iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/BF01874449
Modeling and solution strategies for unconstrained stochastic optimization problems | Annals of Operations Research Skip to main content
Springer Nature Link
Log in

Modeling and solution strategies for unconstrained stochastic optimization problems

  • Stochastic Programming
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

We review some modeling alternatives for handling risk in decision-making processes for unconstrained stochastic optimization problems. Solution strategies are discussed and compared.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. R.T. Rockafellar, An extension of Fenchel's duality theorem for convex functions, Duke Math. J. 33 (1966) 81.

    Google Scholar 

  2. P. Billingsley, Convergence of Probability Measures (Wiley, New York, 1975).

    Google Scholar 

  3. G. Salinetti and R. Wets, On the relation between two types of convergence for convex functions. J. Math. Anal. Appl. 60 (1977) 211.

    Google Scholar 

  4. R. Wets, Convergence of convex functions, variational inequalities and convex optimization problems, in: Variational Inequalities and Complementarity Problems, ed. R. Cottle, F. Gianessi and J-L. Lions (Wiley, New York, 1980) p. 375.

    Google Scholar 

  5. K. Marti. Computation of descent direction in stochastic optimization problems with invariant distributions, Tech. Rep. Hochschule Bundeswehr München, 1982.

  6. R. Wets, Stochastic programming: solution techniques and approximation schemes, in: Mathematical Programming: The State-of-the-Art 1982 (Springer-Verlag, Berlin, 1983) p. 566.

    Google Scholar 

  7. A. Marshall and I. Olkin, Inequalities: Theory of Majorization and Its Applications (Academic Press, New York, 1979).

    Google Scholar 

  8. Y. Ermoliev, Stochastic quasigradient methods and their applications to systems optimization, Stochastics (1983).

  9. H. Kushner, Stochastic approximation algorithms for constrained optimization problems, Annals of Statistics 2 (1974)

  10. B. Poljak, Nonlinear programming methods in the presence of noise, Math. Progr. 14 (1978) 87.

    Google Scholar 

  11. R.T. Rockafellar, Integrals that are convex functionals, II, Pacific J. Math. 39 (1971) 439.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Kentucky, Lexington, KY, USA

    Roger J -B. Wets

  2. IIASA, A-2361, Laxenburg, Austria

    Roger J -B. Wets

Authors
  1. Roger J -B. Wets
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Invited lecture at the International Institute on Stochastics and Optimization, Gargnano, Italy, September 1–10, 1982.

Supported in part by a Guggenheim Fellowship and a grant of the National Science Foundation.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wets, R.J.B. Modeling and solution strategies for unconstrained stochastic optimization problems. Ann Oper Res 1, 3–22 (1984). https://doi.org/10.1007/BF01874449

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01874449

Keywords and phrases

Search

Navigation