Normal decomposition systems unify many results from convex matrix analysis regarding functions that are invariant with respect to a group of transformations – particularly those matrix functions that are unitarily-invariant and the affiliated permutation-invariant "spectral functions" that depend only on eigenvalues. Spectral functions extend in a natural way to Euclidean Jordan algebras, and several authors have studied the problem of making a Euclidean Jordan algebra into a normal decomposition system. In particular it is known to be possible with respect to the "eigenvalues of" map when the algebra is essentially-simple. We show the converse, that essential-simplicity is essential to that process.

Contact details are reproduced from the original publication and may be historical.

Michael Orlitzky

Dept. of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, U.S.A.

michael@orlitzky.com
  1. M. Baes: Convexity and differentiability properties of spectral functions and spectral mappings on Euclidean Jordan algebras, Linear Algebra Appl. 422/2-3 (2007) 664–700.https://doi.org/10.1016/j.laa.2006.11.025Google Scholar
  2. C. Davis: All convex invariant functions of hermitian matrices, Archiv Math. 8/4 (1957) 276–278.https://doi.org/10.1007/BF01898787Google Scholar
  3. J. Faraut, A. Korányi: Analysis on Symmetric Cones, Clarendon Press, Oxford (1994).https://doi.org/10.1093/oso/9780198534778.001.0001Google Scholar
  4. S. Friedland: Convex spectral functions, Linear Multilinear Algebra 9/4 (1981) 299–316.https://doi.org/10.1080/03081088108817381Google Scholar
  5. M. S. Gowda: Optimizing certain combinations of spectral and linear/distance functions over spectral sets, Technical Report trGOW19-01, March (2019) http://www.math.umbc.edu/ gowda/tech-reports/trGOW19-01.html.Google Scholar
  6. M. S. Gowda, J. Jeong: Commutation principles in Euclidean Jordan algebras and normal decomposition systems, SIAM J. Optimization 27/3 (2017) 1390–1402.https://doi.org/10.1137/16M1071006Google Scholar
  7. L. C. Grove, C. T. Benson: Finite Reflection Groups, 2nd ed., Graduate Texts in Mathematics 99, Springer, New York (1985).https://doi.org/10.1007/978-1-4757-1869-0Google Scholar
  8. O. Güler: Barrier functions in interior point methods, Math. Oper. Res. 21/4 (1996) 860–885.https://doi.org/10.1287/moor.21.4.860Google Scholar
  9. G. H. Hardy, J. E. Littlewood, G. Pólya: Inequalities, 2nd ed., Cambridge University Press, New York (1952).Google Scholar
  10. R. A. Horn, C. R. Johnson: Topics in Matrix Analysis, Cambridge University Press, Cambridge (1991).https://doi.org/10.1017/CBO9780511840371Google Scholar
  11. A. Iusem, A. Seeger: On pairs of vectors achieving the maximal angle of a convex cone, Math. Programming B 104/2-3 (2005) 501–523.https://doi.org/10.1007/s10107-005-0626-zGoogle Scholar
  12. J. Jeong, M. S. Gowda: Spectral sets and functions on Euclidean Jordan algebras, Linear Algebra Appl. 518/1 (2017) 31–56.https://doi.org/10.1016/j.laa.2016.12.020Google Scholar
  13. P. Jordan, J. von Neumann, E. Wigner: On an algebraic generalization of the quantum mechanical formalism, Annals Math. (2) 35/1 (1934) 29–64.https://doi.org/10.2307/1968117Google Scholar
  14. A. S. Lewis: Convex analysis on the Hermitian matrices, SIAM J. Optimization 6/1 (1996) 164–177.https://doi.org/10.1137/0806009Google Scholar
  15. A. S. Lewis: Group invariance and convex matrix analysis, SIAM J. Matrix Analysis Appl. 17/4 (1996) 927–949.https://doi.org/10.1137/S0895479895283173Google Scholar
  16. A. S. Lewis: Convex analysis on Cartan subspaces, Nonlinear Analysis 42/5 (2000) 813–820.https://doi.org/10.1016/S0362-546X(99)00126-1Google Scholar
  17. Y. Lim, J. Kim, L. Faybusovich: Simultaneous diagonalization on simple Euclidean Jordan algebras and its applications, Forum Math. 15/4 (2003) 639–644.https://doi.org/10.1515/form.2003.034Google Scholar
  18. E. Marques de Sá: Exposed faces and duality for symmetric and unitarily invariant norms, Linear Algebra Appl. 197-198 (1994) 429–450.https://doi.org/10.1016/0024-3795(94)90499-5Google Scholar
  19. A. W. Marshall, I. Olkin, B. C. Arnold: Inequalities: Theory of Majorization and its Applications, 2nd ed., Springer, New York (2011).https://doi.org/10.1007/978-0-387-68276-1Google Scholar
  20. K. McCrimmon: A Taste of Jordan Algebras, Springer, New York (2004).Google Scholar
  21. L. Mirsky: On the trace of matrix products, Math. Nachrichten 20/3-6 (1959) 171–174.https://doi.org/10.1002/mana.19590200306Google Scholar
  22. L. Mirsky: A trace inequality of John von Neumann, Monatshefte Math. 79/4 (1975) 303–306.https://doi.org/10.1007/BF01647331Google Scholar
  23. Y. E. Nesterov, M. J. Todd: Self-scaled barriers and interior-point methods for convex programming, Math. Oper. Res. 22/1 (1997) 1–42.https://doi.org/10.1287/moor.22.1.1Google Scholar
  24. M. Niezgoda: Group majorization and Schur type inequalities, Linear Algebra Appl. 268/1 (1998) 9–30.https://doi.org/10.1016/S0024-3795(97)89322-6Google Scholar
  25. H. Ramírez, A. Seeger, D. Sossa: Commutation principle for variational problems on Euclidean Jordan algebras, SIAM J. Optimization 23/2 (2013) 687–694.https://doi.org/10.1137/120879397Google Scholar
  26. D. Sun, J. Sun: Löwner’s operator and spectral functions in Euclidean Jordan algebras, Math. Oper. Res. 33/2 (2008) 421–445.https://doi.org/10.1287/moor.1070.0300Google Scholar
  27. T.-Y. Tam: An extension of a result of Lewis, Elec. J. Linear Algebra 5 (1999) 1–10.https://doi.org/10.13001/1081-3810.1026Google Scholar
  28. C. M. Theobald: An inequality for the trace of the product of two symmetric matrices, Math. Proc. Cambridge Phil. Soc. 77/2 (1975) 265–267.https://doi.org/10.1017/S0305004100051070Google Scholar

M. Orlitzky. “Proscribed Normal Decompositions of Euclidean Jordan Algebras.” Journal of Convex Analysis 29 (2022), No. 3, 755–766. https://doi.org/10.68381/jca29043