proposed method with a few similar meth...

Journal: :Numerische Mathematik 2002
Christopher C. Paige Zdenek Strakos

The standard approaches to solving overdetermined linear systems Bx ≈ c construct minimal corrections to the data to make the corrected system compatible. In ordinary least squares (LS) the correction is restricted to the right hand side c, while in scaled total least squares (STLS) [14, 12] corrections to both c and B are allowed, and their relative sizes are determined by a real positive para...

Journal: :Computers & Mathematics with Applications 2014

Journal: :Computational Optimization and Applications 2016

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