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Terminal repeller unconstrained subenergy tunneling (trust) for fast global optimization

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Abstract

A new method for unconstrained global function optimization, acronymedtrust, is introduced. This method formulates optimization as the solution of a deterministic dynamical system incorporating terminal repellers and a novel subenergy tunneling function. Benchmark tests comparing this method to other global optimization procedures are presented, and thetrust algorithm is shown to be substantially faster. Thetrust formulation leads to a simple stopping criterion. In addition, the structure of the equations enables an implementation of the algorithm in analog VLSI hardware, in the vein of artificial neural networks, for further substantial speed enhancement.

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References

  1. Cetin, B. C., Kerns, D. A., Burdick, J. W., andBarhen, J.,Analog Circuits for Terminal Attractors, Repellers and Gradient Descent, Robotics and Mechanical Systems Report No. RMS-92-01, Department of Mechanical Engineering, California Institute of Technology, Pasadena, California, 1991.

    Google Scholar 

  2. Kan, A. H. G. R., andTimmer, G. T.,A Stochastic Approach to Global Optimization, Numerical Optimization, Edited by P. T. Boggs, R. H. Byrd, and R. B. Schnabel, SIAM, Philadelphia, Pennsylvania, pp. 245–262, 1985.

    Google Scholar 

  3. Levy, A. V., andMontalvo, A.,The Tunneling Algorithm for the Global Minimization of Functions, SIAM Journal on Scientific and Statistical Computing, Vol. 6, pp. 15–29, 1985.

    Google Scholar 

  4. Yao, Y.,Dynamic Tunneling Algorithm for Global Optimization, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 19, pp. 1222–1230, 1989.

    Google Scholar 

  5. Zak, M.,Terminal Attractors in Neural Networks, Neural Networks, Vol. 2, pp. 259–274, 1989.

    Google Scholar 

  6. Zak, M., andBarhen, J.,Neural Networks with Creative Dynamics, Mathematical & Computer Modeling, Vol. 14, pp. 290–294, 1990.

    Google Scholar 

  7. Barhen, J., Toomarian, N., andGulati, S.,Application of Adjoint Operators to Neural Learning, Applied Mathematical Letters, Vol. 3, pp. 13–18, 1990.

    Google Scholar 

  8. Barhen, J., Zak, M., andToomarian, N.,Non-Lipschitzian Neural Dynamics, Advanced Neural Computers, Edited by R. Eckmiller, North-Holland, Amsterdam, Holland, pp. 102–112, 1990.

    Google Scholar 

  9. Barhen, J., Zak, M., andToomarian, N.,Adjoint Operator Algorithms for Faster Learning in Neural Networks, Advanced Neural Information Processing Systems, Vol. 2, pp. 498–508, 1990.

    Google Scholar 

  10. Gruver, W. A., andSachs, E.,Algorithmic Methods in Optimal Control, Pitman Publishing, Melbourne, Australia, 1990.

    Google Scholar 

  11. Aluffi-Pentini, F., Parisi, V., andZirilli, F.,Global Optimization and Stochastic Differential Equations, Journal of Optimization Theory and Applications, Vol. 47, pp. 1–15, 1985.

    Google Scholar 

  12. Walster, G. W., Hansen, E. R., andSengupta, S.,Test Results for a Global Optimization Problem, Numerical Optimization, Edited by P. T. Boggs, R. H. Byrd, and R. B. Schnabel, SIAM, Philadelphia, Pennsylvania, pp. 272–287, 1985.

    Google Scholar 

  13. Ge, R.,A Filled Function Method for Finding a Global Minimizer of a Function of Several Variables, Mathematical Programming, Vol. 46, pp. 191–204, 1990.

    Google Scholar 

  14. Szu, H., andHartley, R.,Fast Simulated Annealing, Physics Letters A, Vol. 122, pp. 157–162, 1987.

    Google Scholar 

  15. Kirkpatrick, S., Gelatt, C. D., andVecchi, M. P.,Optimization by Simulated Annealing, Science, Vol. 220, pp. 671–680, 1983.

    Google Scholar 

  16. Bremermann, H. A.,A Method of Unconstrained Global Optimization, Mathematical Biosciences, Vol. 9, pp. 1–15, 1970.

    Google Scholar 

  17. Törn, A., andZilinskas, A.,Global Optimization, Springer-Verlag, Berlin, Germany, 1989.

    Google Scholar 

  18. Price, W. L.,A Controlled Random Search Procedure for Global Optimization, Toward Global Optimization 2, Edited by L. C. W. Dixon and G.-P. Szegö, North-Holland, Amsterdam, Holland, 1978.

    Google Scholar 

  19. Törn, A. A.,A Search Clustering Approach to Global Optimization, Toward Global Optimization 2, Edited by L. C. W. Dixon and G. P. Szegö, North-Holland, Amsterdam, Holland, 1978.

    Google Scholar 

  20. Cetin, B. C., Barhen, J., andBurdick, J. W.,Terminal Repeller Unconstrained Subenergy Tunneling (TRUST) for Global Optimization, Robotics and Mechanical Systems Report No. RMS-90-03, Department of Mechanical Engineering, California Institute of Technology, Pasadena, California, 1990.

    Google Scholar 

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Communicated by G. Di Pillo

This work was supported by the Department of Energy, Office of Basic Energy Sciences, Grant No. DE-A105-89-ER14086.

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Cetin, B.C., Barhen, J. & Burdick, J.W. Terminal repeller unconstrained subenergy tunneling (trust) for fast global optimization. J Optim Theory Appl 77, 97–126 (1993). https://doi.org/10.1007/BF00940781

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