Fundamental insights into the properties of a function come from the study of its Moreau envelopes and Proximal point mappings. In this paper we examine the stability of these two objects under several types of perturbations. In the simplest case, we consider tilt-perturbations, i.e. perturbations which correspond to adding a linear term to the objective function. We show that for functions that have single-valued Lipschitz continuous proximal mappings, in particular for prox-regular functions, tilt-perturbations result in stable, i.e. single-valued Lipschitz continuous, proximal point mappings. In the more complex case, we consider the class of parametrically prox-regular functions. These include most of the functions that arise in the framework of nonlinear programming and its extensions (e.g. convex, lower-C2\mathcal{C}^2, strongly amenable (convexly composite)). New characterizations of prox-regularity are given and more general perturbations along the lines of A. B. Levy, R. A. Poliquin and R. T. Rockafellar [SIAM J. Optimization 10(2) (2000) 580–604] are studied. We show that under suitable conditions (compatible parameterization, positive coderivative,...), the proximal point mappings of the function fu(x)=f(x,u)f_u(x) = f(x, u) depends in a Lipschitz fashion on the parameter u and the prox-parameter r.

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

Warren L. Hare

IRMACS, Simon Fraser University, Burnaby, B.C. V5A 1S6, Canada

whare@irmacs.sfu.ca

René A. Poliquin

Dept. of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alb. T6G 2G1, Canada

rene.poliquin@ualberta.ca
  1. F. Bernard, L. Thibault. Prox-regular functions in Hilbert spaces, J. Math. Anal. Appl. 303(1) (2005) 1–14.https://doi.org/10.1016/j.jmaa.2004.06.003Google Scholar
  2. F. Bernard, L. Thibault, D. Zagrodny: Integration of primal lower nice functions in Hilbert spaces, J. Optimization Theory Appl. 124(3) (2005) 561–579.https://doi.org/10.1007/s10957-004-1174-zGoogle Scholar
  3. C. L. Chiriac: Proximal point algorithm with generalized Bregman functions, Fixed Point Theory 6(1) (2005) 25–32.Google Scholar
  4. M. C. Ferris: Finite termination of the proximal point algorithm, Math. Program., Ser. A 50(3) (1991) 359–366.https://doi.org/10.1007/BF01594944Google Scholar
  5. A. Hamdi: A primal-dual proximal point algorithm for constrained convex programs, Appl. Math. Comput. 162(1) (2005) 293–303.https://doi.org/10.1016/j.amc.2003.12.137Google Scholar
  6. W. L. Hare, A. S. Lewis: Identifying active constraints via partial smoothness and prox-regularity, J. Convex Analysis 11 (2004) 251–266.https://doi.org/10.68381/jca11017Google Scholar
  7. W. L. Hare, C. Sagastizábal: Computing proximal points of nonconvex functions, Math. Program., to appear.Google Scholar
  8. S. Ibaraki, M. Fukushima: Primal-dual proximal point algorithm for multicommodity network flow problems, J. Oper. Res. Soc. Japan 37(4) (1994) 297–309.https://doi.org/10.15807/jorsj.37.297Google Scholar
  9. A. Iusem, R. D. C. Monteiro: On dual convergence of the generalized proximal point method with Bregman distances, Math. Oper. Res. 25(4) (2000) 606–624.https://doi.org/10.1287/moor.25.4.606.12110Google Scholar
  10. A. N. Iusem, T. Pennanen, B. F. Svaiter: Inexact variants of the proximal point algorithm without monotonicity, SIAM J. Optim. 13(4) (2003) 1080–1097.https://doi.org/10.1137/S1052623401399587Google Scholar
  11. C. Lemaréchal, F. Oustry, C. Sagastizábal. The U -Lagrangian of a convex function, Trans. Amer. Math. Soc. 352(2) (2000) 711–729.https://doi.org/10.1090/S0002-9947-99-02243-6Google Scholar
  12. A. B. Levy, R. A. Poliquin, R. T. Rockafellar: Stability of locally optimal solutions, SIAM J. Optim. 10(2) (2000) 580–604.https://doi.org/10.1137/S1052623498348274Google Scholar
  13. B. Martinet. Régularisation d’inéquations variationnelles par approximations successives, Rev. Franç. Inform. Rech. Opér. 4(R-3) (1970) 154–158.https://doi.org/10.1051/m2an/197004R301541Google Scholar
  14. B. Martinet: Détermination approchée d’un point fixe d’une application pseudo-contractante. Cas de l’application prox, C. R. Acad. Sci., Paris, Sér. A 274 (1972) 163–165.Google Scholar
  15. R. Mifflin, C. Sagastizábal: Proximal points are on the fast track, J. Convex Analysis 9(2) (2002) 563–579.https://doi.org/10.68381/jca09033Google Scholar
  16. J.-J. Moreau. Proximité et dualité dans un espace hilbertien, Bull. Soc. Math. Fr. 93 (1965) 273–299.https://doi.org/10.24033/bsmf.1625Google Scholar
  17. J.-J. Moreau: Propriétés des applications “prox”, C. R. Acad. Sci., Paris 256 (1963) 1069–1071.Google Scholar
  18. T. Pennanen: Local convergence of the proximal point algorithm and multiplier methods without monotonicity, Math. Oper. Res. 27(1) (2002) 170–191.https://doi.org/10.1287/moor.27.1.170.331Google Scholar
  19. R. A. Poliquin, R. T. Rockafellar: Generalized hessian properties of regularized nonsmooth functions, SIAM J. Optim. 6(4) (1996) 1121–1137.https://doi.org/10.1137/S1052623494279316Google Scholar
  20. R. A. Poliquin, R. T. Rockafellar: Prox-regular functions in variational analysis, Trans. Amer. Math. Soc. 348(5) (1996) 1805–1838.https://doi.org/10.1090/S0002-9947-96-01544-9Google Scholar
  21. R. A. Poliquin, R. T. Rockafellar: Tilt stability of a local minimum, SIAM J. Optim. 8(2) (1998) 287–299.https://doi.org/10.1137/S1052623496309296Google Scholar
  22. R. A. Poliquin, R. T. Rockafellar, L. Thibault: Local differentiability of distance functions, Trans. Amer. Math. Soc. 352(11) (2000) 5231–5249.https://doi.org/10.1090/S0002-9947-00-02550-2Google Scholar
  23. R. T. Rockafellar: Monotone operators and the proximal point algorithm, SIAM J. Control Optimization 14(5) (1976) 877–898.https://doi.org/10.1137/0314056Google Scholar
  24. R. T. Rockafellar, R. J.-B. Wets: Variational Analysis, Grundlehren der Mathematischen Wissenschaften 317, Springer, Berlin (1998).https://doi.org/10.1007/978-3-642-02431-3Google Scholar

W. L. Hare, R. A. Poliquin. “Prox-Regularity and Stability of the Proximal Mapping.” Journal of Convex Analysis 14 (2007), No. 3, 589–606. https://doi.org/10.68381/jca14035