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Abstract. We analyze the approximation properties of some interpolation operators and some ¿¿-orthogonal projection operators related to systems of polynomials which are orthonormal with respect to a weight function u(xx,. . . , xd), d > 1. The error estimates for the Legendre system and the Chebyshev system of the first kind are given in the norms of the Sobolev spaces H^. These results are useful in the numerical analysis of the approximation of partial differential equations by spectral methods. 0. Introduction. Spectral methods are a classical and largely used technique to solve differential equations, both theoretically and numerically. During the years they have gained new popularity in automatic computations for a wide class of physical problems (for instance in the fields of fluid and gas dynamics), due to the use of the Fast Fourier Transform algorithm.These methods appear to be competitive with finite difference and finite element methods and they must be decisively preferred to the last ones whenever the solution is highly regular and the geometric dimension of the domain becomes large. Moreover, by these methods it is possible to control easily the solution (filtering) of those numerical problems affected by oscillation and instability phenomena.The use of spectral and pseudo-spectral methods in computations in many fields of engineering has been matched by deeper theoretical studies; let us recall here the pioneering works by Orszag [25], [26], Kreiss and Öliger [14] and the monograph by Gottlieb and Orszag [13]. The theoretical results of such works are mainly concerned with the study of the stability of approximation of parabolic and hyperbolic equations; the solution is assumed to be infinitely differentiable, so that by an analysis of the Fourier coefficients an infinite order of convergence can be 67License or copyright restrictions may apply to redistribution; see http://www.ams.org/journal-terms-of-use G8 C. CANUTO AND A. QUARTERONI large norms which affect negatively the rate of convergence (for instance in problems with boundary layers).Both spectral and pseudo-spectral methods are essentially Ritz-Galerkin methods (combined with some integration formulae in the pseudo-spectral case). It is well known that when Galerkin methods are used the distance between the exact and the discrete solution (approximation error) is bounded by the distance between the exact solution and its orthogonal projection upon the subspace (projection error), or by the distance between the exact solution and its interpolated polynomial at some suitable points (interpolation error). This upper bound is often realistic, in the sense that the asymptotic behavior of the approximation error is not better than the one of the projection (or even the interpolation) error. Even more, in some cases the approximate solution coincides with the projection of the true solution upon the subspace (for instance when linear problems with constant coefficients are approximated by spectral methods). This motivates the interest in eva...

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Abstract:In this paper we analyze rendez-vous algorithms in the situation when agents can only exchange information below a given distance threshold R. We study the system under an Eulerian point of view considering (possibly continuous) probability distributions of agents and we present convergence results both in discrete and in continuous time. The limit distribution is always necessarily a convex combination of delta's at least R far apart from each other: in other terms these algorithms are locally aggregating. Numerical simulations seem to show that starting from continuous distributions, in general these algorithms do not converge to a unique delta (rendez-vous) in agreement with previous literature on this subject.

We present an efficient method for the numerical realization of elliptic PDEs in domains depending on random variables. Domains are bounded, and have finite fluctuations. The key feature is the combination of a fictitious domain approach and a polynomial chaos expansion. The PDE is solved in a larger, fixed domain (the fictitious domain), with the original boundary condition enforced via a Lagrange multiplier acting on a random manifold inside the new domain. A (generalized) Wiener expansion is invoked to convert such a stochastic problem into a deterministic one, depending on an extra set of real variables (the stochastic variables). Discretization is accomplished by standard mixed finite elements in the physical variables and a Galerkin projection method with numerical integration (which coincides with a collocation scheme) in the stochastic variables. A stability and convergence analysis of the method, as well as numerical results, are provided. The convergence is "spectral" in the polynomial chaos order, in any subdomain which does not contain the random boundaries. Mathematics Subject Classification (1991)

In this paper we analyze a class of multi-agent consensus dynamical systems inspired by Krause's original model. As in Krause's, the basic assumption is the so-called bounded confidence: two agents can influence each other only when their state values are below a given distance threshold R. We study the system under an Eulerian point of view considering (possibly continuous) probability distributions of agents and we present original convergence results. The limit distribution is always necessarily a convex combination of delta functions at least R far apart from each other: in other terms these models are locally aggregating. The Eulerian perspective provides the natural framework for designing a numerical algorithm, by which we obtain several simulations in 1 and 2 dimensions.