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...
01a Articolo in rivista
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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...
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The use and creation of machine‐learning‐based solutions to solve problems or reduce their computational costs are becoming increasingly widespread in many domains. Deep Learning plays a large part in this growth. However, it has drawbacks such as a lack of explainability and behaving as a black‐...
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Machine learning techniques are a driving force for research in various fields, from credit card fraud detection to stock analysis. Recently, a growing interest in increasing human involvement has emerged, with the primary goal of improving the interpretability of machine learning models. Among...