IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v51y2012i1p77-123.html
   My bibliography  Save this article

Recourse-based stochastic nonlinear programming: properties and Benders-SQP algorithms

Author

Listed:
  • Ankur Kulkarni
  • Uday Shanbhag

Abstract

No abstract is available for this item.

Suggested Citation

  • Ankur Kulkarni & Uday Shanbhag, 2012. "Recourse-based stochastic nonlinear programming: properties and Benders-SQP algorithms," Computational Optimization and Applications, Springer, vol. 51(1), pages 77-123, January.
    • Handle: RePEc:spr:coopap:v:51:y:2012:i:1:p:77-123
    DOI: 10.1007/s10589-010-9316-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10589-010-9316-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10589-010-9316-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    2. Arjan Berkelaar & Cees Dert & Bart Oldenkamp & Shuzhong Zhang, 2002. "A Primal-Dual Decomposition-Based Interior Point Approach to Two-Stage Stochastic Linear Programming," Operations Research, INFORMS, vol. 50(5), pages 904-915, October.
    3. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    4. Blomvall, Jorgen & Lindberg, Per Olov, 2002. "A Riccati-based primal interior point solver for multistage stochastic programming," European Journal of Operational Research, Elsevier, vol. 143(2), pages 452-461, December.
    5. Stephen M. Robinson, 1996. "Analysis of Sample-Path Optimization," Mathematics of Operations Research, INFORMS, vol. 21(3), pages 513-528, August.
    6. Jeff Linderoth & Alexander Shapiro & Stephen Wright, 2006. "The empirical behavior of sampling methods for stochastic programming," Annals of Operations Research, Springer, vol. 142(1), pages 215-241, February.
    7. Xiaojun Chen & Masao Fukushima, 2005. "Expected Residual Minimization Method for Stochastic Linear Complementarity Problems," Mathematics of Operations Research, INFORMS, vol. 30(4), pages 1022-1038, November.
    8. Rockafellar, R. T. & Wets, R. J. -B., 1975. "Stochastic convex programming: Kuhn-Tucker conditions," Journal of Mathematical Economics, Elsevier, vol. 2(3), pages 349-370, December.
    9. Zhang, S., 2002. "An interior-point and decomposition approach to multiple stage stochastic programming," Econometric Institute Research Papers EI 2002-35, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. John R. Birge, 1985. "Decomposition and Partitioning Methods for Multistage Stochastic Linear Programs," Operations Research, INFORMS, vol. 33(5), pages 989-1007, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jinlong Lei & Uday V. Shanbhag & Jong-Shi Pang & Suvrajeet Sen, 2020. "On Synchronous, Asynchronous, and Randomized Best-Response Schemes for Stochastic Nash Games," Mathematics of Operations Research, INFORMS, vol. 45(1), pages 157-190, February.
    2. Sebastián Arpón & Tito Homem-de-Mello & Bernardo K. Pagnoncelli, 2020. "An ADMM algorithm for two-stage stochastic programming problems," Annals of Operations Research, Springer, vol. 286(1), pages 559-582, March.
    3. Chen, Wenyi & Kucukyazici, Beste & Verter, Vedat & Jesús Sáenz, María, 2015. "Supply chain design for unlocking the value of remanufacturing under uncertainty," European Journal of Operational Research, Elsevier, vol. 247(3), pages 804-819.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jie Sun & Xinwei Liu, 2006. "Scenario Formulation of Stochastic Linear Programs and the Homogeneous Self-Dual Interior-Point Method," INFORMS Journal on Computing, INFORMS, vol. 18(4), pages 444-454, November.
    2. Castro, Jordi & Escudero, Laureano F. & Monge, Juan F., 2023. "On solving large-scale multistage stochastic optimization problems with a new specialized interior-point approach," European Journal of Operational Research, Elsevier, vol. 310(1), pages 268-285.
