Equivalence between some dynamical systems for optimization
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It is shown by the derivation of solution methods for an elementary optimization problem that the stochastic relaxation in image analysis, the Potts neural networks for combinatorial optimization and interior point methods for nonlinear programming have common formulation of their dynamics. This unification of these algorithms leads us to possibility for real time solution of these problems with common analog electronic circuits.
KeywordsNeural Network Dynamical System Image Analysis Artificial Intelligence Complex System
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- R.A. Hummel, S.W. Zucker. On the foundations of relaxation labeling processes, IEEE Trans. Patt. Anal. Mac. Intelli., PAMI-5, pp.267–286, 1983.Google Scholar
- L.E. Faybusovich. Interior point methods and entropy,1991 ICDC, pp.2094–2095, 1991.Google Scholar
- A. Yuille, D. Kosowsky. Statistical physics algorithms that converge,Neural Comp., 6, pp.341–356, 1994.Google Scholar
- U. Helmke, J.B. Moore. Optimization and dynamical systems, Springer-Verlag, 1994.Google Scholar
- K. Urahama. Performance of neural algorithms for maximum-cut problems,J. Circuit, Syst. Comput., 2, pp.389–395, 1992.Google Scholar
- M. Thathachar, P.S. Sastry. Relaxation labeling with learning automata,IEEE Trans. Patt. Anal. Mach. Intelli., PAMI-8, pp.256–267, 1986.Google Scholar
- E. Akin.The geometry of population genetics, Springer-Verlag, 1979.Google Scholar
- C. Mead.Analog VLSI and neural systems, Addison-Wesley, 1989.Google Scholar
- K. Urahama. Analog method for solving combinatorial optimization problems,IEICE Trans. Fundamentals, E77-A, pp.302–308, 1994.Google Scholar