Add to ORKGA constrained least squares problem in a Hilbert space H is considered. The standard Tikhonov regularizationmethod is used. In the case where the set of the constraints is the nonempty intersection of a finite collection of closed convex subsets ofH, an iterative algorithm is designed. The resulting sequence is shown to converge strongly to the unique solution of the regularized problem. The net of the solutions to the regularized problems strongly converges to the minimum norm solution of the least squares problem if its solution set is nonempty. 1
Tikhonov regularization is one of the most popular methods for the solution of linear discrete ill-p...
An iterative method to compute the least-squares solutions of the matrix AXB=C over the norm inequal...
This paper studies the regularization of constrained Maximum Likelihood iterative algorithms applied...
A constrained least squares problem in a Hilbert space H is considered. The standard Tikhonov regula...
A constrained least squares problem in a Hilbert space H is considered. The standard Tikhonov regula...
AbstractThree new iterative methods for the solution of the linear least squares problem with bound ...
We propose an iterative method that solves constrained linear least-squares problems by formulating ...
In this paper we propose an iterative algorithm to solve large size linear inverse ill posed problem...
Abstract. We propose an iterative method that solves constrained linear least-squares problems by fo...
. A nonlinear least squares problem is almost rank deficient at a local minimum if there is a large ...
Straightforward solution of discrete ill-posed least-squares problems with error-contaminated data d...
. Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditio...
We study randomized sketching methods for approximately solving least-squares prob-lem with a genera...
We propose an implicit iterative scheme and an explicit iterative scheme for finding a common elemen...
AbstractStraightforward solution of discrete ill-posed least-squares problems with error-contaminate...
Tikhonov regularization is one of the most popular methods for the solution of linear discrete ill-p...
An iterative method to compute the least-squares solutions of the matrix AXB=C over the norm inequal...
This paper studies the regularization of constrained Maximum Likelihood iterative algorithms applied...
A constrained least squares problem in a Hilbert space H is considered. The standard Tikhonov regula...
A constrained least squares problem in a Hilbert space H is considered. The standard Tikhonov regula...
AbstractThree new iterative methods for the solution of the linear least squares problem with bound ...
We propose an iterative method that solves constrained linear least-squares problems by formulating ...
In this paper we propose an iterative algorithm to solve large size linear inverse ill posed problem...
Abstract. We propose an iterative method that solves constrained linear least-squares problems by fo...
. A nonlinear least squares problem is almost rank deficient at a local minimum if there is a large ...
Straightforward solution of discrete ill-posed least-squares problems with error-contaminated data d...
. Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditio...
We study randomized sketching methods for approximately solving least-squares prob-lem with a genera...
We propose an implicit iterative scheme and an explicit iterative scheme for finding a common elemen...
AbstractStraightforward solution of discrete ill-posed least-squares problems with error-contaminate...
Tikhonov regularization is one of the most popular methods for the solution of linear discrete ill-p...
An iterative method to compute the least-squares solutions of the matrix AXB=C over the norm inequal...
This paper studies the regularization of constrained Maximum Likelihood iterative algorithms applied...