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The network transforms some optimization problems (shortest path, transitive closure, assignment, etc.) into network convergence problems and divides the work load evenly among all processors. The network topology is set up to match conventional optimization procedures.
This thesis proposes a new parallel computing network - a binary relation inference network. The determination of the link weights in a Hopfield-type neural network can be elaborate and convergence for large problems remains a problem. Neural network approaches have been proposed for optimization problems, however, optimal solutions are often not guaranteed when the gradient descent scheme is used. Many of them work in the discrete time domain. A distributed computation model usually embodies the data structure for a specific type of problem. Many distributed parallel computing models have been proposed for various types of problems. These kind of algorithms usually have difficulty in distributing work load evenly among processors. A parallel algorithm can be developed from its sequential counterpart by decomposing the unfinished task dynamically according to the availability of processors. Parallel processing offers faster solutions by dividing its work load among many processors. Traditional graph search techniques provide sequential solutions to constraint satisfaction, but their speed is limited by the computational ability of a single processor. This thesis presents a novel parallel computing network designed for some optimization problems which can be represented by binary relations and the calculation upon them. Many constrained optimization problems are computation-intensive, and yet require fast solutions. Constrained optimization is an essential problem in artificial intelligence, operations research, robotics and very large scale integration hardware design, etc.
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