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Exact barrier function methods for Lipschitz programs

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Abstract

The purpose of this paper is twofold. First we consider a class of nondifferentiable penalty functions for constrained Lipschitz programs and then we show how these penalty functions can be employed to solve a constrained Lipschitz program. The penalty functions considered incorporate a barrier term which makes their value go to infinity on the boundary of a perturbation of the feasible set. Exploiting this fact it is possible to prove, under mild compactness and regularity assumptions, a complete correspondence between the unconstrained minimization of the penalty functions and the solution of the constrained program, thus showing that the penalty functions are exact according to the definition introduced in [17]. Motivated by these results, we propose some algorithm models and study their convergence properties. We show that, even when the assumptions used to establish the exactness of the penalty functions are not satisfied, every limit point of the sequence produced by a basic algorithm model is an extended stationary point according to the definition given in [8]. Then, based on this analysis and on the one previously carried out on the penalty functions, we study the consequence on the convergence properties of increasingly demanding assumptions. In particular we show that under the same assumptions used to establish the exactness properties of the penalty functions, it is possible to guarantee that a limit point at least exists, and that any such limit point is a KKT point for the constrained problem.

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References

  1. M. S. Bazaraa and J. J. Goode. Sufficient conditions for a globally exact penalty function without convexity. Mathematical Programming Study, 19:1–15, 1982.

    Google Scholar 

  2. D. P. Bertsekas. Necessary and sufficient conditions for a penalty method to be exact. Mathematical Programming, 9:87–99, 1975.

    Google Scholar 

  3. D. P. Bertsekas. Constrained Optimization and Lagrange Multipliers Methods. Academic Press, New York, 1982.

    Google Scholar 

  4. J. M. Borwein. Stability and regular points of inequality systems. Journal of Optimization Theory and Applications, 48:9–52, 1986.

    Google Scholar 

  5. J. V. Burke. A sequential quadratic programming method for potentially infeasible mathematical programs. Journal of Mathematical Analysis and Applications, 139:319–351, 1989.

    Google Scholar 

  6. J. V. Burke, Calmness and exact penalization. SIAM Journal on Control and Optimization, 29:493–497, 1991.

    Google Scholar 

  7. J. V. Burke. An exact penalization viewpoint of constrained optimization. SIAM Journal on Control and Optimization, 29:968–998, 1991.

    Google Scholar 

  8. J. V. Burke. A robust trust region method for constrained nonlinear programming problems. SIAM Journal on Optimization, 2:325–347, 1992.

    Google Scholar 

  9. J. V. Burke and S.-P. Han. A robust sequential quadratic programming method. Mathematical Programming, 43:277–303, 1989.

    Google Scholar 

  10. C. Charalambous. A lower bound for the controlling parameters of the exact penalty functions. Mathematical Programming, 15:278–290, 1978.

    Google Scholar 

  11. C. Charalambous. On conditions for optimality of a class of nondifferentiable functions. Journal of Optimization Theory and Applications, 43:135–142, 1982.

    Google Scholar 

  12. F. H. Clarke. Optimization and Nonsmooth Analysis. Wiley, New York, 1983.

    Google Scholar 

  13. G. Di Pillo and F. Facchinei. Exact penalty functions for nondifferential programming problems. In F. H. Clarke, V. F. Demyanov, and F. Giannessi, editors, Nonsmooth Optimization and Related Topics, pp. 89–107, Plenum, New York, 1989.

    Google Scholar 

  14. G. Di Pillo and F. Facchinei. Regularity conditions and exact penalty functions in Lipschitz programming problems. In F. Giannessi, editor, Nonsmooth Optimization Methods and Applications, pp. 107–120. Gordon and Breach, New York, 1992.

    Google Scholar 

  15. G. Di Pillo and L. Grippo. An exact penalty method with global convergence properties for nonlinear programming problems. Mathematical Programming, 36:1–18, 1986.

    Google Scholar 

  16. G. Di Pillo and L. Grippo. On the exactness of a class of nondifferentiable penalty functions. Journal of Optimization Theory and Applications, 57:399–410, 1988.

    Google Scholar 

  17. G. Di Pillo and L. Grippo. Exact penalty functions in constrained optimization. SIAM Journal on Control and Optimization, 27:1333–1360, 1989.

    Google Scholar 

  18. G. Di Pillo, L. Grippo, and S. Lucidi. A smooth method for the minimax problem. Mathematical Programming, 60:187–214, 1993.

    Google Scholar 

  19. J. P. Evans, F. J. Gould, and J. W. Tolle. Exact penalty functions in nonlinear programming. Mathematical Programming, 4:72–97, 1973.

