The aim of this paper is to extend the applicability of the incomplete oblique projections method (IOP) previously introduced by the authors for solving inconsistent linear systems to the box constrained case. The new algorithm employs incomplete projections onto the set of solutions of the augmented system Ax − r = b, together with the box constraints, based on a scheme similar to the one of IOP, adding the conditions for accepting an approximate solution in the box. The theoretical properties of the new algorithm are analyzed, and numerical experiences are presented comparing its performance with some well-known methods.Facultad de IngenieríaFacultad de Ciencias Exacta
One of the possible approaches for the solution of underdetermined linear least-squares problems in ...
We provide a full characterization of the oblique projector U(VU) †V in the general case where the r...
Numerous scientific applications across a variety of fields depend on box-constrained convex optimiz...
The aim of this paper is to extend the applicability of the incomplete oblique projections method (I...
The aim of this paper is to extend the applicability of an algorithm for solving inconsistent linear...
Large and sparse systems of linear equations arise in many important applications [1] as radi-ation ...
AbstractAn algorithm previously introduced by the author for finding a feasible point of a system of...
AbstractLinear least squares problems with box constraints are commonly solved to find model paramet...
The Projected Aggregation Methods (PAM) for solving linear systems of equali- ties and/or inequaliti...
AbstractWe consider in the paper the problem of finding an approximat solution of a large scale inco...
AbstractIn this paper we construct and theoretically analyze a class of direct projection algorithms...
Linear least squares problems with box constraints are commonly solved to find model parameters with...
The main contribution of this paper is presenting a flexible solution to the box-constrained least s...
The Projected Aggregation Methods (PAM) for solving linear systems of equalities and/or inequalities...
We consider linear feasibility problems in the "standard" form Ax = b, 1 ≤ x ≤ u. The successive ort...
One of the possible approaches for the solution of underdetermined linear least-squares problems in ...
We provide a full characterization of the oblique projector U(VU) †V in the general case where the r...
Numerous scientific applications across a variety of fields depend on box-constrained convex optimiz...
The aim of this paper is to extend the applicability of the incomplete oblique projections method (I...
The aim of this paper is to extend the applicability of an algorithm for solving inconsistent linear...
Large and sparse systems of linear equations arise in many important applications [1] as radi-ation ...
AbstractAn algorithm previously introduced by the author for finding a feasible point of a system of...
AbstractLinear least squares problems with box constraints are commonly solved to find model paramet...
The Projected Aggregation Methods (PAM) for solving linear systems of equali- ties and/or inequaliti...
AbstractWe consider in the paper the problem of finding an approximat solution of a large scale inco...
AbstractIn this paper we construct and theoretically analyze a class of direct projection algorithms...
Linear least squares problems with box constraints are commonly solved to find model parameters with...
The main contribution of this paper is presenting a flexible solution to the box-constrained least s...
The Projected Aggregation Methods (PAM) for solving linear systems of equalities and/or inequalities...
We consider linear feasibility problems in the "standard" form Ax = b, 1 ≤ x ≤ u. The successive ort...
One of the possible approaches for the solution of underdetermined linear least-squares problems in ...
We provide a full characterization of the oblique projector U(VU) †V in the general case where the r...
Numerous scientific applications across a variety of fields depend on box-constrained convex optimiz...