Given a square matrix A, a Brauer’s theorem [Brauer A., Limits for the characteristic roots of a matrix. IV. Applications to stochastic matrices, Duke Math. J., 1952, 19(1), 75–91] shows how to modify one single eigenvalue of A via a rank-one perturbation without changing any of the remaining eigenvalues. Older and newer results can be considered in the framework of the above theorem. In this paper, we present its application to stabilization of control systems, including the case when the system is noncontrollable. Other applications presented are related to the Jordan form of A and Wielandt’s and Hotelling’s deflations. An extension of the aforementioned Brauer’s result, Rado’s theorem, shows how to modify r eigenvalues of A at the same time via a rank-r perturbation without changing any of the remaining eigenvalues. The same results considered by blocks can be put into the block version framework of the above theorem.
@article{bwmeta1.element.doi-10_2478_s11533-011-0113-0, author = {Rafael Bru and Rafael Cant\'o and Ricardo Soto and Ana Urbano}, title = {A Brauer's theorem and related results}, journal = {Open Mathematics}, volume = {10}, year = {2012}, pages = {312-321}, zbl = {1248.15007}, language = {en}, url = {http://dml.mathdoc.fr/item/bwmeta1.element.doi-10_2478_s11533-011-0113-0} }
Rafael Bru; Rafael Cantó; Ricardo Soto; Ana Urbano. A Brauer’s theorem and related results. Open Mathematics, Tome 10 (2012) pp. 312-321. http://gdmltest.u-ga.fr/item/bwmeta1.element.doi-10_2478_s11533-011-0113-0/
[1] Brauer A., Limits for the characteristic roots of a matrix. IV. Applications to stochastic matrices, Duke Math. J., 1952, 19(1), 75–91 http://dx.doi.org/10.1215/S0012-7094-52-01910-8 | Zbl 0046.01202
[2] Crouch P.E., Introduction to Mathematical Systems Theory, Mathematik-Arbeitspapiere, Bremen, 1988
[3] Delchamps D.F., State-Space and Input-Output Linear Systems, Springer, New York, 1988 http://dx.doi.org/10.1007/978-1-4612-3816-4
[4] Hautus M.L.J., Controllability and observability condition of linear autonomous systems, Nederl. Akad. Wetensch. Indag. Math., 1969, 72, 443–448 | Zbl 0188.46801
[5] Kailath T., Linear Systems, Prentice Hall Inform. System Sci. Ser., Prentice Hall, Englewood Cliffs, 1980
[6] Langville A.N., Meyer C.D., Deeper inside PageRank, Internet Math., 2004, 1(3), 335–380 http://dx.doi.org/10.1080/15427951.2004.10129091 | Zbl 1098.68010
[7] Perfect H., Methods of constructing certain stochastic matrices. II, Duke Math. J., 1955, 22(2), 305–311 http://dx.doi.org/10.1215/S0012-7094-55-02232-8 | Zbl 0068.32704
[8] Saad Y., Numerical Methods for Large Eigenvalue Problems, Classics Appl. Math., 66, SIAM, Philadelphia, 2011 http://dx.doi.org/10.1137/1.9781611970739
[9] Soto R.L., Rojo O., Applications of a Brauer theorem in the nonnegative inverse eigenvalue problem, Linear Algebra Appl., 2006, 416(2–3), 844–856 http://dx.doi.org/10.1016/j.laa.2005.12.026 | Zbl 1097.15014
[10] Wilkinson J.H., The Algebraic Eigenvalue Problem, Clarendon Press, Oxford, 1965 | Zbl 0258.65037