6,609 research outputs found

    Two variable orthogonal polynomials and structured matrices

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    30 pages, no figures.-- MSC2000 codes: 42C05, 30E05, 47A57.MR#: MR2218946 (2006m:47021)Zbl#: Zbl 1136.42305We consider bivariate real valued polynomials orthogonal with respect to a positive linear functional. The lexicographical and reverse lexicographical orderings are used to order the monomials. Recurrence formulas are derived between polynomials of different degrees. These formulas link the orthogonal polynomials constructed using the lexicographical ordering with those constructed using the reverse lexicographical ordering. Relations between the coefficients in the recurrence formulas are established and used to give necessary and sufficient conditions for the existence of a positive linear functional. Links to the theory of matrix orthogonal polynomials are developed as well the consequences of a zero assumption on one of the coefficients in the the recurrence formulas.The second and fourth authors were partially supported by NATO grant PST.CLG.979738. The second author was partially supported by an NSF grant. The first and fourth authors were partially supported by grant BFM2003-06335-C03-02 from the Dirección General de Investigación, Ministerio de Educación y Ciencia of Spain.Publicad

    Stochastic collocation on unstructured multivariate meshes

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    Collocation has become a standard tool for approximation of parameterized systems in the uncertainty quantification (UQ) community. Techniques for least-squares regularization, compressive sampling recovery, and interpolatory reconstruction are becoming standard tools used in a variety of applications. Selection of a collocation mesh is frequently a challenge, but methods that construct geometrically "unstructured" collocation meshes have shown great potential due to attractive theoretical properties and direct, simple generation and implementation. We investigate properties of these meshes, presenting stability and accuracy results that can be used as guides for generating stochastic collocation grids in multiple dimensions.Comment: 29 pages, 6 figure

    The limit empirical spectral distribution of Gaussian monic complex matrix polynomials

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    We define the empirical spectral distribution (ESD) of a random matrix polynomial with invertible leading coefficient, and we study it for complex n×nn \times n Gaussian monic matrix polynomials of degree kk. We obtain exact formulae for the almost sure limit of the ESD in two distinct scenarios: (1) n→∞n \rightarrow \infty with kk constant and (2) k→∞k \rightarrow \infty with nn constant. The main tool for our approach is the replacement principle by Tao, Vu and Krishnapur. Along the way, we also develop some auxiliary results of potential independent interest: we slightly extend a result by B\"{u}rgisser and Cucker on the tail bound for the norm of the pseudoinverse of a non-zero mean matrix, and we obtain several estimates on the singular values of certain structured random matrices.Comment: 25 pages, 4 figure

    Computing a partial Schur factorization of nonlinear eigenvalue problems using the infinite Arnoldi method

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    The partial Schur factorization can be used to represent several eigenpairs of a matrix in a numerically robust way. Different adaptions of the Arnoldi method are often used to compute partial Schur factorizations. We propose here a technique to compute a partial Schur factorization of a nonlinear eigenvalue problem (NEP). The technique is inspired by the algorithm in [8], now called the infinite Arnoldi method. The infinite Arnoldi method is a method designed for NEPs, and can be interpreted as Arnoldi's method applied to a linear infinite-dimensional operator, whose reciprocal eigenvalues are the solutions to the NEP. As a first result we show that the invariant pairs of the operator are equivalent to invariant pairs of the NEP. We characterize the structure of the invariant pairs of the operator and show how one can carry out a modification of the infinite Arnoldi method by respecting the structure. This also allows us to naturally add the feature known as locking. We nest this algorithm with an outer iteration, where the infinite Arnoldi method for a particular type of structured functions is appropriately restarted. The restarting exploits the structure and is inspired by the well-known implicitly restarted Arnoldi method for standard eigenvalue problems. The final algorithm is applied to examples from a benchmark collection, showing that both processing time and memory consumption can be considerably reduced with the restarting technique

    Fast algorithm for border bases of Artinian Gorenstein algebras

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    Given a multi-index sequence σ\sigma, we present a new efficient algorithm to compute generators of the linear recurrence relations between the terms of σ\sigma. We transform this problem into an algebraic one, by identifying multi-index sequences, multivariate formal power series and linear functionals on the ring of multivariate polynomials. In this setting, the recurrence relations are the elements of the kerne lII\sigma of the Hankel operator $H$\sigma associated to σ\sigma. We describe the correspondence between multi-index sequences with a Hankel operator of finite rank and Artinian Gorenstein Algebras. We show how the algebraic structure of the Artinian Gorenstein algebra AA\sigmaassociatedtothesequence associated to the sequence \sigma yields the structure of the terms $\sigma\alphaforall for all α\alpha ∈\in N n.Thisstructureisexplicitlygivenbyaborderbasisof. This structure is explicitly given by a border basis of Aσ\sigma,whichispresentedasaquotientofthepolynomialring, which is presented as a quotient of the polynomial ring K[x 1 ,. .. , xn]bythekernel] by the kernel Iσ\sigmaoftheHankeloperator of the Hankel operator Hσ\sigma.Thealgorithmprovidesgeneratorsof. The algorithm provides generators of Iσ\sigmaconstitutingaborderbasis,pairwiseorthogonalbasesof constituting a border basis, pairwise orthogonal bases of Aσ\sigma$ and the tables of multiplication by the variables in these bases. It is an extension of Berlekamp-Massey-Sakata (BMS) algorithm, with improved complexity bounds. We present applications of the method to different problems such as the decomposition of functions into weighted sums of exponential functions, sparse interpolation, fast decoding of algebraic codes, computing the vanishing ideal of points, and tensor decomposition. Some benchmarks illustrate the practical behavior of the algorithm

    Symmetric Tensor Decomposition by an Iterative Eigendecomposition Algorithm

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    We present an iterative algorithm, called the symmetric tensor eigen-rank-one iterative decomposition (STEROID), for decomposing a symmetric tensor into a real linear combination of symmetric rank-1 unit-norm outer factors using only eigendecompositions and least-squares fitting. Originally designed for a symmetric tensor with an order being a power of two, STEROID is shown to be applicable to any order through an innovative tensor embedding technique. Numerical examples demonstrate the high efficiency and accuracy of the proposed scheme even for large scale problems. Furthermore, we show how STEROID readily solves a problem in nonlinear block-structured system identification and nonlinear state-space identification
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