1. Optimization with Neural Networks Presented by: Mahmood Khademi Babak Bashiri Instructor: Dr. Bagheri Sharif University of Technology April 2007

2. Introduction An optimization problem consists of two parts: Cost function and Constraints  Constrained The constraints are built in the cost function, so minimizing the cost function also satisfies the constraints  Unconstraint There is no constraint for the problem!  Combinatorial We separate the constraints and the cost function, minimize each of them and then add them together

3. Application Applications in many fields like:  Routing in computer networks  VLSI circuit design  Planning in operational and logistic systems  Power distribution systems  Wireless and satellite communication systems

4. Basic idea  If : decision variables  Suppose is our objective function.  Constraints can be expressed as nonnegative penalty functions that only when represent a feasible solution  By combining the penalty functions with F, the original constrained problem may be reformulated as unconstrained problem in which the goal is to minimize the quantity :

5. TSP  Is simple to state but very difficult to solve.  The problem is to find the shortest possible tour through a set of N vertices so that each vertex is visited exactly once.  This problem is known to be NP-complete

6. Why neural network?  Drawbacks of conventional computing systems: Perform poorly on complex problems Lack the computational power Don’t utilize the inherent parallelism of problems  Advantages of artificial neural networks: Perform well even on complex problems Very fast computational cycles if implemented in hardware Can take the advantage of inherent parallelism of problems

7. Some Efforts to solve optimization problems  Many ANN algorithms with different architectures have been used to solve different optimization problems…  We’ve selected: Hopfield NN Elastic Net Self Organizing Map NN

8. Hopfield-Tank model  TSP must be mapped, in some way, onto the neural network structure  Each row corresponds to a particular city and each column to a particular position in the tour

9. Mapping TSP to Hopfield neural net  There is a connection between each pair of units  The signal sent along a connection from i to t j is equal to the weight Tij if i is activated. It is equal to 0 otherwise.  A negative weight defines inhibitory connection between the two units  It is unlikely that two units with negative weigh will be active or “on” at the same time

10. Discrete Hopfield Model  connection weights are not learned  Hopfield network evolves by updating the activation of each unit in turn  In final state, all units are stable according to the update rule  The units are updated at random, one unit at a time {Vi}i=1,...,L, L :number of units Vi :activation level of unit i Tij: connection weight between units i and j tetai: threshold of unit i.

11. Discrete Hopfield Model (Cont.)  Energy function  Units changes its activation level if and only if the energy of the network decreases by doing so:  Since the energy can only decrease over time and the number configuration is finite the network must converge (but not necessarily the minimum energy state)

12. Continuous Hopfield-Tank  Neuron function is continuous (Sigmoid function)  The evolution of the units over time is now characterized by the following differential equation : Ui, Ii and Vi are the input, input bias, and activation level of unit I, respectively

13. Continuous Hopfield-Tank  Energy function  Discrete time approximation is applied to the equations of motion

14. Application of the Hopfield-Tank Model to the TSP

15. Application of the Hopfield-Tank model to the TSP (1)The TSP is represented as an N*N matrix (2) Energy function (3)Bias and connection weights are derived

16. Application of the Hopfield-Tank model to the TSP

17. Results of Hopfield-Tank  Hopfield and Tank were able to solve a randomly generated 10-city,with parameter value :A=B=500,C=200,N=15.  They reported for 20 trails, network converge 16 times to feasible tours.  Half of those tours were one of two optimal tours

18.  The size of each black square indicates the value of the output of the corresponding neuron

19. The main weaknesses of the original Hopfield-Tank model

20. (d) Model plagued with the limitation of “hill-climbing” approaches (e) Model does not guarantee feasibility

21. The main weaknesses of the original Hopfield-Tank model The positive points: Can easily implemented in hardware Can be applied to non-Euclidean TSPs

22. Elastic net (Willshaw-Von der Malsburg)

23. Elastic net

24. Energy function for Elastic net

25. The self organizing map  The SOM are instances of “competitive NN”, used by unsupervised learning system to classify data  Adjusting the weights  Related to elastic net  Differ of elastic net

26. Competitive Network  Group a set of I-dimensional input pattern in to K cluster (K<=M)

27. SOM in the TSP context  A set of 2-dimensional coordinates must be mapped onto a set of 1-dimensional positions in the tour 

28. SOM in the TSP context

29. Different SOM based on that form  Fort increased speed of convergence by reducing neighborhood and reducing modification to weights of neighboring units over time.  The work of Angeniol

30. Questions ?