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A note on solving nonlinear minimax problems via a differentiable penalty function

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Abstract

The note demonstrates that modeling a nonlinear minimax problem as a nonlinear programming problem and applying a classical differentiable penalty function to attempt to solve the problem can lead to convergence to a stationary point of the penalty function which is not a feasible point of the nonlinear programming problem. This occurred naturally in an application from statistical reliability theory. The note resolves the problem through modification of both the problem formulation and the iterative penalty function method.

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References

  1. Zangwill, W. I.,An Algorithm for the Chebyshev Problem with An Application to Concave Programming, Management Science, Vol. 14, pp. 58–78, 1967.

    Google Scholar 

  2. Han, S. P.,Superlinear Convergence of a Minimax Method, Cornell University, Ithaca, New York, Department of Computer Science, Technical Report No. 78-336, 1978.

    Google Scholar 

  3. Conn, A. R.,An Efficient Second-Order Method to Solve the Constrained Minimax Problem, University of Waterloo, Waterloo, Canada, Department of Combinatorics and Optimization, CORR 79-5, 1979.

    Google Scholar 

  4. Murray, W., andOverton, M. L.,A Projected Lagrangian Algorithm for Nonlinear Minimax Optimization, SIAM Journal of Scientific and Statistical Computing, Vol. 1, pp. 345–370, 1980.

    Google Scholar 

  5. Fletcher, R.,Practical Methods of Optimization, Vol. 2, Constrained Optimization, Wiley Interscience, New York, New York, 1981.

    Google Scholar 

  6. Barlow, R. E., andProschan, F.,Statistical Theory of Reliability and Life Testing, Holt, Rinehart, and Winston, New York, New York, 1975.

    Google Scholar 

  7. Marshall, A. W., andProschan, F.,Maximum Likelihood Estimation for Distributions with Monotone Failure Rate, Annals of Mathematical Statistics, Vol. 36, pp. 69–77, 1965.

    Google Scholar 

  8. Crow, L. H., andShimi, I. N.,Maximum Likelihood Estimation of Life Distributions from Renewal Testings, Annals of Mathematical Statistics, Vol. 43, pp. 1827–1838, 1972.

    Google Scholar 

  9. Barlow, R. E., Bartholomew, D. J., Bremner, J. M., andBrunk, H. D.,Statistical Inference under Order Restrictions, John Wiley and Sons, New York, New York, 1972.

    Google Scholar 

  10. Wolfowitz, J.,The Minimal Distance Method, Annals of Mathematical Statistics, Vol. 28, pp. 75–88, 1957.

    Google Scholar 

  11. Whitaker, L. R.,Some Nonparametric Inference Results in Reliability, University of California, Davis, Division of Statistics, PhD Dissertation, 1984.

    Google Scholar 

  12. Shanno, D. F., andPhua, K. H.,Remark on Algorithm 500, Transactions on Mathematical Software, Vol. 6, pp. 618–622, 1980.

    Google Scholar 

  13. Coleman, T. F., andConn, A. R.,Second-Order Conditions for an Exact Penalty Function, Mathematical Programming, Vol. 19, pp. 178–185, 1980.

    Google Scholar 

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Shanno, D.F., Whitaker, L. A note on solving nonlinear minimax problems via a differentiable penalty function. J Optim Theory Appl 46, 95–103 (1985). https://doi.org/10.1007/BF00938763

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