    Google Scholar 

  20. F. Facchinei. Exact penalty functions and Lagrange multipliers. Optimization, 22:579–606, 1991.

    Google Scholar 

  21. F. Facchinei. Refinements of necessary conditions for optimality in nonlinear programming. Journal of Optimization Theory and Applications, 73:65–74, 1992.

    Google Scholar 

  22. A. V. Fiacco and G. P. McCormick. Nonlinear Programming: Sequential Unconstrained Minimization Techniques. Wiley, New York, 1968.

    Google Scholar 

  23. R. Fletcher. Practical Methods of Optimization. Wiley, New York, 1987.

    Google Scholar 

  24. S. P. Han. A globally convergent method for nonlinear programming. Journal of Optimization Theory and Applications, 22:297–309, 1977.

    Google Scholar 

  25. S. P. Han and O. L. Mangasarian. Exact penalty functions in nonlinear programming. Mathematical Programming, 17:251–269, 1979.

    Google Scholar 

  26. J.-B. Hiriart-Urruty. Refinements of necessary optimality conditions in nondifferentiable programming, I. Applied Mathematics and Optimization, 5:63–82, 1979.

    Google Scholar 

  27. J.-B. Hiriart-Urruty. Refinements of necessary optimality conditions in nondifferentiable programming, II. Mathematical Programming Study, 19:120–139, 1982.

    Google Scholar 

  28. S. Howe. New conditions for the exactness of a simple penalty function. SIAM Journal on Control, 11:378–381, 1973.

    Google Scholar 

  29. A. D. Ioffe. Necessary and sufficient conditions for a local minimum. 1: A reduction theorem and first-order conditions. SIAM Journal on Control and Optimization, 17:245–250, 1979.

    Google Scholar 

  30. A. D. Ioffe. Necessary and sufficient conditions for a local minimum. 2: Conditions of Levitin-Miljutin-Osmolovskii type. SIAM Journal on Control and Optimization, 17:251–265, 1979.

    Google Scholar 

  31. A. D. Ioffe. Necessary and sufficient conditions for a local minimum. 3: Second-order conditions and augmented duality. SIAM Journal on Control and Optimization, 17:266–288, 1979.

    Google Scholar 

  32. V. H. Nguyen, J.-J. Strodiot, and R. Mifflin. On conditions to have bounded multipliers in locally Lipschitz programming. Mathematical Programming, 18:100–106, 1980.

    Google Scholar 

  33. T. Pietrzykowski. An exact potential method for constrained maxima. SIAM Journal on Numerical Analysis, 6:299–304, 1969.

    Google Scholar 

  34. T. Pietrzykowski. The potential method for conditional maxima in the locally compact metric spaces. Numerische Mathematik, 14:325–329, 1970.

    Google Scholar 

  35. E. Polak, D. Q. Mayne, and Y. Ward. On the extension of constrained optimization algorithms from differentiable to nondifferentiable problems. SIAM Journal on Control and Optimization, 21:179–203, 1983.

    Google Scholar 

  36. R. T. Rockafellar. Convex Analysis. Princeton University Press, Princeton, NJ, 1970.

    Google Scholar 

  37. E. Rosenberg. Exact penalty functions and stability in locally lipschitz programming. Mathematical Programming, 30:340–356, 1984.

    Google Scholar 

  38. J. Stoer. Principles of sequential quadratic programming methods for solving nonlinear programs. In K. Schittkowski, editor, Computational Mathematical Programming, NATO ASI Series, Vol. F15. Springer-Verlag, Berlin, 1985.

    Google Scholar 

  39. D. E. Ward. Exact penalties and sufficient conditions for optimality in nonsmooth optimization. Journal of Optimization Theory and Applications, 57:485–499, 1988.

    Google Scholar 

  40. D. E. Ward and J. M. Borwein. Nonsmooth calculus in finite dimensions. SIAM Journal on Control and Optimization, 25:1312–1340, 1987.

    Google Scholar 

  41. W. I. Zangwill. Non-linear programming via penalty functions. Management Science, 13:344–358, 1967.

    Google Scholar 

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Communicated by F. H. Clarke

This research has been partially supported by the National Research Program on “Metodi di Ottimizzazione per le Decisioni”, Ministero dell' Università e della Ricerca Scientifica e Tecnologica.

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Di Pillo, G., Facchinei, F. Exact barrier function methods for Lipschitz programs. Appl Math Optim 32, 1–31 (1995). https://doi.org/10.1007/BF01189901

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