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Globally convergent algorithm for nonlinearly constrained optimization problems

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Abstract

A new globally convergent algorithm for minimizing an objective function subject to equality and inequality constraints is presented. The algorithm determines a search direction by first solving a linear program and using the information gained thereby to define a quadratic approximation, with a guaranteed solution, to the original problem; the solution of the quadratic problem is the desired search direction. The algorithm incorporates a new method for choosing the penalty parameter. Numerical results illustrate the performance of the algorithm.

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Authors and Affiliations

  1. Department of Electrical Engineering, Imperial College of Science and Technology, London, England

    M. Sahba (Research Officer)

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  1. M. Sahba
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Communicated by D. Q. Mayne

The author wishes to thank Professor D. Q. Mayne and Dr. F. A. Pantoja for critically reviewing the first draft of this paper, for their suggestions, criticism, and contributions to some of the proofs. Support of the UK Science Research and Engineering Council is gratefully acknowledged.

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Sahba, M. Globally convergent algorithm for nonlinearly constrained optimization problems. J Optim Theory Appl 52, 291–309 (1987). https://doi.org/10.1007/BF00941288

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