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A Convex Approximation Method For Large Scale Linear Inequality Constrained Minimization

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  • HAI-JUN WANG

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    Science of College, China University of Mining and Technology, Xuzhou 221008, China)

  • QIN NI

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

A new method of moving asymptotes for large scale minimization subject to linear inequality constraints is discussed in this paper. In each step of the iterative process, a descend direction is obtained by solving a convex separable subproblem with dual technique. The new rules for controlling the asymptotes parameters are designed by the trust region radius and some approximation properties such that the global convergence of the new method are obtained. The numerical results show that the new method may be capable of processing some large scale problems.

Suggested Citation

  • Hai-Jun Wang & Qin Ni, 2010. "A Convex Approximation Method For Large Scale Linear Inequality Constrained Minimization," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 27(01), pages 85-101.
    • Handle: RePEc:wsi:apjorx:v:27:y:2010:i:01:n:s0217595910002557
    DOI: 10.1142/S0217595910002557
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    References listed on IDEAS

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    1. Wenyu Sun & Ya-Xiang Yuan, 2006. "Optimization Theory and Methods," Springer Optimization and Its Applications, Springer, number 978-0-387-24976-6, June.
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