Vides, Fredy On uniform connectivity of algebraic matrix sets. (English) Zbl 1482.47151 Banach J. Math. Anal. 13, No. 4, 918-943 (2019). Let the space \(M_n(\mathbb{C})^m\) of \(m\)-tuples of \(n\times n\) pairwise commuting normal contractions \(\mathbf{X}=(X_1,\ldots,X_m)\) be equipped with some metric \(\eth\). Given \(r\) complex polynomials \(p_1,\ldots,p_r\) in \(m\) variables, the set of algebraic normal contractions is \(\mathbb{Z}\mathbb{D}_n^m(p_1,\ldots,p_r)=\{\mathbf{X}\in M_n(\mathbb{C})^m: \|p_j(\mathbf{X})\|=0\text{ for } j=1,\ldots,r\}\). The author proves the connectivity of this set in the following sense. Given two elements \(\mathbf{X},\mathbf{Y}\in \mathbb{Z}\mathbb{D}_n^m(p_1,\ldots,p_r)\) with \(\eth(\mathbf{X},\mathbf{Y})\le\delta\), it is possible to connect them with a smooth path that stays in the neighborhood of \(\mathbf{X}\), actually in the ball \(B_\eth(\mathbf{X},\varepsilon)\) for given \(\varepsilon\ge\delta\). The path is uniform because it does not depend of \(n\). In a second part, he also proves uniform connectivity of nearly algebraic normal contractions, in which case it is assumed that \(\|p_j(\mathbf{X})\|\le\varepsilon\) for \(j=1,\ldots,r\) instead of being zero. This is solved by proving that there are algebraic normal contractions \(\tilde{\mathbf{X}}\) and \(\tilde{\mathbf{Y}}\) close to the given nearly algebraic normal contractions \(\mathbf{X}\) and \(\mathbf{Y}\) that satisfy the previous theorem. The motivation for these problems comes, for example, from the theory of structure preserving perturbation theory from linear algebra and how preconditioning influences the result Reviewer: Adhemar Bultheel (Leuven) MSC: 47N40 Applications of operator theory in numerical analysis 47A58 Linear operator approximation theory 15A60 Norms of matrices, numerical range, applications of functional analysis to matrix theory 15A27 Commutativity of matrices Keywords:joint spectrum; matrix function; functional calculus; matrix path; eigenvalue clustering PDFBibTeX XMLCite \textit{F. Vides}, Banach J. Math. Anal. 13, No. 4, 918--943 (2019; Zbl 1482.47151) Full Text: DOI arXiv Euclid References: [1] M. Ahues, F. Dias d’Almeida, A. Largillier, and P. B. Vasconcelos, Spectral refinement for clustered eigenvalues of quasi-diagonal matrices, Linear Algebra Appl. 413 (2006), no. 2-3, 394-402. · Zbl 1089.65029 · doi:10.1016/j.laa.2005.03.004 [2] R. Bhatia, Matrix Analysis, Grad. Texts in Math. 169, Springer, New York, 1996. · Zbl 0863.15001 [3] R. Bhatia, Pinching, trimming, truncating, and averaging of matrices, Amer. Math. Monthly 107 (2000), no. 7, 602-608. · Zbl 0984.15024 · doi:10.1080/00029890.2000.12005245 [4] R. Bhatia, C. Davis, and A. McIntosh, Perturbation of spectral subspaces and solution of linear operator equations, Linear Algebra Appl, 52/53 (1983), 45-67. · Zbl 0518.47013 · doi:10.1016/0024-3795(83)90007-1 [5] M. T. Chu, Linear algebra algorithms as dynamical systems, Acta Numer. 17 (2008), 1-86. · Zbl 1165.65021 · doi:10.1017/S0962492906340019 [6] C. Di Fiore and P. Zellini, Matrix algebras in optimal preconditioning, Linear Algebra Appl. 335 (2001), no. 1-3, 1-54. · Zbl 0983.65061 · doi:10.1016/S0024-3795(00)00137-3 [7] Z. Drmač and K. Veselić, Approximate eigenvectors as preconditioner, Linear Algebra Appl. 309 (2000), no. 1-3, 191-215. · Zbl 0948.65046 [8] A. Edelman, E. Elmroth, and B. Kågström, A geometric approach to perturbation theory of matrices and matrix pencils, Part II: A stratification-enhanced staircase algorithm, SIAM J. Matrix Anal. Appl. 20 (1999), no. 3, 667-699. · Zbl 0940.65040 · doi:10.1137/S0895479896310184 [9] T.-M. Huang, W.-W. Lin, and W. Wang, A hybrid Jacobi-Davidson method for interior cluster eigenvalues with large null-space in three dimensional lossless Drude dispersive metallic photonic crystals, Comput. Phys. Commun. 207 (2016), 221-231. · Zbl 1375.78044 · doi:10.1016/j.cpc.2016.06.017 [10] E. Kokabifar, G. B. Loghmani, and P. J. Psarrakos, On The Distance from a Weakly Normal Matrix Polynomial to Matrix Polynomials with a Prescribed Multiple Eigenvalue, Electron. J. Linear Algebra 31 (2016), 71-86. · Zbl 1332.15025 · doi:10.13001/1081-3810.2921 [11] D. Kressner and A. Šušnjara, Fast Computation of Spectral Projectors of Banded Matrices, SIAM J. Matrix Anal. Appl. 38 (2017), no. 3, 984-1009. · Zbl 1373.65023 · doi:10.1137/16M1087278 [12] H. Lin, An Introduction to the Classification of Amenable \(C^*\)-algebras, World Scientific, River Edge, NJ, 2001. · Zbl 1013.46055 [13] T. A. Loring and F. Vides, Local matrix homotopies and soft tori, Banach J. Math. Anal. 12 (2018), no. 1, 167-190. · Zbl 1394.46056 · doi:10.1215/17358787-2017-0048 [14] T. Maehara and K. Murota, Algorithm for error-controlled simultaneous block-diagonalization of matrices, SIAM J. Matrix Anal. Appl. 32 (2011), no. 2, 605-620. · Zbl 1227.65039 · doi:10.1137/090779966 [15] C. Musco, C. Musco, and A. Sidford, “Stability of the Lanczos method for matrix function approximation” in Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms (Philadelphia, 2018), SIAM, Philadelphia, 2018, 1605-1624. · Zbl 1406.65032 [16] J. A. Sifuentes, M. Embree, and R. B. Morgan, GMRES convergence for perturbed coefficient matrices, with application to approximate deflation preconditioning, SIAM J. Matrix Anal. Appl. 34 (2013), no. 3, 1066-1088. · Zbl 1314.65051 · doi:10.1137/120884328 [17] F. Tudisco, C. Di Fiore, and E. E. Tyrtyshnikov, Optimal rank matrix algebras preconditioners, Linear Algebra Appl. 438 (2013), no. 1, 405-427. · Zbl 1308.65044 · doi:10.1016/j.laa.2012.07.042 [18] E. E. Tyrtyshnikov, A. Y. Yeremin, and N. L. Zamarashkin, Clusters, preconditioners, convergence, Linear Algebra Appl. 263 (1997), 25-48. · Zbl 0917.65032 · doi:10.1016/S0024-3795(96)00445-4 [19] A. J. Wathen, Preconditioning, Acta Numer. 24 (2015), 329-376. · Zbl 1316.65039 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.