1. Neural Networks for Optimization William J. Wolfe California State University Channel Islands

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3. Neural Models Simple processing units, and lots of them Highly interconnected Variety of connection architectures/strengths Exchange excitatory and inhibitory signals Learning: changes in connection strengths Knowledge: connection strengths/architecture No central processor: distributed processing

4. Simple Neural Model a i Activation e i External input w ij Connection Strength Assume: w ij = w ji (“symmetric” network)  W = (w ij ) is a symmetric matrix

5. Net Input

6. Dynamics Basic idea:

7. Energy

9. Lower Energy da/dt = net = -grad(E)  seeks lower energy

10. Problem: Divergence

11. A Fix: Saturation

12. Keeps the activation vector inside the hypercube boundaries Encourages convergence to corners

13. Summary: The Neural Model a i Activation e i External Input w ij Connection Strength W (w ij = w ji ) Symmetric

14. Example: Inhibitory Networks Completely inhibitory –wij = -1 for all i,j –k-winner Inhibitory Grid –neighborhood inhibition

15. Traveling Salesman Problem Classic combinatorial optimization problem Find the shortest “tour” through n cities n!/2n distinct tours

16. Neural Network Approach

17. Tours – Permutation Matrices tour: CDBA permutation matrices correspond to the “feasible” states.

18. Not Allowed

19. Only one city per time stop Only one time stop per city  Inhibitory rows and columns

20. Distance Connections: Inhibit the neighboring cities in proportion to their distances.

21. putting it all together:

22. R n 2 = F 0  E c  E r  D aix proj = aix + act avg - rowx avg - coli avg Feasible Solutions

23. E = -1/2 { ∑ i ∑ x ∑ j ∑ y a ix a jy w ixjy } = -1/2 { ∑ i ∑ x ∑ y (- d(x,y)) a ix ( a i+1 y + a i-1 y ) + ∑ i ∑ x ∑ j (-1/n) a ix a jx + ∑ i ∑ x ∑ y (-1/n) a ix a iy + ∑ i ∑ x ∑ j ∑ y (1/n 2 ) a ix a jy }

24. Research Questions Which architecture is best? Does the network produce: –feasible solutions? –high quality solutions? –optimal solutions? How do the initial activations affect network performance? Is the network similar to “nearest city” or any other traditional heuristic? How does the particular city configuration affect network performance? Is there any way to understand the nonlinear dynamics? References: –Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research. Kate A. Smith, Informs Journal on Computing, Vol. 11, No. 1, Winter 1999. –An Analytical Framework for Optimization Problems. A. Gee, S. V. B. Aiyer, R. Prager, 1993, Neural Networks 6, 79-97. –Neural Computation of Decisions in Optimization Problems. J. J. Hopfield, D. W. Tank, Biol. Cybern. 52, 141-152 (1985).

25. typical state of the network before convergence

26. “Fuzzy Readout”

27. DEMO 1

28. Fuzzy Tour Lengths Tour Length Iteration

30. DEMO 2 Applet by Darrell Long http://hawk.cs.csuci.edu/william.wolfe/TSP001/TSP1.html

31. EXTRA SLIDES

32. Brain Approximately 10 10 neurons Neurons are relatively simple Approximately 10 4 fan out No central processor Neurons communicate via excitatory and inhibitory signals Learning is associated with modifications of connection strengths between neurons

35. with external input e = 1/2

36. Perfect K-winner Performance: e = k-1/2