Strong convexity in risk-averse stochastic programs with complete recourse | Computational Management Science Skip to main content
Log in

Strong convexity in risk-averse stochastic programs with complete recourse

  • Original Paper
  • Published:
Computational Management Science Aims and scope Submit manuscript

Abstract

We give sufficient conditions for the expected excess and the mean-upper-semideviation of recourse functions to be strongly convex. This is done in the setting of two-stage stochastic programs with complete linear recourse and random right-hand side. This work extends results on strong convexity of risk-neutral models.

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

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Attouch H, Wets RJ-B (1993) Quantitative stability of variational systems II. A framework for nonlinear conditioning. SIAM J Optim 3:359–381

    Article  Google Scholar 

  • Azagra D (2013) Global and fine approximation of convex functions. Proc Lond Math Soc 107:799–824

    Article  Google Scholar 

  • Bauschke H, Combettes P (2011) Convex analysis and monotone operator theory in Hilbert spaces. Springer, New York

    Book  Google Scholar 

  • Beale EML (1955) On minimizing a convex function subject to linear inequalities. J R Stat Soc Ser B 17:173–184

    Google Scholar 

  • Birge JR, Louveaux F (2011) Introduction to stochastic programming, 2nd edn. Springer, New York

    Book  Google Scholar 

  • Borwein JM, Vanderwerff JD (2010) Convex functions—constructions, characterizations, and counterexamples. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Dantzig GB (1955) Linear programming under uncertainty. Manag Sci 1:197–206

    Article  Google Scholar 

  • Poljak B (1966) Existence theorems and convergence of minimizing sequences in extremum problems with restrictions. Dokl Akad Nauk SSSR 166:287–290; Soviet Math Dokl 7:72–75

  • Rachev ST (1991) Probability metrics and the stability of stochastic models. Wiley, Chichester

    Google Scholar 

  • Rockafellar RT (1968) Integrals which are convex functionals. Pac J Math 24(3):525–539

    Article  Google Scholar 

  • Römisch W, Schultz R (1991) Stability analysis for stochastic programs. Ann Oper Res 30:241–266

    Article  Google Scholar 

  • Römisch W, Schultz R (1996) Lipschitz stability for stochastic programs with complete recourse. SIAM J Optim 6(2):531–547

    Article  Google Scholar 

  • Schultz R (1994) Strong concexity in stochastic programs with complete recourse. J Comput Appl Math 56:3–22

    Article  Google Scholar 

  • Shapiro A, Dentcheva D, Ruszczyński A (2014) Lectures on stochastic programming—modeling and theory, 2nd edn. MOS-SIAM, Philadelphia

    Google Scholar 

  • Walkup DW, Wets RJ-B (1969) Lifting projections of convex polyhedra. Pac J Math 28:465–475

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Deutsche Forschungsgemeinschaft for supporting the first and second author via the Collaborative Research Center TRR 154.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Matthias Claus.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Claus, M., Schultz, R. & Spürkel, K. Strong convexity in risk-averse stochastic programs with complete recourse. Comput Manag Sci 15, 411–429 (2018). https://doi.org/10.1007/s10287-018-0331-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10287-018-0331-z

Keywords

Mathematics Subject Classification

Navigation