Constructing all self-adjoint matrices with prescribed spectrum and diagonal
- 215 Downloads
- 5 Citations
Abstract
The Schur–Horn Theorem states that there exists a self-adjoint matrix with a given spectrum and diagonal if and only if the spectrum majorizes the diagonal. Though the original proof of this result was nonconstructive, several constructive proofs have subsequently been found. Most of these constructive proofs rely on Givens rotations, and none have been shown to be able to produce every example of such a matrix. We introduce a new construction method that is able to do so. This method is based on recent advances in finite frame theory which show how to construct frames whose frame operator has a given prescribed spectrum and whose vectors have given prescribed lengths. This frame construction requires one to find a sequence of eigensteps, that is, a sequence of interlacing spectra that satisfy certain trace considerations. In this paper, we show how to explicitly construct every such sequence of eigensteps. Here, the key idea is to visualize eigenstep construction as iteratively building a staircase. This visualization leads to an algorithm, dubbed Top Kill, which produces a valid sequence of eigensteps whenever it is possible to do so. We then build on Top Kill to explicitly parametrize the set of all valid eigensteps. This yields an explicit method for constructing all self-adjoint matrices with a given spectrum and diagonal, and moreover all frames whose frame operator has a given spectrum and whose elements have given lengths.
Keywords
Schur–Horn Interlacing Majorization FramesMathematics Subject Classification (2010)
42C15Preview
Unable to display preview. Download preview PDF.
References
- 1.Antezana, J., Massey, P., Ruiz, M., Stojanoff, D.: The Schur–Horn theorem for operators and frames with prescribed norms and frame operator. Illinois J. Math. 51, 537–560 (2007)MathSciNetMATHGoogle Scholar
- 2.Batson, J., Spielman, D.A., Srivastava, N.: Twice-Ramanujan sparsifiers. In: STOC 09: Proc. 41st Annu. ACM Symp. Theory Comput., pp. 255–262 (2009)Google Scholar
- 3.Bendel, R.B., Mickey, M.R.: Population correlation matrices for sampling experiments. Commun. Stat. Simul. Comput. 7, 163–182 (1978)CrossRefGoogle Scholar
- 4.Bodmann, B.G., Casazza, P.G.: The road to equal-norm Parseval frames. J. Funct. Anal. 258, 397–420 (2010)MathSciNetCrossRefMATHGoogle Scholar
- 5.Cahill, J., Fickus, M., Mixon, D.G., Poteet, M.J., Strawn, N.: Constructing finite frames of a given spectrum and set of lengths. Appl. Comput. Harmon. Anal. (to appear). arXiv:1106.0921
- 6.Casazza, P.G., Fickus, M., Mixon, D.G.: Auto-tuning unit norm tight frames. Appl. Comput. Harmon. Anal. 32, 1‒15 (2012)MathSciNetCrossRefMATHGoogle Scholar
- 7.Casazza, P.G., Fickus, M., Mixon, D.G., Wang, Y., Zhou, Z.: Constructing tight fusion frames. Appl. Comput. Harmon. Anal. 30, 175–187 (2011)MathSciNetCrossRefMATHGoogle Scholar
- 8.Casazza, P.G., Kovačević, J.: Equal-norm tight frames with erasures. Adv. Comput. Math. 18, 387–430 (2003)MathSciNetCrossRefMATHGoogle Scholar
- 9.Casazza, P.G., Leon, M.: Existence and construction of finite tight frames. J. Comput. Appl. Math. 4, 277–289 (2006)MathSciNetMATHGoogle Scholar
- 10.Chan, N.N., Li, K.-H.: Diagonal elements and eigenvalues of a real symmetric matrix. J. Math. Anal. Appl. 91, 562–566 (1983)MathSciNetCrossRefMATHGoogle Scholar
- 11.Chu, M.T.: Constructing a Hermitian matrix from its diagonal entries and eigenvalues. SIAM J. Matrix Anal. Appl. 16, 207–217 (1995)MathSciNetCrossRefMATHGoogle Scholar
- 12.Davies, P.I., Higham, N.J.: Numerically stable generation of correlation matrices and their factors. BIT 40, 640–651 (2000)MathSciNetCrossRefMATHGoogle Scholar
- 13.Dhillon, I.S., Heath, R.W., Sustik, M.A., Tropp, J.A.: Generalized finite algorithms for constructing Hermitian matrices with prescribed diagonal and spectrum. SIAM J. Matrix Anal. Appl. 27, 61–71 (2005)MathSciNetCrossRefMATHGoogle Scholar
- 14.Dykema, K., Strawn, N.: Manifold structure of spaces of spherical tight frames. Int. J. Pure Appl. Math. 28, 217–256 (2006)MathSciNetMATHGoogle Scholar
- 15.Fickus, M., Mixon, D.G., Poteet, M.J.: In: Proc. SPIE 8138, 81380Q/1–8 (2011)Google Scholar
- 16.Goyal, V.K., Vetterli, M., Thao, N.T.: Quantized overcomplete expansions in \({\mathbb R}^N\): analysis, synthesis, and algorithms. IEEE Trans. Inform. Theory 44, 16–31 (1998)MathSciNetCrossRefMATHGoogle Scholar
- 17.Goyal, V.K., Kovačević, J., Kelner, J.A.: Quantized frame expansions with erasures. Appl. Comput. Harmon. Anal. 10, 203–233 (2001)MathSciNetCrossRefMATHGoogle Scholar
- 18.Higham, N.J.: Matrix nearness problems and applications. In: Gover, M.J.C., Barnett, S. (eds.) Applications of Matrix Theory, pp. 1–27. Oxford University Press (1989)Google Scholar
- 19.Holmes, R.B., Paulsen, V.I.: Optimal frames for erasures. Linear Algebra Appl. 377, 31–51 (2004)MathSciNetCrossRefMATHGoogle Scholar
- 20.Horn, A.: Doubly stochastic matrices and the diagonal of a rotation matrix. Amer. J. Math. 76, 620–630 (1954)MathSciNetCrossRefMATHGoogle Scholar
- 21.Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge University Press, Cambridge (1985)CrossRefMATHGoogle Scholar
- 22.Leite, R.S., Richa, T.R.W., Tomei, C.: Geometric proofs of some theorems of Schur–Horn type. Linear Algebra Appl. 286, 149–173 (1999)MathSciNetCrossRefMATHGoogle Scholar
- 23.Massey, P., Ruiz, M.: Tight frame completions with prescribed norms. Sampl. Theory Signal Image Process. 7, 1–13 (2008)MathSciNetMATHGoogle Scholar
- 24.Schur, I.: Über eine klasse von mittelbildungen mit anwendungen auf die determinantentheorie. Sitzungsber. Berl. Math. Ges. 22, 9–20 (1923)Google Scholar
- 25.Strawn, N.: Finite frame varieties: nonsingular points, tangent spaces, and explicit local parameterizations. J. Fourier Anal. Appl. 17, 821–853 (2011)MathSciNetCrossRefMATHGoogle Scholar
- 26.Tropp, J.A., Dhillon, I.S., Heath, R.W.: Finite-step algorithms for constructing optimal CDMA signature sequences. IEEE Trans. Inform. Theory 50, 2916–2921 (2004)MathSciNetCrossRefGoogle Scholar
- 27.Viswanath, P., Anantharam, V.: Optimal sequences and sum capacity of synchronous CDMA systems. IEEE Trans. Inform. Theory 45, 1984–1991 (1999)MathSciNetCrossRefMATHGoogle Scholar