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Trust region affine scaling algorithms for linearly constrained convex and concave programs

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Abstract

We study a trust region affine scaling algorithm for solving the linearly constrained convex or concave programming problem. Under primal nondegeneracy assumption, we prove that every accumulation point of the sequence generated by the algorithm satisfies the first order necessary condition for optimality of the problem. For a special class of convex or concave functions satisfying a certain invariance condition on their Hessians, it is shown that the sequences of iterates and objective function values generated by the algorithm convergeR-linearly andQ-linearly, respectively. Moreover, under primal nondegeneracy and for this class of objective functions, it is shown that the limit point of the sequence of iterates satisfies the first and second order necessary conditions for optimality of the problem. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.

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Authors and Affiliations

  1. School of Industrial and Systems Engineering, Georgia Institute of Technology, 30332, Atlanta, GA, USA

    Renato D. C. Monteiro & Yanhui Wang

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  1. Renato D. C. Monteiro
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  2. Yanhui Wang
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Additional information

The work of these authors was based on research supported by the National Science Foundation under grant INT-9600343 and the Office of Naval Research under grants N00014-93-1-0234 and N00014-94-1-0340.

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Monteiro, R.D.C., Wang, Y. Trust region affine scaling algorithms for linearly constrained convex and concave programs. Mathematical Programming 80, 283–313 (1998). https://doi.org/10.1007/BF01581170

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  • DOI: https://doi.org/10.1007/BF01581170

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