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Implicit multifunction theorems for the sensitivity analysis of variational conditions

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Abstract

We study implicit multifunctions (set-valued mappings) obtained from inclusions of the form 0∈M(p,x), whereM is a multifunction. Our basic implicit multifunction theorem provides an approximation for a generalized derivative of the implicit multifunction in terms of the derivative of the multifunctionM. Our primary focus is on three special cases of inclusions 0∈M(p,x) which represent different kinds of generalized variational inequalities, called “variational conditions”. Appropriate versions of our basic implicit multifunction theorem yield approximations for generalized derivatives of the solutions to each kind of variational condition. We characterize a well-known generalized Lipschitz property in terms of generalized derivatives, and use our implicit multifunction theorems to state sufficient conditions (and necessary in one case) for solutions of variational conditions to possess this Lipschitz, property. We apply our results to a general parameterized nonlinear programming problem, and derive a new second-order condition which guarantees that the stationary points associated with the Karush-Kuhn-Tucker conditions exhibit generalized Lipschitz continuity with respect to the parameter.

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References

  1. J.P. Aubin and H. Frankowska,Set-valued Analysis (Birkhäuser, Basel, 1990).

    MATH  Google Scholar 

  2. J.F. Bonnans, Local analysis of Newton-type methods for variational inequalities and nonlinear programming,Applied Mathematics and Optimization 29 (1994) 161–186.

    Article  MATH  MathSciNet  Google Scholar 

  3. A.L. Dontchev, Implicit function theorems for generalized equations,Mathematical Programming 70 (1995) 91–106.

    MathSciNet  Google Scholar 

  4. A.L. Dontchev and W.W. Hager, On Robinson's implicit function theorem, in:Set-Valued Analysis and Differential Inclusions (Birkhäuser, Basel, 1991).

    Google Scholar 

  5. A.L. Dontchev and W.W. Hager, Lipschitzian stability in nonlinear control and optimization,SIAM Journal on Control and Optimization 31 (1993) 569–603.

    Article  MATH  MathSciNet  Google Scholar 

  6. A.L. Dontchev and W.W. Hager, Implicit functions. Lipschitz maps, and stability in optimization,Mathematics of Operations Research 19 (1994) 753–768.

    MATH  MathSciNet  Google Scholar 

  7. A.L. Dontchev, Characterizations of Lipschitz stability in optimization, in: R. Lucchetti and J. Revaslki, eds.,Recent Developments in Well-posed Problems (Kluwer Academic Publishers, Dordrecht, 1995). pp. 95–115.

    Google Scholar 

  8. A.V. Fiacco and J. Kyparisis, Sensitivity analysis in nonlinear programming under second order assumptions, in: A.V. Balakrishnan and E.M. Thoma, eds.,Lecture Notes in Control and Information Sciences, Vol. 66 (Springer, Berlin, 1985) pp. 74–97.

    Google Scholar 

  9. A.J. King and R.T. Rockafellar, Sensitivity analysis for nonsmooth generalized equations,Mathematical Programming 55 (1992) 193–212.

    Article  MathSciNet  Google Scholar 

  10. D. Klatte, Nonlinear optimization under data perturbations, in: W. Krabs and J. Zowe, eds.,Modern Methods of Optimization (Springer, Berlin, 1992) pp. 204–235.

    Google Scholar 

  11. J. Kyparisis, Parametric variational inequalities with multivalued solution sets,Mathematics of Operations Research 17 (1992) 341–364.

    MATH  MathSciNet  Google Scholar 

  12. A.B. Levy and R.T. Rockafellar, Sensitivity analysis of solutions to generalized equations,Transactions of the American Mathematical Society 345 (1994) 661–671.

    Article  MATH  MathSciNet  Google Scholar 

  13. A.B. Levy and R.T. Rockafellar, Sensitivity of solutions in nonlinear programming problems with nonunique multipliers, in: D.-Z. Du, L. Qi and R.S. Womersley, eds.,Recent Advances in Nonsmooth Optimization (World Scientific, Singapore, 1995) pp. 215–223.

    Google Scholar 

  14. A.B. Levy and R.T. Rockafellar, Variational conditions and the proto-differentiation of partial subgradient mappings, to appear inNonlinear Analysis, Theory, Methods, and Applications 26 (1996) 1951–1964.

  15. A.B. Levy and R.T. Rockafellar, Proto-derivatives and the geometry of solution mappings in nonlinear programming, to appear inProceedings of Erice-Conference (1995).

  16. J.-S. Pang, A degree-theoretic approach to parametric nonsmooth equations with multivalued perturbed solution sets,Mathematical Programming 62 (1993) 359–383.

    Article  MathSciNet  Google Scholar 

  17. J.-S. Pang, Necessary and sufficient conditions for solution stability of parametric nonsmooth equations, in: D.-Z. Du, L. Qi and R.S. Womersley, eds.,Recent Advances in Nonsmooth Optimization (World Scientific, Singapore, 1995) pp. 261–288.

    Google Scholar 

  18. M.S. Gowda and J.-S. Pang, Stability analysis of variational inequalities and nonlinear complementarity problems, via the mixed linear complementarity problem and degree theory,Mathematics of Operations Research 19 (1994) 831–879.

    MATH  MathSciNet  Google Scholar 

  19. Y. Qiu and T.L. Magnanti, Sensitivity analysis for variational inequalities,Mathematics of Operations Research 17 (1992) 61–76.

    Article  MATH  MathSciNet  Google Scholar 

  20. S.M. Robinson, Generalized equations and their solutions, Part I: Basic theory,Mathematical Programming Study 10 (1979) 128–141.

    MATH  Google Scholar 

  21. S.M. Robinson, Strongly regular generalized equations,Mathematics of Operations Research 5 (1980) 43–62.

    MATH  MathSciNet  Google Scholar 

  22. S.M. Robinson, Generalized equations and their solutions. Part II: Applications to nonlinear programming,Mathematical Programming Study 19 (1982) 200–221.

    MATH  Google Scholar 

  23. S.M. Robinson, Local structure of feasible sets in nonlinear programming, Part III: Stability and sensitivity,Mathematical Programming Study 30 (1987) 45–66.

    MATH  Google Scholar 

  24. S.M. Robinson, An implicit-function theorem for a class of nonsmooth functions,Mathematics of Operations Research 16 (1991) 292–309.

    MATH  MathSciNet  Google Scholar 

  25. R.T. Rockafellar, Proto-differentiability of set-valued mappings and its applications in optimization, in: H. Attouch, J.P. Aubin, F.H. Clarke and I. Ekeland, eds.,Analyse Non Linéaire (Gauthier-Villars, Paris, 1989) pp. 449–482.

    Google Scholar 

  26. R.T. Rockafellar, Nonsmooth analysis and parametric optimization, in: A. Cellina, ed.,Methods of Nonconvex Analysis, Lecture Notes in Mathematics, Vol. 1446 (Springer, Berlin, 1990) pp. 137–151.

    Chapter  Google Scholar 

  27. A. Shapiro, Sensitivity analysis of nonlinear programs and differentiability properties of metric projections,SIAM Journal on Control and Optimization 26 (1988) 628–645.

    Article  MATH  MathSciNet  Google Scholar 

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Levy, A.B. Implicit multifunction theorems for the sensitivity analysis of variational conditions. Mathematical Programming 74, 333–350 (1996). https://doi.org/10.1007/BF02592203

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