D
Daniela di Serafino
Researcher at Seconda Università degli Studi di Napoli
Publications - 88
Citations - 1086
Daniela di Serafino is an academic researcher from Seconda Università degli Studi di Napoli. The author has contributed to research in topics: Preconditioner & Linear system. The author has an hindex of 17, co-authored 83 publications receiving 912 citations. Previous affiliations of Daniela di Serafino include Indian Council of Agricultural Research & University of Naples Federico II.
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On the steplength selection in gradient methods for unconstrained optimization
TL;DR: This work investigates the relationships between the steplengths of a variety of gradient methods and the spectrum of the Hessian of the objective function, providing insight into the computational effectiveness of the methods, for both quadratic and general unconstrained optimization problems.
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Primary gas thermometry by means of laser-absorption spectroscopy: determination of the Boltzmann constant.
G. Casa,Antonio Castrillo,Gianluca Galzerano,Richard Wehr,Andrea Merlone,Daniela di Serafino,P. Laporta,Livio Gianfrani +7 more
TL;DR: A new optical implementation of primary gas thermometry based on laser-absorption spectrometry in the near infrared, retrieving the Doppler broadening from highly accurate observations of the line shape of the R(12) nu1+2nu2(0)+nu3 transition in CO2 gas at thermodynamic equilibrium is reported.
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On spectral properties of steepest descent methods
TL;DR: This work shows how, for convex quadratic problems, moving from some theoretical properties of the SD method, second-order information provided by the step length can be exploited to dramatically improve the usually poor practical behaviour of this method.
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An efficient gradient method using the Yuan steplength
TL;DR: A new gradient method for quadratic programming, named SDC, which alternates some steepest descent iterates with some gradient iterates that use a constant steplength computed through the Yuan formula, which tends to outperform the Dai–Yuan method, which is one of the fastest methods among the gradient ones.
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On mutual impact of numerical linear algebra and large-scale optimization with focus on interior point methods
TL;DR: The mutual impact of linear algebra and optimization is discussed, focusing on interior point methods and on the iterative solution of the KKT system, with a focus on preconditioning, termination control for the inner iterations, and inertia control.