The approximate minimization of a quadratic function within an ellipsoidal trust region is an
important subproblem for many nonlinear programming methods. When the number of …

In this paper we propose efficient new linesearch algorithms for solving large scale
unconstrained optimization problems which exploit any local nonconvexity of the objective …

A method for the solution of minimization problems with simple bounds is presented. Global
convergence of a general scheme requiring the approximate solution of a single linear …

We propose a new truncated Newton method for large scale unconstrained optimization,
where a Conjugate Gradient (CG)-based technique is adopted to solve Newton's equation …

In this paper, we present a new conjugate gradient (CG) based algorithm in the class of
planar conjugate gradient methods. These methods aim at solving systems of linear …

In this paper, we describe an application of the planar conjugate gradient method introduced
in Part 1 (Ref. 1) and aimed at solving indefinite nonsingular sets of linear equations. We …

In this paper we deal with the iterative computation of negative curvature directions of an
objective function, within large scale optimization frameworks. In particular, suitable …

We consider the constrained nonlinear optimization problem where the feasible set is
possibly empty, in which case no solution exists. However, in many real-life situations some …

A black-box method using the finite elements, the Crank–Nicolson and a nonmonotone
truncated Newton (TN) method is presented for solving optimal control problems (OCPs) …

Functional magnetic resonance imaging (fMRI) using the Blood-Oxygenation Level
Dependent (BOLD) effect relies on microscopic susceptibility differences between …