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A new technique for inconsistent QP problems in the SQP method

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  • P. Spellucci

Abstract

Successful treatment of inconsistent QP problems is of major importance in the SQP method, since such occur quite often even for well behaved nonlinear programming problems. This paper presents a new technique for regularizing inconsistent QP problems, which compromises in its properties between the simple technique of Pantoja and Mayne [36] and the highly successful, but expensive one of Tone [47]. Global convergence of a corresponding algorithm is shown under reasonable weak conditions. Numerical results are reported which show that this technique, combined with a special method for the case of regular subproblems, is quite competitive to highly appreciated established ones. Copyright Physica-Verlag 1998

Suggested Citation

  • P. Spellucci, 1998. "A new technique for inconsistent QP problems in the SQP method," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 47(3), pages 355-400, October.
    • Handle: RePEc:spr:mathme:v:47:y:1998:i:3:p:355-400
    DOI: 10.1007/BF01198402
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    Cited by:

    1. Patrik Flisberg & Mikael Rönnqvist & Stefan Nilsson, 2009. "Billerud Optimizes Its Bleaching Process Using Online Optimization," Interfaces, INFORMS, vol. 39(2), pages 119-132, April.
    2. Xiong, Jie & Zhao, Haibo & Zhang, Chao & Zheng, Chuguang & Luh, Peter B., 2012. "Thermoeconomic operation optimization of a coal-fired power plant," Energy, Elsevier, vol. 42(1), pages 486-496.
    3. Flisberg, Patrik & Nilsson, Stefan & Rönnqvist, Mikael, 2007. "Optimized on-line process control of bleaching operations with OptCab," Discussion Papers 2007/9, Norwegian School of Economics, Department of Business and Management Science.
    4. Meade, N. & Beasley, J.E. & Adcock, C.J., 2021. "Quantitative portfolio selection: Using density forecasting to find consistent portfolios," European Journal of Operational Research, Elsevier, vol. 288(3), pages 1053-1067.
    5. Domschke, Pia & Kolb, Oliver & Lang, Jens, 2015. "Adjoint-based error control for the simulation and optimization of gas and water supply networks," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 1003-1018.
    6. Emms, Paul, 2007. "Pricing general insurance with constraints," Insurance: Mathematics and Economics, Elsevier, vol. 40(2), pages 335-355, March.
    7. Subramaniam Saravana Sankar & Yiqun Xia & Julaluk Carmai & Saiprasit Koetniyom, 2020. "Optimal Eco-Driving Cycles for Conventional Vehicles Using a Genetic Algorithm," Energies, MDPI, vol. 13(17), pages 1-15, August.
    8. Kolb, Oliver & Göttlich, Simone, 2015. "A continuous buffer allocation model using stochastic processes," European Journal of Operational Research, Elsevier, vol. 242(3), pages 865-874.
    9. 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.

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