IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v51y2012i2p729-747.html
   My bibliography  Save this article

Heuristics for convex mixed integer nonlinear programs

Author

Listed:
  • Pierre Bonami
  • João Gonçalves

Abstract

No abstract is available for this item.

Suggested Citation

  • Pierre Bonami & João Gonçalves, 2012. "Heuristics for convex mixed integer nonlinear programs," Computational Optimization and Applications, Springer, vol. 51(2), pages 729-747, March.
    • Handle: RePEc:spr:coopap:v:51:y:2012:i:2:p:729-747
    DOI: 10.1007/s10589-010-9350-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10589-010-9350-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10589-010-9350-6?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. Kumar Abhishek & Sven Leyffer & Jeff Linderoth, 2010. "FilMINT: An Outer Approximation-Based Solver for Convex Mixed-Integer Nonlinear Programs," INFORMS Journal on Computing, INFORMS, vol. 22(4), pages 555-567, November.
    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. Christoph Neumann & Oliver Stein & Nathan Sudermann-Merx, 2019. "A feasible rounding approach for mixed-integer optimization problems," Computational Optimization and Applications, Springer, vol. 72(2), pages 309-337, March.
    2. Meenarli Sharma & Prashant Palkar & Ashutosh Mahajan, 2022. "Linearization and parallelization schemes for convex mixed-integer nonlinear optimization," Computational Optimization and Applications, Springer, vol. 81(2), pages 423-478, March.
    3. Justin A. Sirignano & Gerry Tsoukalas & Kay Giesecke, 2016. "Large-Scale Loan Portfolio Selection," Operations Research, INFORMS, vol. 64(6), pages 1239-1255, December.
    4. Francisco Trespalacios & Ignacio E. Grossmann, 2016. "Cutting Plane Algorithm for Convex Generalized Disjunctive Programs," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 209-222, May.
    5. Kuo-Ling Huang & Sanjay Mehrotra, 2015. "An empirical evaluation of a walk-relax-round heuristic for mixed integer convex programs," Computational Optimization and Applications, Springer, vol. 60(3), pages 559-585, April.
    6. Chunyi Wang & Fengzhang Luo & Zheng Jiao & Xiaolei Zhang & Zhipeng Lu & Yanshuo Wang & Ren Zhao & Yang Yang, 2022. "An Enhanced Second-Order Cone Programming-Based Evaluation Method on Maximum Hosting Capacity of Solar Energy in Distribution Systems with Integrated Energy," Energies, MDPI, vol. 15(23), pages 1-19, November.
    7. Timo Berthold & Andrea Lodi & Domenico Salvagnin, 2019. "Ten years of feasibility pump, and counting," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 7(1), pages 1-14, March.
    8. Saïd Hanafi & Raca Todosijević, 2017. "Mathematical programming based heuristics for the 0–1 MIP: a survey," Journal of Heuristics, Springer, vol. 23(4), pages 165-206, August.
    9. Timo Berthold, 2018. "A computational study of primal heuristics inside an MI(NL)P solver," Journal of Global Optimization, Springer, vol. 70(1), pages 189-206, January.
    10. Christoph Neumann & Oliver Stein & Nathan Sudermann-Merx, 2020. "Granularity in Nonlinear Mixed-Integer Optimization," Journal of Optimization Theory and Applications, Springer, vol. 184(2), pages 433-465, February.
    11. Sonia Cafieri & Claudia D’Ambrosio, 2018. "Feasibility pump for aircraft deconfliction with speed regulation," Journal of Global Optimization, Springer, vol. 71(3), pages 501-515, July.
    12. Andrew Allman & Qi Zhang, 2021. "Branch-and-price for a class of nonconvex mixed-integer nonlinear programs," Journal of Global Optimization, Springer, vol. 81(4), pages 861-880, December.
    13. Antonio Frangioni & Fabio Furini & Claudio Gentile, 2016. "Approximated perspective relaxations: a project and lift approach," Computational Optimization and Applications, Springer, vol. 63(3), pages 705-735, April.
    14. M. Li & L. Vicente, 2013. "Inexact solution of NLP subproblems in MINLP," Journal of Global Optimization, Springer, vol. 55(4), pages 877-899, April.

