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Monotone Variable–Metric Algorithm for Linearly Constrained Nonlinear Programming

Author

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  • G. Pacelli

    (University of Ancona)

  • M. C. Recchioni

    (University of Ancona)

Abstract

A new method for linearly constrained nonlinear programming is proposed. This method follows affine scaling paths defined by systems of ordinary differential equations and it is fully parallelizable. The convergence of the method is proved for a nondegenerate problem with pseudoconvex objective function. In practice, the algorithm works also under more general assumptions on the objective function. Numerical results obtained with this computational method on several test problems are shown.

Suggested Citation

  • G. Pacelli & M. C. Recchioni, 2000. "Monotone Variable–Metric Algorithm for Linearly Constrained Nonlinear Programming," Journal of Optimization Theory and Applications, Springer, vol. 104(2), pages 255-279, February.
    • Handle: RePEc:spr:joptap:v:104:y:2000:i:2:d:10.1023_a:1004645328197
    DOI: 10.1023/A:1004645328197
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    References listed on IDEAS

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    Cited by:

    1. Miglierina, E. & Molho, E. & Recchioni, M.C., 2008. "Box-constrained multi-objective optimization: A gradient-like method without "a priori" scalarization," European Journal of Operational Research, Elsevier, vol. 188(3), pages 662-682, August.
    2. Maria Cristina Recchioni & Gabriele Tedeschi, 2016. "From bond yield to macroeconomic instability: The effect of negative interest rates," Working Papers 2016/06, Economics Department, Universitat Jaume I, Castellón (Spain).

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