Solving Least-Squares Problems via a Double-Optimal Algorithm and a Variant of the Karush–Kuhn–Tucker Equation for Over-Determined Systems

A double optimal solution (DOS) of a least-squares problem Ax=b,A∈Rq×n with q≠n is derived in an m+1-dimensional varying affine Krylov subspace (VAKS); two minimization techniques exactly determine the m+1 expansion coefficients of the solution x in the VAKS. The minimal-norm solution can be obtaine...

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Bibliographic Details
Published in:	Algorithms Vol. 17; no. 5; p. 211
Main Authors:	Liu, Chein-Shan, Kuo, Chung-Lun, Chang, Chih-Wen
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.05.2024
Subjects:	absolute convergence Algorithms double optimal algorithm Gaussian elimination Iterative algorithms Iterative methods Least squares method linear least-squares problems Linear systems Matrices (mathematics) Methods minimal-norm solution Numerical analysis Optimization Optimization techniques residual orthogonality Subspaces Thermal expansion varying affine Krylov subspace
ISSN:	1999-4893, 1999-4893
Online Access:	Get full text
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