    3. Unai Aldasoro & Laureano Escudero & María Merino & Juan Monge & Gloria Pérez, 2015. "On parallelization of a stochastic dynamic programming algorithm for solving large-scale mixed 0–1 problems under uncertainty," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 703-742, October.
    4. Panos Parpas & Berç Rustem, 2007. "Computational Assessment of Nested Benders and Augmented Lagrangian Decomposition for Mean-Variance Multistage Stochastic Problems," INFORMS Journal on Computing, INFORMS, vol. 19(2), pages 239-247, May.
    5. Sebastián Arpón & Tito Homem-de-Mello & Bernardo K. Pagnoncelli, 2020. "An ADMM algorithm for two-stage stochastic programming problems," Annals of Operations Research, Springer, vol. 286(1), pages 559-582, March.
    6. X. W. Liu & M. Fukushima, 2006. "Parallelizable Preprocessing Method for Multistage Stochastic Programming Problems," Journal of Optimization Theory and Applications, Springer, vol. 131(3), pages 327-346, December.
    7. Michael Freimer & Jeffrey Linderoth & Douglas Thomas, 2012. "The impact of sampling methods on bias and variance in stochastic linear programs," Computational Optimization and Applications, Springer, vol. 51(1), pages 51-75, January.
    8. Teemu Pennanen & Markku Kallio, 2006. "A splitting method for stochastic programs," Annals of Operations Research, Springer, vol. 142(1), pages 259-268, February.
    9. Ketabchi, Saeed & Behboodi-Kahoo, Malihe, 2015. "Augmented Lagrangian method within L-shaped method for stochastic linear programs," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 12-20.
    10. V.I. Norkin & G.C. Pflug & A. Ruszczynski, 1996. "A Branch and Bound Method for Stochastic Global Optimization," Working Papers wp96065, International Institute for Applied Systems Analysis.
    11. Jie Jiang & Shengjie Li, 2021. "Regularized Sample Average Approximation Approach for Two-Stage Stochastic Variational Inequalities," Journal of Optimization Theory and Applications, Springer, vol. 190(2), pages 650-671, August.
    12. Gyana R. Parija & Shabbir Ahmed & Alan J. King, 2004. "On Bridging the Gap Between Stochastic Integer Programming and MIP Solver Technologies," INFORMS Journal on Computing, INFORMS, vol. 16(1), pages 73-83, February.
    13. Fei, Xin & Gülpınar, Nalân & Branke, Jürgen, 2019. "Efficient solution selection for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 277(3), pages 918-929.
    14. Hannes Schwarz & Valentin Bertsch & Wolf Fichtner, 2018. "Two-stage stochastic, large-scale optimization of a decentralized energy system: a case study focusing on solar PV, heat pumps and storage in a residential quarter," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 265-310, January.
    15. Raymond K.-M. Cheung & Warren B. Powell, 2000. "Shape -- A Stochastic Hybrid Approximation Procedure for Two-Stage Stochastic Programs," Operations Research, INFORMS, vol. 48(1), pages 73-79, February.
    16. Zhou, Feng & Huang, Gordon H. & Chen, Guo-Xian & Guo, Huai-Cheng, 2009. "Enhanced-interval linear programming," European Journal of Operational Research, Elsevier, vol. 199(2), pages 323-333, December.
    17. Marco Colombo & Andreas Grothey, 2013. "A decomposition-based crash-start for stochastic programming," Computational Optimization and Applications, Springer, vol. 55(2), pages 311-340, June.
    18. A. Ruszczynski, 1994. "On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs," Working Papers wp94005, International Institute for Applied Systems Analysis.
    19. Entriken, Robert & Infanger, Gerd, 1990. "5.2 Decomposition and importance sampling for stochastic linear models," Energy, Elsevier, vol. 15(7), pages 645-659.
    20. Julia Higle & Suvrajeet Sen, 2006. "Multistage stochastic convex programs: Duality and its implications," Annals of Operations Research, Springer, vol. 142(1), pages 129-146, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:coopap:v:51:y:2012:i:1:p:77-123. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.