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Pregled bibliografske jedinice broj: 93892

Highly accurate symmetric eigenvalue decomposition and hyperbolic SVD


Slapničar, Ivan
Highly accurate symmetric eigenvalue decomposition and hyperbolic SVD // Linear algebra and its applications, 358 (2003), 387-424 (međunarodna recenzija, članak, znanstveni)


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Naslov
Highly accurate symmetric eigenvalue decomposition and hyperbolic SVD

Autori
Slapničar, Ivan

Izvornik
Linear algebra and its applications (0024-3795) 358 (2003); 387-424

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Hyperbolic singular value decomposition; Symmetric eigenvalue problem; Symmetric indefinite decomposition; Jacobi method; Relative perturbation theory; High relative accuracy

Sažetak
Let G be a m×n real matrix with full column rank and let J be a n×n diagonal matrix of signs, Jii{1, 1}. The hyperbolic singular value decomposition (HSVD) of the pair (G, J) is defined as G=UV1, where U is orthogonal, is positive definite diagonal, and V is J-orthogonal matrix, VTJV=J. We analyze when it is possible to compute the HSVD with high relative accuracy. This essentially means that each computed hyperbolic singular value is guaranteed to have some correct digits, even if they have widely varying magnitudes. We show that one-sided J-orthogonal Jacobi method method computes the HSVD with high relative accuracy. More precisely, let B=GD1, where D is diagonal such that the columns of B have unit norms. Essentially, we show that the computed hyperbolic singular values of the pair (G, J) will have log10(/min(B)) correct decimal digits, where is machine precision. We give the necessary relative perturbation bounds and error analysis of the algorithm. Our numerical tests confirmed all theoretical results. For the symmetric non-singular eigenvalue problem Hx=x, we analyze the two-step algorithm which consists of factorization H=GJGT followed by the computation of the HSVD of the pair (G, J). Here G is square and non-singular. Let =G, where is diagonal such that the rows of have unit norms, and let B be defined as above. Essentially, we show that the computed eigenvalues of H will have log10(/2min()+/min(B)) correct decimal digits. This accuracy can be much higher then the one obtained by the classical QR and Jacobi methods applied to H, where the accuracy depends on the spectral condition number of H, particularly if the matrices B and are well conditioned, and we are interested in the accurate computation of tiny eigenvalues. Again, we give the perturbation and error bounds, and our theoretical predictions are confirmed by a series of numerical experiments.We also give the corresponding results for eigenvectors and hyperbolic singular vectors.

Izvorni jezik
Engleski

Znanstvena područja
Matematika



POVEZANOST RADA


Projekti:
0023002

Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split

Profili:

Avatar Url Ivan Slapničar (autor)


Citiraj ovu publikaciju:

Slapničar, Ivan
Highly accurate symmetric eigenvalue decomposition and hyperbolic SVD // Linear algebra and its applications, 358 (2003), 387-424 (međunarodna recenzija, članak, znanstveni)
Slapničar, I. (2003) Highly accurate symmetric eigenvalue decomposition and hyperbolic SVD. Linear algebra and its applications, 358, 387-424.
@article{article, author = {Slapni\v{c}ar, Ivan}, year = {2003}, pages = {387-424}, keywords = {Hyperbolic singular value decomposition, Symmetric eigenvalue problem, Symmetric indefinite decomposition, Jacobi method, Relative perturbation theory, High relative accuracy}, journal = {Linear algebra and its applications}, volume = {358}, issn = {0024-3795}, title = {Highly accurate symmetric eigenvalue decomposition and hyperbolic SVD}, keyword = {Hyperbolic singular value decomposition, Symmetric eigenvalue problem, Symmetric indefinite decomposition, Jacobi method, Relative perturbation theory, High relative accuracy} }
@article{article, author = {Slapni\v{c}ar, Ivan}, year = {2003}, pages = {387-424}, keywords = {Hyperbolic singular value decomposition, Symmetric eigenvalue problem, Symmetric indefinite decomposition, Jacobi method, Relative perturbation theory, High relative accuracy}, journal = {Linear algebra and its applications}, volume = {358}, issn = {0024-3795}, title = {Highly accurate symmetric eigenvalue decomposition and hyperbolic SVD}, keyword = {Hyperbolic singular value decomposition, Symmetric eigenvalue problem, Symmetric indefinite decomposition, Jacobi method, Relative perturbation theory, High relative accuracy} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • SCI-EXP, SSCI i/ili A&HCI





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