Gradient Projection Network: Analog Solver for Linearly Constrained Nonlinear Programming

Neural Computation

Volumn 8, Issue 5, 1996, Pages 1061-1073

  Urahama, Kiichi ^a  

^a KYUSHU UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0001302140     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/neco.1996.8.5.1061     Document Type: Article

References (12)

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3. Hopfield, J., and Tank, D. 1985. Neural computations of decisions in optimization problems. Biol. Cybern. 52, 141-152.
4. Karmarkar, N. 1984. A new polynomial-time algorithm for linear programming. Combinatorica 4, 373-395.
5. Karmarkar, N. 1990. Riemannian geometry underlying interior-point methods for linear programming. Contemp. Math. 114, 51-75.
6. Peterson, C., and Söderberg, B. 1989. A new method for mapping optimization problems onto neural networks. Int. J. Neural Syst. 1, 3-22.
7. Platt, J. C., and Barr, A. H. 1987. Constrained differential optimization. In Neural Information Processing Systems, D. Z. Anderson, ed., pp. 612-621. American Institute of Physics, New York.
8. Urahama, K. 1992. Deterministic annealing in neural networks for combinatorial optimization. Proc. Int. Symp. Neural Inf. Process. 94-97.
9. Urahama, K. 1994a. Analog method for solving combinatorial optimization problems. IEICE Trans. Fundamentals E77-A, 302-308.
10. Urahama, K. 1994b. Analog circuit for solving assignment problems. IEEE Trans. Circuits Syst. 41(1), 426-429.
11. Urahama, K., and Ueno, S. 1993. A gradient system solution to Potts mean field equations and its electronic implementation. Int. J. Neural Syst. 4, 27-34.
12. Yuille, A. L., and Kosowsky, J. J. 1994. Statistical physics algorithms that converge. Neural Comp. 6(3), 341-356.