We study the semiconvergence of Gauss-Seidel iterative methods for the least squares solution of minimal norm of rank deficient linear systems of equations. Necessary and sufficient conditions for the semiconvergence of the Gauss-Seidel iterative method are given. We also show that if the linear system of equations is consistent, then the proposed methods with a zero vector as an initial guess converge in one iteration. Some numerical results are given to illustrate the theoretical results
Abstract. We present a new unified proof for the convergence of both the Jacobi and the Gauss–Seidel...
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AbstractIn this paper, we obtain a practical sufficient condition for convergence of the Gauss-Seide...
An optimization problem that does not have an unique local minimum is often very difficult to solve....
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The Total Least Squares solution of an overdetermined, approximate linear equation Ax approx b minim...
It is well known that as a famous type of iterative methods in numerical linear algebra, Gauss-Seide...
Nonlinear least-squares problems appear in many real-world applications. When a nonlinear model is u...
In this paper, we study the scaled total least squares problems of rank-deficient linear systems. We...
Analysis of real life problems often results in linear systems of equations for which solutions are ...
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The problem of finding sparse solutions to underdetermined systems of linear equations is very commo...
Abstract. We present a new unified proof for the convergence of both the Jacobi and the Gauss–Seidel...
We present a novel iterative algorithm for approximating the linear least squares solution with low ...
AbstractIn this paper, we obtain a practical sufficient condition for convergence of the Gauss-Seide...
An optimization problem that does not have an unique local minimum is often very difficult to solve....
In two papers, we develop theory and methods for regularization of nonlinear least squares problems ...
. A nonlinear least squares problem is almost rank deficient at a local minimum if there is a large ...
When a physical system is modeled by a nonlinear function, the unknown parameters can be estimated b...
In this paper, we suggest a generalized Gauss-Seidel approach to sparse linear and nonlinear least-s...
The Total Least Squares solution of an overdetermined, approximate linear equation Ax approx b minim...
It is well known that as a famous type of iterative methods in numerical linear algebra, Gauss-Seide...
Nonlinear least-squares problems appear in many real-world applications. When a nonlinear model is u...
In this paper, we study the scaled total least squares problems of rank-deficient linear systems. We...
Analysis of real life problems often results in linear systems of equations for which solutions are ...
AbstractWe develop successive overrelaxation (SOR) methods for finding the least squares solution of...
The problem of finding sparse solutions to underdetermined systems of linear equations is very commo...
Abstract. We present a new unified proof for the convergence of both the Jacobi and the Gauss–Seidel...
We present a novel iterative algorithm for approximating the linear least squares solution with low ...
AbstractIn this paper, we obtain a practical sufficient condition for convergence of the Gauss-Seide...