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23 results on '"Gotusso, L"'

10. On the choice of parameter in a method for the inversion of Fourier series

Author

DI NATALE, M, Gotusso, L, Pavani, R, Roux, D, DI NATALE, MARINA, Roux, D., DI NATALE, M, Gotusso, L, Pavani, R, Roux, D, DI NATALE, MARINA, and Roux, D.

Abstract

The a priori evaluation of the pointwise approximation, by a regularization method suggested earlier, of a L1periodic function f, when only noisy Fourier coefficients of f are known, is given here in a formulation more suitable for numerical verification. A careful evaluation of the constants involved in the formulas is also provided. Finally, a procedure is described of a statistical check performed in order to verify whether the theoretically suggested value of the regularization parameter is a good choice with respect to functions generally arising in applications

Published
1992

19. A unifying approach to the regularization of Fourier polynomials.

Author

Falco, D., Frontini, M., and Gotusso, L.

Abstract

In a previous paper [4] the following problem was considered: find, in the class of Fourier polynomials of degree n, the one which minimizes the functional:, where ∥·∥ is the L norm, F is the rth derivative of the Fourier polynomial F( x), and f(x) is a given function with Fourier coefficients c. It was proved that the optimal polynomial has coefficients c given by. In this paper we consider the more general functional, which reduces to (0.1) for σ =σ/r!. We will prove that the classical sigma-factor method for the regularization of Fourier polynomials may be obtained by minimizing the functional (0.3) for a particular choice of the weights σ. This result will be used to propose a motivated numerical choice of the parameter σ in (0.1). [ABSTRACT FROM AUTHOR]

Published
1993
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20. A Fortran IV quasi-decision algorithm for the P-equivalence of two matrices.

Author

Gotusso, L. and Santolini, A.

Abstract

An iterative procedure is given which enables effecting a rather small number of permutations to decide, in a great number of cases, if two matrices can, or cannot, be reduced one to the other by means of permutations on the rows and columns. Some examples of application of this algorithm are given. [ABSTRACT FROM AUTHOR]

Published
1968
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21. Generazione di sequenze di numeri pseudocasuali periodiche e aperiodiche e confronti statistici nell'estensione di 106 elementi

Author

Gotusso, L. and Bassi, V.

Abstract

Summary: A great number of pseudo-random sequences is studied and results on the statistical behaviour of a selected part of them are given. Some of the considered sequences, studied here for the first time, are aperiodic, uniformly distributed and with good pseudorandomness qualities.

Published
1968
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23. On the Choice of Parameter in a Method for the Inversion of Fourier Series

Author

Raffaella Pavani, M. Di Natale, D. Roux, L. Gotusso, DI NATALE, M, Gotusso, L, Pavani, R, and Roux, D

Subjects
Pointwise, Algebra and Number Theory, Integrable system, Applied Mathematics, Mathematical analysis, Inversion (meteorology), Torus, Fourier series, Periodic function, Computational Mathematics, Regularization (physics), A priori and a posteriori, MAT/05 - ANALISI MATEMATICA, Mathematics
Abstract

The a priori evaluation of the pointwise approximation, by a regu- larization method suggested earlier, of a L1 periodic function f, when only noisy Fourier coefficients of f are known, is given here in a formulation more suitable for numerical verification. A careful evaluation of the constants in- volved in the formulas is also provided. Finally, a procedure is described of a statistical check performed in order to verify whether the theoretically suggestedvalue of the regularization parameter is a good choice with respect to functionsgenerally arising in applications. 1. IntroductionWe consider the (generally) ill-posed problem of "reconstructing" a function/, integrable on the A-dimensional torus, when we only know the sequence ofits Fourier coefficients. A recent paper [2] (see also [1]) describes a regularizationmethod to solve this problem which is stable also in the case of noisy data. Extensive numerical experience shows that the method is very efficient [4] (seealso [5]). The efficiency is closely related to the choice of the regularizationparameter cr. In [2], for large classes of functions, evaluations are given of the

Published
1992
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