In this work, we deal with Truncated Newton methods for solving large scale (possibly nonconvex) unconstrained optimization problems. In particular,we consider the use of amodified Bunch and Kaufman factorization for solving the Newton equation, at each (outer) iteration of the method. The Bunch...
01a Articolo in rivista
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This paper includes a twofold result for the Nonlinear Conjugate Gradient (NCG) method, in large scale unconstrained optimization. First we consider a theoretical analysis, where preconditioning is embedded in a strong convergence framework of an NCG method from the literature. Mild conditions to...
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Starting from the paper by Nash and Sofer (1990), we propose a heuristic adaptive truncation criterionfor the inner iterations within linesearch-based truncated Newton methods. Our aim is to possibly avoid ‘‘over-solving’’ of the Newton equation, based on a comparison between the predicted...
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In this paper, we report data and experiments related to the research article entitled “An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization” by Caliciotti et. Al. [1]. In particular, in [1], large scale unconstrained optimization...
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In this paper we study new preconditioners to be used within the nonlinear conjugate gradient (NCG) method, for large scale unconstrained optimization. The rationale behind our proposal draws inspiration from quasi-Newton updates, and its aim is to possibly approximate in some sense the inverse of...
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In this paper we propose the use of damped techniques within Nonlinear Conjugate Gradient (NCG) methods. Damped techniques were introduced by Powell and recently reproposed by Al-Baali and till now, only applied in the framework of quasi--Newton methods. We extend their use to NCG methods in large...
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This paper aims to analyze overall economic and environmental performances of alternative bus powertrains by focusing on U.S. active fleets in different urban contexts. We define a life cycle cost model related to bus technologies by referring to real-world data of 256 transport operators, which...
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In this paper, we analyze the compelling issue of monetary valuation of a scientific publication. While many academic scholars tend to overlook the topic, as being either too difficult or even meaningless, policymakers begin to use very rough tools for evaluating publications, which have many...
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Training agents over sequences of tasks is often employed in deep reinforcement learning to let the agents progress more quickly towards better behaviours. This problem, known as curriculum learning, has been mainly tackled in the literature by numerical methods based on enumeration strategies,...
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We address the minimization of an objective function over the solution set of a (non-parametric) lower-level variational inequality. This problem is a special instance of semi-infinite programs and encompasses, as particular cases, simple (smooth) bilevel and equilibrium selection problems. We...