Logarithmic Barrier Optimization Problem Using Neural Network
This work provides an approximate solution method for linearly constrained optimization problems, but it is incremental as it applies existing barrier function techniques to neural network computation.
The paper addresses linearly constrained continuous optimization problems by using a logarithmic barrier function within a neural network framework, developing an algorithm that generates a decreasing sequence of solutions converging to zero.
The combinatorial optimization problem is one of the important applications in neural network computation. The solutions of linearly constrained continuous optimization problems are difficult with an exact algorithm, but the algorithm for the solution of such problems is derived by using logarithm barrier function. In this paper we have made an attempt to solve the linear constrained optimization problem by using general logarithm barrier function to get an approximate solution. In this case the barrier parameters behave as temperature decreasing to zero from sufficiently large positive number satisfying convexity of the barrier function. We have developed an algorithm to generate decreasing sequence of solution converging to zero limit.