Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-24T17:36:30.644Z Has data issue: false hasContentIssue false

Proto-Differentiation of Subgradient Set-Valued Mappings

Published online by Cambridge University Press:  20 November 2018

René A. Poliquin*
Affiliation:
University of AlbertaEdmonton, Alberta
Rights & Permissions [Opens in a new window]

Extract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Set-valued mappings arise quite naturally in optimization and nonsmooth analysis. In optimization, typically one has a family of optimization problems that depend on some parameter. One can then associate to this family of problems the set-valued mappings that assign to the parameter the set of optimal solutions, the set of feasible solutions or the set of multipliers. Many of these set-valued mappings encountered in optimization have been shown to be “proto-differentiable” (see Rockafellar [16]) i.e., in some sense these set-valued mappings are “differentiable”. Using estimates provided by the proto-derivatives, see Proposition 2.1, one can then obtain information on how the sets depend on the parameter. The concept of proto-differentiation is described in Section 2.

Keywords

Type
Research Article
Copyright
Copyright © Canadian Mathematical Society 1990

References

1. Burke, J.V. and Poliquin, R.A., Second-order opiimality conditions in non-finite convex compositeoptimization, preprint.Google Scholar
2. Clarke, F.H., Optimization and nonsmooth analysis (John Wiley, New York, 1983).Google Scholar
3. King, A.J., Generalized delta theorems for multivalued mappings and measurable selections, preprint.Google Scholar
4. Mangasarian, O.L. and Fromovitz, S., The Fritz John conditions in the presence of equality andinequality constraints, J. Math. Anal. Appl. 17 (1967), 3747.Google Scholar
5. Ndoutoume, J.L., Condition nécessaire d optimalité du premier ordre pour des problèmes decontrôle optimal d'inéquations variationnelles, Publications AVAMAC, Université Perpignan, Mathématiques, 66025 cedex.Google Scholar
6. Poliquin, R.A., Proto-differentiation and integration of proximal subgradients, Ph.D. dissertation, University of Washington (1988).Google Scholar
7. Poliquin, R.A. and Rockafellar, R.T., Proto-derivatives of solution mappings and sensitivityanalysis in optimization, forthcoming.Google Scholar
8. Rockafellar, R.T., Convex analysis (Princeton University Press, 1970).Google Scholar
9. Rockafellar, R.T., Directionally Lipschitzian functions and subdifferential calculus, Proceedings of the London Mathematical Society (3), 34 (1979).Google Scholar
10. Rockafellar, R.T., The theory of subgradients and its applications to problems of optimization: Convexand nonconvex functions (Heldermann, Berlin, 1981).Google Scholar
11. Rockafellar, R.T., Favorable classes of Lipschitz-continuous functions in subgradient optimization, Progress in Nondifferentiable Optimization, USA Collaborative Proceedings Series, International Institute of Applied Systems Analysis, Laxenburg, Austria (1982), 125144.Google Scholar
12. Rockafellar, R.T., Extensions of subgradient calculus with applications to optimization, Nonlinear Analysis 9(1985), 665698.Google Scholar
13. Rockafellar, R.T., First and second-order epi-differentiability in nonlinear programming, Trans. Amer. Math. Soc. 307 (1988), 75108.Google Scholar
14. Rockafellar, R.T., Second-order optimality conditions in nonlinear programming obtained by way of epiderivatives, Math, of Op. Res., to appear.Google Scholar
15. Generalized second derivatives of convex functions and saddle functions, forthcoming.Google Scholar
16. Proto-differentiability of set-valued mappings and its applications in optimization, Ann. Inst. H. Poincaré: Analyse Non Linéaire, submitted.Google Scholar
17. Rockafellar, R.T., Perturbation of generalized Kuhn-Tucker points in finite-dimensional optimization, forthcoming.Google Scholar
18. Sun, J., On monotropic piecewise quadratic programming, Ph.D. dissertation, University of Washington (1986).Google Scholar
19. Wets, R.J.B., Convergence of convex functions, variational inequalities and convex optimizationproblems, in Variational inequalities and complimentary problems (John Wiley and Sons).Google Scholar