    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. Pia Domschke & Bjorn Geißler & Oliver Kolb & Jens Lang & Alexander Martin & Antonio Morsi, 2011. "Combination of Nonlinear and Linear Optimization of Transient Gas Networks," INFORMS Journal on Computing, INFORMS, vol. 23(4), pages 605-617, November.
    2. Andrés Gómez & Oleg A. Prokopyev, 2021. "A Mixed-Integer Fractional Optimization Approach to Best Subset Selection," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 551-565, May.
    3. Fatima Bellahcene, 2019. "Application of the polyblock method to special integer chance constrained problem," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 29(4), pages 23-40.
    4. David E. Bernal & Zedong Peng & Jan Kronqvist & Ignacio E. Grossmann, 2022. "Alternative regularizations for Outer-Approximation algorithms for convex MINLP," Journal of Global Optimization, Springer, vol. 84(4), pages 807-842, December.
    5. Andreas Lundell & Jan Kronqvist & Tapio Westerlund, 2022. "The supporting hyperplane optimization toolkit for convex MINLP," Journal of Global Optimization, Springer, vol. 84(1), pages 1-41, September.
    6. Mohammad R. Oskoorouchi & Hamid R. Ghaffari & Tamás Terlaky & Dionne M. Aleman, 2011. "An Interior Point Constraint Generation Algorithm for Semi-Infinite Optimization with Health-Care Application," Operations Research, INFORMS, vol. 59(5), pages 1184-1197, October.
    7. Meenarli Sharma & Prashant Palkar & Ashutosh Mahajan, 2022. "Linearization and parallelization schemes for convex mixed-integer nonlinear optimization," Computational Optimization and Applications, Springer, vol. 81(2), pages 423-478, March.
    8. Pietro Belotti, 2013. "Bound reduction using pairs of linear inequalities," Journal of Global Optimization, Springer, vol. 56(3), pages 787-819, July.
    9. Francisco Trespalacios & Ignacio E. Grossmann, 2016. "Cutting Plane Algorithm for Convex Generalized Disjunctive Programs," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 209-222, May.
    10. Francisco Trespalacios & Ignacio E. Grossmann, 2015. "Algorithmic Approach for Improved Mixed-Integer Reformulations of Convex Generalized Disjunctive Programs," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 59-74, February.
    11. Hassan Hijazi & Pierre Bonami & Adam Ouorou, 2014. "An Outer-Inner Approximation for Separable Mixed-Integer Nonlinear Programs," INFORMS Journal on Computing, INFORMS, vol. 26(1), pages 31-44, February.
    12. Vaidas Jusevičius & Remigijus Paulavičius, 2021. "Web-Based Tool for Algebraic Modeling and Mathematical Optimization," Mathematics, MDPI, vol. 9(21), pages 1-18, October.
    13. Mahdi Hamzeei & James Luedtke, 2014. "Linearization-based algorithms for mixed-integer nonlinear programs with convex continuous relaxation," Journal of Global Optimization, Springer, vol. 59(2), pages 343-365, July.
    14. Branislav Micieta & Jolanta Staszewska & Matej Kovalsky & Martin Krajcovic & Vladimira Binasova & Ladislav Papanek & Ivan Antoniuk, 2021. "Innovative System for Scheduling Production Using a Combination of Parametric Simulation Models," Sustainability, MDPI, vol. 13(17), pages 1-20, August.
    15. Fukasawa, Ricardo & He, Qie & Song, Yongjia, 2016. "A disjunctive convex programming approach to the pollution-routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 61-79.
    16. Terry-Leigh Oliphant & M. Montaz Ali, 2018. "A trajectory-based method for mixed integer nonlinear programming problems," Journal of Global Optimization, Springer, vol. 70(3), pages 601-623, March.
    17. Lu, Jie & Gupte, Akshay & Huang, Yongxi, 2018. "A mean-risk mixed integer nonlinear program for transportation network protection," European Journal of Operational Research, Elsevier, vol. 265(1), pages 277-289.
    18. Toriello, Alejandro & Vielma, Juan Pablo, 2012. "Fitting piecewise linear continuous functions," European Journal of Operational Research, Elsevier, vol. 219(1), pages 86-95.

    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:2:p:729-747. 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.