we present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step q-superlinear convergence. the approach is based on an adaptive structured scheme due to mahdavi-amiri and bartels of the exact penalty method of coleman and conn for nonlinearly constrained optimization problems. the structured adaptation also makes use of the ideas of n...

Abstract. In this paper, we investigate the numerical identifications of physical parameters in parabolic initial-boundary value problems. The identifying problem is first formulated as a constrained minimization one using the output least squares approach with the H 1-regularization or BV -regularization. Then a simple finite element method is used to approximate the constrained minimization p...

In system identification, the true system is often known to be stable. However, due to finite sample constraints, modeling errors, plant disturbances and measurement noise, the identified model may be unstable. We present a constrained optimization method to ensure asymptotic stability of the identified model in the context of subspace identification methods. In subspace identification, we firs...

The primary goal of this article is to extend the reciprocal LASSO for applications binary and survival outcomes. We consider least squares approximation (LSA) as a solver problem. LSA general theoretical framework that includes generalized linear models, Cox regression, many others special cases. By applying regularization, we transfer original problem into an asymptotically equivalent While e...