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Dive into the research topics where Francesco Rinaldi is active.

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Featured researches published by Francesco Rinaldi.


IEEE Transactions on Industrial Electronics | 2012

Finite-Element-Based Multiobjective Design Optimization Procedure of Interior Permanent Magnet Synchronous Motors for Wide Constant-Power Region Operation

Francesco Parasiliti; Marco Villani; Stefano Lucidi; Francesco Rinaldi

This paper proposes the design optimization procedure of three-phase interior permanent magnet (IPM) synchronous motors with minimum weight, maximum power output, and suitability for wide constant-power region operation. The particular rotor geometry of the IPM synchronous motor and the presence of several variables and constraints make the design problem very complicated. The authors propose to combine an accurate finite-element analysis with a multiobjective optimization procedure using a new algorithm belonging to the class of controlled random search algorithms. The optimization procedure has been employed to design two IPM motors for industrial application and a city electrical scooter. A prototype has been realized and tested. The comparison between the predicted and measured performances shows the reliability of the simulation results and the effectiveness, versatility, and robustness of the proposed procedure.


Computational Optimization and Applications | 2010

Concave programming for minimizing the zero-norm over polyhedral sets

Francesco Rinaldi; Fabio Schoen; Marco Sciandrone

Given a non empty polyhedral set, we consider the problem of finding a vector belonging to it and having the minimum number of nonzero components, i.e., a feasible vector with minimum zero-norm. This combinatorial optimization problem is NP-Hard and arises in various fields such as machine learning, pattern recognition, signal processing. One of the contributions of this paper is to propose two new smooth approximations of the zero-norm function, where the approximating functions are separable and concave. In this paper we first formally prove the equivalence between the approximating problems and the original nonsmooth problem. To this aim, we preliminarily state in a general setting theoretical conditions sufficient to guarantee the equivalence between pairs of problems. Moreover we also define an effective and efficient version of the Frank-Wolfe algorithm for the minimization of concave separable functions over polyhedral sets in which variables which are null at an iteration are eliminated for all the following ones, with significant savings in computational time, and we prove the global convergence of the method. Finally, we report the numerical results on test problems showing both the usefulness of the new concave formulations and the efficiency in terms of computational time of the implemented minimization algorithm.


Applied Mathematics and Computation | 2009

A patient adaptable ECG beat classifier based on neural networks

A. De Gaetano; Simona Panunzi; Francesco Rinaldi; A. Risi; Marco Sciandrone

A novel supervised neural network-based algorithm is designed to reliably distinguish in electrocardiographic (ECG) records between normal and ischemic beats of the same patient. The basic idea behind this paper is to consider an ECG digital recording of two consecutive R-wave segments (RRR interval) as a noisy sample of an underlying function to be approximated by a fixed number of Radial Basis Functions (RBF). The linear expansion coefficients of the RRR interval represent the input signal of a feed-forward neural network which classifies a single beat as normal or ischemic. The system has been evaluated using several patient records taken from the European ST-T database. Experimental results show that the proposed beat classifier is very reliable, and that it may be a useful practical tool for the automatic detection of ischemic episodes.


Journal of Global Optimization | 2012

An approach to constrained global optimization based on exact penalty functions

G. Di Pillo; Stefano Lucidi; Francesco Rinaldi

In the field of global optimization many efforts have been devoted to solve unconstrained global optimization problems. The aim of this paper is to show that unconstrained global optimization methods can be used also for solving constrained optimization problems, by resorting to an exact penalty approach. In particular, we make use of a non-differentiable exact penalty function


Computational Optimization and Applications | 2012

Derivative-free methods for bound constrained mixed-integer optimization

Giampaolo Liuzzi; Stefano Lucidi; Francesco Rinaldi


Optimization Letters | 2009

New results on the equivalence between zero-one programming and continuous concave programming

Francesco Rinaldi

{P_q(x;\varepsilon)}


Journal of Optimization Theory and Applications | 2015

A Derivative-Free Algorithm for Constrained Global Optimization Based on Exact Penalty Functions

Gianni Di Pillo; Stefano Lucidi; Francesco Rinaldi


Optimization Methods & Software | 2012

A concave optimization-based approach for sparse portfolio selection

David Di Lorenzo; Giampaolo Liuzzi; Francesco Rinaldi; Fabio Schoen; Marco Sciandrone

. We show that, under weak assumptions, there exists a threshold value


Siam Journal on Optimization | 2016

A Fast Active Set Block Coordinate Descent Algorithm for

Marianna De Santis; Stefano Lucidi; Francesco Rinaldi


Journal of Optimization Theory and Applications | 2015

\ell_1

Giampaolo Liuzzi; Stefano Lucidi; Francesco Rinaldi

{\bar \varepsilon >0 }

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Stefano Lucidi

Sapienza University of Rome

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Giampaolo Liuzzi

Sapienza University of Rome

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Marianna De Santis

Alpen-Adria-Universität Klagenfurt

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Andrea Serani

National Research Council

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Matteo Diez

National Research Council

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Andrea Cristofari

Sapienza University of Rome

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Giovanni Fasano

Ca' Foscari University of Venice

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