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PCA_SA

swMATH ID: 38319
Software Authors: Fenzi, Luca; Michiels, Wim
Description: MATLAB tutorial PCA_SA: Polynomial (chaos) approximation of maximum eigenvalue functions. This tutorial reviews the numerical experiments contained in the article, Fenzi & Michiels (2019) ”Polynomial (chaos) approximation of maximum eigenvalue functions: efficiency and limitations”, providing a template that can be modified for explorations of your own. The tutorial explores the polynomial approximation of smooth, non-differentiable and non-Lipschitz continuous functions in the univariate and bivariate cases. The analyzed functions arise from parameter eigenvalue problems; in particular, they are the real part of the rightmost eigenvalue (the so-called spectral abscissa). The polynomial approximations are obtained by Galerkin and collocation approaches. In the Galerkin approach, the numerical approximation of the coefficients in the univariate case is achieved by extended (or composite) Trapezoidal and Simpson’s rules or by Gauss and Clenshaw-Curtis quadrature rules. For the bivariate case, the coefficients are approximated by tensorial and non-tensorial Clenshaw-Curtis cubature rules, based on tensor-product Chebyshev grid and Padua points, respectively. The collocation approach interpolates the function on Chebyshev points in the univariate case, while for the bivariate case the interpolant nodes are given by tensor-product Chebyshev grid and Padua points.
Homepage: https://lucafe.github.io/Software.htm
Dependencies: Matlab
Related Software: UDDAE_Optimization; UQLab; Chebfun
Referenced in: 1 Publication

Referenced by 2 Authors

1 Fenzi, Luca
1 Michiels, Wim

Referenced in 1 Serial

1 Numerical Algorithms

Referenced in 1 Field

1 Numerical analysis (65-XX)

Referencing Publications by Year