IDEAS home Printed from https://ideas.repec.org/p/wop/iasawp/wp95015.html
   My bibliography  Save this paper

Constraint Aggregation Principle in Convex Optimization

Author

Listed:
  • Y.M. Ermoliev
  • A.V. Kryazhimskii
  • A. Ruszczynski

Abstract

A general constraint aggregation technique is proposed for convex optimization problems. At each iteration a set of convex inequalities and linear equations is replaced by a single inequality formed as a linear combination of the original constraints. After solving the simplified subproblem, new aggregation coefficients are calculated and the iteration continues. This general aggregation principle is incorporated into a number of specific algorithms. Convergence of the new methods is proved and speed of convergence analyzed. It is shown that in case of linear programming, the method with aggregation has a polynomial complexity. Finally, application to decomposable problems is discussed.

Suggested Citation

  • Y.M. Ermoliev & A.V. Kryazhimskii & A. Ruszczynski, 1995. "Constraint Aggregation Principle in Convex Optimization," Working Papers wp95015, International Institute for Applied Systems Analysis.
  • Handle: RePEc:wop:iasawp:wp95015
    as

    Download full text from publisher

    File URL: http://www.iiasa.ac.at/Publications/Documents/WP-95-015.ps
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fred Glover, 1975. "Surrogate Constraint Duality in Mathematical Programming," Operations Research, INFORMS, vol. 23(3), pages 434-451, June.
    2. Andrzej Ruszczyński, 1987. "A Linearization Method for Nonsmooth Stochastic Programming Problems," Mathematics of Operations Research, INFORMS, vol. 12(1), pages 32-49, February.
    3. David F. Rogers & Robert D. Plante & Richard T. Wong & James R. Evans, 1991. "Aggregation and Disaggregation Techniques and Methodology in Optimization," Operations Research, INFORMS, vol. 39(4), pages 553-582, August.
    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. Y.M. Ermoliev & A. Ruszczynski, 1995. "Convex Optimization by Radial Search," Working Papers wp95036, International Institute for Applied Systems Analysis.
    2. M. K. H. Fan & Y. Gong, 1999. "Exterior Minimum-Penalty Path-Following Methods in Semidefinite Programming," Journal of Optimization Theory and Applications, Springer, vol. 100(2), pages 327-348, February.
    3. A.V. Kryazhimskii & V.I. Maksimov & Yu.S. Osipov, 1996. "Reconstruction of Boundary Sources through Sensor Observations," Working Papers wp96097, International Institute for Applied Systems Analysis.
    4. B.V. Digas & Y.M. Ermoliev & A.V. Kryazhimskii, 1998. "Guaranteed Optimization in Insurance of Catastrophic Risks," Working Papers ir98082, International Institute for Applied Systems Analysis.
    5. A.V. Kryazhimskii & A. Ruszczynski, 1997. "Constraint Aggregation in Infinite-Dimensional Spaces and Applications," Working Papers ir97051, International Institute for Applied Systems Analysis.
    6. R. Rozycki, 1995. "Constraint Aggregation Principle: Application to a Dual Transportation Problem," Working Papers wp95103, International Institute for Applied Systems Analysis.
    7. M. Davidson, 1996. "Proximal Point Mappings and Constraint Aggregation Principle," Working Papers wp96102, International Institute for Applied Systems Analysis.

    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. Alidaee, Bahram, 2014. "Zero duality gap in surrogate constraint optimization: A concise review of models," European Journal of Operational Research, Elsevier, vol. 232(2), pages 241-248.
    2. Richard Connors & David Watling, 2015. "Assessing the Demand Vulnerability of Equilibrium Traffic Networks via Network Aggregation," Networks and Spatial Economics, Springer, vol. 15(2), pages 367-395, June.
    3. Merrick, James H. & Weyant, John P., 2019. "On choosing the resolution of normative models," European Journal of Operational Research, Elsevier, vol. 279(2), pages 511-523.
    4. Wilhelm, Wilbert E. & Xu, Kaihong, 2002. "Prescribing product upgrades, prices and production levels over time in a stochastic environment," European Journal of Operational Research, Elsevier, vol. 138(3), pages 601-621, May.
    5. John N. Hooker, 2002. "Logic, Optimization, and Constraint Programming," INFORMS Journal on Computing, INFORMS, vol. 14(4), pages 295-321, November.
    6. Vicens, E. & Alemany, M. E. & Andres, C. & Guarch, J. J., 2001. "A design and application methodology for hierarchical production planning decision support systems in an enterprise integration context," International Journal of Production Economics, Elsevier, vol. 74(1-3), pages 5-20, December.
    7. Y.M. Ermoliev & A. Ruszczynski, 1995. "Convex Optimization by Radial Search," Working Papers wp95036, International Institute for Applied Systems Analysis.
    8. James H. Merrick & John E. T. Bistline & Geoffrey J. Blanford, 2021. "On representation of energy storage in electricity planning models," Papers 2105.03707, arXiv.org, revised May 2021.
    9. Renaud Chicoisne, 2023. "Computational aspects of column generation for nonlinear and conic optimization: classical and linearized schemes," Computational Optimization and Applications, Springer, vol. 84(3), pages 789-831, April.
    10. Kenneth Carling & Mengjie Han & Johan Håkansson, 2012. "Does Euclidean distance work well when the p-median model is applied in rural areas?," Annals of Operations Research, Springer, vol. 201(1), pages 83-97, December.
    11. Srinivasa, Anand V. & Wilhelm, Wilbert E., 1997. "A procedure for optimizing tactical response in oil spill clean up operations," European Journal of Operational Research, Elsevier, vol. 102(3), pages 554-574, November.
    12. Hadi Bidhandi, 2006. "A new approach based on the surrogating method in the project time compression problems," Annals of Operations Research, Springer, vol. 143(1), pages 237-250, March.
    13. 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.
    14. Goerigk, Marc & Deghdak, Kaouthar & Heßler, Philipp, 2014. "A comprehensive evacuation planning model and genetic solution algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 82-97.
    15. Li, Han-Lin & Fang, Shu-Cherng & Huang, Yao-Huei & Nie, Tiantian, 2016. "An enhanced logarithmic method for signomial programming with discrete variables," European Journal of Operational Research, Elsevier, vol. 255(3), pages 922-934.
    16. M. A. Venkataramana & John J. Dinkel & John Mote, 1991. "Vector processing approach to constrained network problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 38(1), pages 71-85, February.
    17. R.L. Francis & T.J. Lowe & M.B. Rayco & A. Tamir, 2003. "Exploiting self‐canceling demand point aggregation error for some planar rectilinear median location problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(6), pages 614-637, September.
    18. Kim, Kwang Jae & Moskowitz, Herbert & Koksalan, Murat, 1996. "Fuzzy versus statistical linear regression," European Journal of Operational Research, Elsevier, vol. 92(2), pages 417-434, July.
    19. Murwan Siddig & Yongjia Song, 2022. "Adaptive partition-based SDDP algorithms for multistage stochastic linear programming with fixed recourse," Computational Optimization and Applications, Springer, vol. 81(1), pages 201-250, January.
    20. Beltran-Royo, C., 2017. "Two-stage stochastic mixed-integer linear programming: The conditional scenario approach," Omega, Elsevier, vol. 70(C), pages 31-42.

    More about this item

    Statistics

    Access and download statistics

    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:wop:iasawp:wp95015. 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: Thomas Krichel (email available below). General contact details of provider: https://edirc.repec.org/data/iiasaat.html .

    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.