Inexact Variable Metric Method for Convex-Constrained Optimization Problems

This paper is concerned with the inexact variable metric method for solving convex-constrained optimization problems. At each iteration of this method, the search direction is obtained by inexactly minimizing a strictly convex quadratic function over the closed convex feasible set. Here, we propose a new inexactness criterion for the search direction subproblems. Under mild assumptions, … Read more

A non-monotone Inexact Restoration approach for minimization with orthogonality constraints

In this work we consider the problem of minimizing a differentiable functional restricted to the set of $n\times p$ matrices with orthonormal columns. This problem appears in several fields such as statistics, signal processing, global positioning system, machine learning, physics, chemistry and others. We present an algorithm based on a recent non-monotone variation of the … Read more

Non-monotone Inexact Restoration Method for nonlinear programming

This paper deals with a new variant of the Inexact Restoration Method of Fischer and Friedlander (COAP, 46, pp. 333-346, 2010). We propose an algorithm that replaces the monotone line-search performed in the tangent phase (with regard to the penalty function) by a non-monotone one. Con- vergence to feasible points satisfying the approximate gradient projection … Read more

Local convergence analysis of the Levenberg-Marquardt framework for nonzero-residue nonlinear least-squares problems under an error bound condition

The Levenberg-Marquardt method (LM) is widely used for solving nonlinear systems of equations, as well as nonlinear least-squares prob- lems. In this paper, we consider local convergence issues of the LM method when applied to nonzero-residue nonlinear least-squares problems under an error bound condition, which is weaker than requiring full-rank of the Jacobian in a … Read more