Abstract The Levenberg–Marquardt method is a fundamental regularization technique for
the Newton method applied to nonlinear equations, possibly constrained, and possibly with …

The need of accurately reproducing the behaviour of elastoplastic materials in
computational environments for the solution of engineering problems motivates the …

Abstract The Levenberg–Marquardt method is a regularized Gauss–Newton method for
solving systems of nonlinear equations. If an error bound condition holds it is known that …

We address the solution of constrained nonlinear systems by new linesearch quasi-Newton
methods. These methods are based on a proper use of the projection map onto the convex …

The Matlab implementation of a trust-region Gauss-Newton method for bound-constrained
nonlinear least-squares problems is presented. The solver, called TRESNEI, is adequate for …

This paper presents a novel optimization technique for an efficient multi-fidelity model
building approach to reduce computational costs for handling aerodynamic shape …

We address the solution of convex-constrained nonlinear systems of equations where the
Jacobian matrix is unavailable or its computation/storage is burdensome. In order to …

We focus on the numerical solution of medium scale bound-constrained systems of
nonlinear equations. In this context, we consider an affine-scaling trust region approach that …

In this paper, we propose an inexact Newton-like conditional gradient method for solving
constrained systems of nonlinear equations. The local convergence of the new method as …

In this paper, we propose two complementary variants of the projected Levenberg–
Marquardt (LM) algorithm for solving convex constrained nonlinear equations. Since the …