Abstract
Important properties such as differentiability and convexity of symmetric functions in can be transferred to the corresponding spectral functions and vice-versa. Continuing to built on this line of research, we hereby prove that a spectral function is prox-regular if and only if the underlying symmetric function is prox-regular. Relevant properties of symmetric sets are also discussed.
References
- D. Aussel, A. Daniilidis, L. Thibault: Subsmooth sets: functional characterizations and related concepts, Trans. Amer. Math. Soc. 357(4) (2005) 1275–1301.https://doi.org/10.1090/S0002-9947-04-03718-3Google Scholar
- F. Bernard, L. Thibault: Uniform prox-regularity of functions and epigraphs in Hilbert spaces, Nonlinear Anal., Theory Methods Appl. 60A(2) (2005) 187–207.https://doi.org/10.1016/j.na.2004.04.015Google Scholar
- J. M. Borwein, A. S. Lewis: Convex Analysis and Nonlinear Optimization, 2nd Ed., Springer, New York (2006).https://doi.org/10.1007/978-0-387-31256-9Google Scholar
- F. Clarke, R. Stern, P. Wolenski: Proximal smoothness and the lower-C 2 property, J. Convex Analysis 2(1-2) (1995) 117–144.https://doi.org/10.68381/jca02008Google Scholar
- J. Dadok: On the C ∞ Chevalley’s theorem, Adv. Math. 44 (1982) 121–131.https://doi.org/10.1016/0001-8708(82)90002-0Google Scholar
- A. Daniilidis, W. Hare, J. Malick: Geometrical interpretation of proximal-type algorithms in structured nonsmooth optimization, Optimization 55(5-6) (2006) 481–503.https://doi.org/10.1080/02331930600815884Google Scholar
- C. Davis: All convex invariant functions of hermitian matrices, Arch. Math. 8 (1957) 276–278.https://doi.org/10.1007/BF01898787Google Scholar
- W. Hare, A. S. Lewis: Identifying active constraints via partial smoothness and prox-regularity, J. Convex Analysis 11(2) (2004) 251–266.https://doi.org/10.68381/jca11017Google Scholar
- R. A. Horn, Ch. R. Johnson: Matrix Analysis, Cambridge University Press, Cambridge (1989); new Ed. (1999).Google Scholar
- A. S. Lewis, J. Malick: Alternating projections on manifolds, Math. Oper. Res. 33(1) (2008) 216–234.https://doi.org/10.1287/moor.1070.0291Google Scholar
- A. S. Lewis: Convex analysis on the Hermitian matrices, SIAM J. Optim. 6 (1996) 164–177.https://doi.org/10.1137/0806009Google Scholar
- A. S. Lewis: Derivatives of spectral functions, Math. Oper. Res. 21 (1996) 576–588.https://doi.org/10.1287/moor.21.3.576Google Scholar
- A. S. Lewis: Nonsmooth analysis of eigenvalues, Math. Program. 84(1) (1999) 1–24.https://doi.org/10.1007/s10107980004aGoogle Scholar
- A. S. Lewis, H. S. Sendov: Twice differentiable spectral functions, SIAM J. Matrix Anal. Appl. 23(2) (2001) 368–386.https://doi.org/10.1137/S089547980036838XGoogle Scholar
- M. K. H. Fan, N.-K. Tsing, E. I. Verriest: On analyticity of functions involving eigenvalues, Linear Algebra Appl. 207 (1994) 159–180.https://doi.org/10.1016/0024-3795(94)90009-4Google Scholar
- R. A. Poliquin, R. T. Rockafellar: Prox-regular functions in variational analysis, Trans. Amer. Math. Soc. 348 (1996) 1805–1838.https://doi.org/10.1090/S0002-9947-96-01544-9Google Scholar
- R. A. Poliquin, R. T. Rockafellar, L. Thibault: Local differentiability of distance functions, Trans. Amer. Math. Soc. 352 (2000) 5231–5249.https://doi.org/10.1090/S0002-9947-00-02550-2Google Scholar
- R. T. Rockafellar: Favorable classes of Lipschitz continuous functions in subgradient optimization, in: Progress in Non-Differentiable Optimization, IIASA Collaborative Proceedings Series, International Institute of Applied Systems Analysis, Laxenburg, Austria, E. Nurminski (ed.), (1982) 125–143.Google Scholar
- R. T. Rockafellar, R. J.-B. Wets: Variational Analysis, Springer, Berlin (1998).https://doi.org/10.1007/978-3-642-02431-3Google Scholar
Suggested citation
Published by Heldermann Verlag, 2008. Rights now held by Banach Press LLC.
