Skip to main content
Log in

A lifting method for generalized semi-infinite programs based on lower level Wolfe duality

  • Published:
Computational Optimization and Applications Aims and scope Submit manuscript

Abstract

This paper introduces novel numerical solution strategies for generalized semi-infinite optimization problems (GSIP), a class of mathematical optimization problems which occur naturally in the context of design centering problems, robust optimization problems, and many fields of engineering science. GSIPs can be regarded as bilevel optimization problems, where a parametric lower-level maximization problem has to be solved in order to check feasibility of the upper level minimization problem. The current paper discusses several strategies to reformulate this class of problems into equivalent finite minimization problems by exploiting the concept of Wolfe duality for convex lower level problems. Here, the main contribution is the discussion of the non-degeneracy of the corresponding formulations under various assumptions. Finally, these non-degenerate reformulations of the original GSIP allow us to apply standard nonlinear optimization algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Ben-Tal, A., Nemirovski, A.: Robust convex optimization. Math. Oper. Res. 23, 769–805 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  2. Ben-Tal, A., El Ghaoui, L., Nemirovski, A.: Robust Optimization. Princeton University Press, Princeton (2009)

    MATH  Google Scholar 

  3. Bhattacharjee, B., Green, W.H., Barton, P.I.: Interval methods for semi-infinite programs. Comput. Optim. Appl. 30, 63–93 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bhattacharjee, B., Lemonidis, P., Green, W.H., Barton, P.I.: Global solution of semi-infinite programs. Math. Program. 103, 283–307 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  5. Dantzig, G.B., Thapa, M.N.: Linear Programming. Springer, Berlin (2003)

    MATH  Google Scholar 

  6. Flegel, M.L., Kanzow, C.: A Fritz John approach to first order optimality conditions for mathematical programs with equilibrium constraints. Optimization 52, 277–286 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  7. Floudas, C.A., Stein, O.: The adaptive convexification algorithm: a feasible point method for semi-infinite programming. SIAM J. Optim. 18, 1187–1208 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  8. Günzel, H., Jongen, H.Th., Stein, O.: On the closure of the feasible set in generalized semi-infinite programming. Cent. Eur. J. Oper. Res. 15, 271–280 (2007)

    Article  MATH  Google Scholar 

  9. Guerra-Vázquez, F., Jongen, H.Th., Shikhman, V.: General semi-infinite programming: symmetric Mangasarian-Fromovitz constraint qualification and the closure of the feasible set. SIAM J. Optim. 20, 2487–2503 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  10. Hettich, R., Jongen, H.Th.: Semi-infinite programming: conditions of optimality and applications. In: Stoer, J. (ed.) Optimization Techniques, Part 2. Lecture Notes in Control and Information Sciences, vol. 7, pp. 1–11. Springer, Berlin (1978)

    Google Scholar 

  11. Hettich, R., Kortanek, K.: Semi infinite programming: theory, methods, and applications. SIAM Rev. 35, 380–429 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hettich, R., Zencke, P.: Numerische Methoden der Approximation und Semi-Infiniten Optimierung. Teubner, Stuttgart (1982)

    MATH  Google Scholar 

  13. Jongen, H.Th., Rückmann, J.J., Stein, O.: Generalized semi-infinite optimization: a first order optimality condition and examples. Math. Program. 83, 145–158 (1998)

    MATH  Google Scholar 

  14. Kropat, E., Weber, G.W.: Robust regression analysis for gene-environment and eco-finance networks under polyhedral and ellipsoidal uncertainty. Preprint at IAM, METU

  15. Levitin, E., Tichatschke, R.: A branch-and-bound approach for solving a class of generalized semi-infinite programming problems. J. Glob. Optim. 13, 299–315 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  16. Mitsos, A., Lemonidis, P., Lee, C.K., Barton, P.I.: Relaxation-based bounds for semi-infinite programs. SIAM J. Optim. 19, 77–113 (2007)

    Article  MathSciNet  Google Scholar 

  17. Mitsos, A., Lemonidis, P., Barton, P.I.: Global solution of bilevel programs with a nonconvex inner program. J. Glob. Optim. 42, 475–513 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  18. Polak, E.: On the mathematical foundation of nondifferentiable optimization in engineering design. SIAM Rev. 29, 21–89 (1987)

