1,721,028 research outputs found
Dynamic scaling based preconditioning for truncated Newton methods in large scale unconstrained optimization
No full textThis paper deals with the preconditioning of truncated Newton methods for the solution of large scale nonlinear unconstrained optimization problems. We focus on preconditioners which can be naturally embedded in the framework of truncated Newton methods, i.e . which can be built without storing the Hessian matrix of the function to be minimized, but only based upon information on the Hessian obtained by the product of the Hessian matrix times a vector. In particular we propose a diagonal preconditioning which enjoys this feature and which enables us to examine the effect of diagonal scaling on truncated Newton methods. In fact, this new preconditioner carries out a scaling strategy and it is based on the concept of equilibration of the data in linear systems of equations. An extensive numerical testing has been performed showing that the diagonal preconditioning strategy proposed is very effective. In fact, on most problems considered, the resulting diagonal preconditioned truncated Newton method performs better than both the unpreconditioned method and the one using an automatic preconditioner based on limited memory quasi-Newton updating (PREQN) recently proposed by Morales and Nocedal [Morales, J.L. and Nocedal, J., 2000, Automatic preconditioning by limited memory quasi-Newton updating. SIAM Journal on Optimization , 10, 1079-1096]
DYNAMIC SCALING BASED PRECONDITIONING FOR TRUNCATED NEWTON METHODS IN LARGE SCALE UNCONSTRAINED OPTIMIZATION: THE COMPLETE RESULTS.
No full textThe ambulance diversion phenomenon in an emergency department network: a case study
No full textMost of the studies dealing with the increasing and well–known problem
of Emergency Department (ED) overcrowding usually focus on modeling the patient
flow within a single ED, without considering the possibilities offered by the
cooperation among EDs. In this work, we analyze the overcrowding phenomenon
considering an EDnetwork, focusing on the so called Ambulance Diversion problem.
A Discrete Event Simulation (DES) model is used to represent the ED network and
the Simulation—Based Optimization (SBO) approach is adopted to study a first–aid
network under different conditions. The aim is to optimize the performances of the
entire network, in order to provide the best service to patients without carrying unsustainable
resource costs. A real case study consisting of six big EDs in the Lazio
region of Italy has been considered. The achieved results show which are the best
diversion policies both in terms of patient waiting time and costs for the service
providers. Our implementation of the SBO procedure is based on a novel approach
adopted for the communication between the DES model and a Derivative–Free
optimization algorithm
LARGE SCALE UNCONSTRAINED OPTIMIZATION
No full textThis work is a survey on the methods for large scale unconstrained optimization. Besides its own theoretical importance, the growing interest in the last years in solving problems with a larger and larger number of variables are arising very frequently from real world as a result of modeling systems with a very complex structure. In this paper the main methods for solving large scale unconstrained optimization problems are briefly described and an accurate choice of references is reported
Nuovi metodi di tipo Newton per problemi di ottimizzazione non vincolata
No full textDottorato di ricerca in ricerca operativa. 7. ciclo. Coordinatore Bruno Simeone. Tutore Stefano LucidiConsiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome; Biblioteca Nazionale Centrale - P.za Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal
Numerical Experiences with New Truncated Newton Methods in Large Scale Unconstrained Optimization
No full textIn this work we present the results of an extensive numerical
experience obtained by different algorithms which belong to the preceding class. This numerical study, besides
investigating which are the best algorithmic choices of the proposed approach, clarifies some significant points
which underlies every truncated Newton based algorith
Stochastic regularization of linear equations and the realization of Gaussian fields
No full textAbstractThe variational representation of the conditional expectation X̂ of a Gaussian signal X given observations Y corrupted by independent white noise is investigated in the general infinite-dimensional setting. Under Hilbert-Schmidt type assumptions it is shown that the filter X̂ can be realized on sample configurations Yω as the extension by continuity of the mapping that gives the solution of a related variational problem
AN APPROXIMATE INVERSE PRECONDITIONER IN TRUNCATED NEWTON METHODS FOR LARGE SCALE OPTIMIZATION
No full textGuest Editorial - Erice 2007 Nonlinear Optimization - Special Issue of COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
No full textAn edited selection of papers presented at the Workshop on "New Problems and Innovative Applications of Nonlinear Optimization", July 31-August 09 2007, Erice, Ital
Nonlinear optimization: a bridge from theory to applications
No full textThis issue of Computational Optimization and Applications collects a selection of referred papers that have been presented at theWorkshop on Nonlinear Optimization: a bridge from theory to applications held in Erice, Italy, on June 10–17, 2013 at the“E. Majorana” Centre for Scientific Culture within the “G. Stampacchia” International School of Mathematics
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