Orthogonal polyanalytic polynomials and normal matrices

DOI10.1090/S0025-5718-02-01417-5zbMath1010.42013OpenAlexW2041557238MaRDI QIDQ4780376

Marko Huhtanen

Publication date: 19 November 2002

Published in: Mathematics of Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1090/s0025-5718-02-01417-5

zbMATH Keywords

normal matrixleast squares approximationArnoldi iterationbivariate Lagrange interpolationslowly growing length of the recurrenceorthogonal polyanalytic polynomial

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Orthogonal functions and polynomials, general theory of nontrigonometric harmonic analysis (42C05) Hermitian, skew-Hermitian, and related matrices (15B57)

Related Items (7)

The minimal and characteristic polyanalytic polynomials of a normal matrix ⋮ SYMMLQ-like procedure for \(Ax=b\) where \(A\) is a special normal matrix ⋮ Block tridiagonal reduction of perturbed normal and rank structured matrices ⋮ On an Economic Arnoldi Method for $BML$-Matrices ⋮ Solving systems of linear equations with normal coefficient matrices and the degree of the minimal polyanalytic polynomial ⋮ Aspects of nonnormality for iterative methods ⋮ The real linear eigenvalue problem in \(\mathbb C^n\)

Cites Work

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• A Stratification of the Set of Normal Matrices
• Exclusion and Inclusion Regions for the Eigenvalues of a Normal Matrix
• A Hermitian Lanczos Method for Normal Matrices
• Updating and Downdating of Orthogonal Polynomials with Data Fitting Applications
• On Generating Orthogonal Polynomials
• Fast QR Decomposition of Vandermonde-Like Mmatrices and Polynomial Least Squares Approximation
• Computational Aspects of Polynomial Interpolation in Several Variables
• Orthogonal Polynomials in Two Variables. A Further Analysis of the Polynomials Orthogonal over a Region Bounded by Two Lines and a Parabola
• For numerical differentiation, dimensionality can be a blessing!
• The Arc Length of the Lemniscate { | p(z) | = 1}
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• The principle of minimized iterations in the solution of the matrix eigenvalue problem
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