    Article  MathSciNet  Google Scholar 

  19. Polak, E.: Optimization. Algorithms and Consistent Approximations. Springer, Berlin (1997)

    MATH  Google Scholar 

  20. Polak, E., Royset, J.O.: On the use of augmented Lagrangians in the solution of generalized semi-infinite min-max problems. Comput. Optim. Appl. 31, 173–192 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  21. Reemtsen, R., Görner, S.: Numerical methods for semi-infinite programming: a survey. In: Reemtsen, R., Rückmann, J.J. (eds.) Semi-Infinite Programming, pp. 195–275. Kluwer, Boston (1998)

    Google Scholar 

  22. Scheel, H., Scholtes, S.: Mathematical programs with complementarity constraints: stationarity, optimality, and sensitivity. Math. Oper. Res. 25, 1–22 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  23. Stein, O.: First order optimality conditions for degenerate index sets in generalized semi-infinite programming. Math. Oper. Res. 26, 565–582 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  24. Stein, O.: Bi-level Strategies in Semi-infinite Programming. Kluwer Academic, Boston (2003)

    MATH  Google Scholar 

  25. Stein, O.: How to solve a semi-infinite optimization problem. Eur. J. Oper. Res. (2012). doi:10.1016/j.ejor.2012.06.009

    Google Scholar 

  26. Stein, O., Steuermann, P.: The adaptive convexification algorithm for semi-infinite programming with arbitrary index sets. Math. Program. B (2012). doi:10.1007/s10107-012-0556-5

    MathSciNet  Google Scholar 

  27. Stein, O., Still, G.: Solving semi-infinite optimization problems with interior point techniques. SIAM J. Control Optim. 42, 769–788 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  28. Stein, O., Winterfeld, A.: A feasible method for generalized semi-infinite programming. J. Optim. Theory Appl. 146, 419–443 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  29. Still, G.: Discretization in semi-infinite programming: the rate of convergence. Math. Program. 91, 53–69 (2001)

    MathSciNet  MATH  Google Scholar 

  30. Still, G.: Generalized semi-infinite programming: numerical aspects. Optimization 49, 223–242 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  31. Weber, G.W., Tezel, A.: On generalized semi-infinite optimization of genetic networks. Top 15, 65–77 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  32. Yakubovich, V.A.: S-procedure in nonlinear control theory. Vestn. Leningr. Univ. 4, 73–93 (1977)

    Google Scholar 

Download references

Acknowledgements

We thank two anonymous referees and the associated editor for their precise and substantial remarks which significantly improved this manuscript.

The research was supported by the Research Council KUL via GOA/11/05 Ambiorics, GOA/10/09 MaNet, CoE EF/05/006 Optimization in Engineering (OPTEC), IOF-SCORES4CHEM and PhD/postdoc/fellow grants, the Flemish Government via FWO (PhD/postdoc grants, projects G0226.06, G0321.06, G.0302.07, G.0320.08, G.0558.08, G.0557.08, G.0588.09, G.0377.09, research communities ICCoS, ANMMM, MLDM) and via IWT (PhD Grants, Eureka-Flite+, SBO LeCoPro, SBO Climaqs, SBO POM, O&O-Dsquare), the Belgian Federal Science Policy Office: IUAP P6/04 (DYSCO, Dynamical systems, control and optimization, 2007–2011), the IBBT, the EU (ERNSI; FP7-HD-MPC (INFSO-ICT-223854), COST intelliCIS, FP7-EMBOCON (ICT-248940), FP7-SADCO (MC ITN-264735), ERC HIGHWIND (259 166)), the Contract Research (AMINAL), the Helmholtz Gemeinschaft via viCERP and the ACCM.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to O. Stein.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Diehl, M., Houska, B., Stein, O. et al. A lifting method for generalized semi-infinite programs based on lower level Wolfe duality. Comput Optim Appl 54, 189–210 (2013). https://doi.org/10.1007/s10589-012-9489-4

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10589-012-9489-4

Keywords

Navigation