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A primal-dual linear programming solver with linear order complexity
Conference paper

A primal-dual linear programming solver with linear order complexity

H.-D. Chiang, J.-L. Yuan and C.-C. Chu
Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
1992
DOI: https://doi.org/10.1109/ISCAS.1992.230349

Abstract

Linear programming;Artificial neural networks;Neurons;Neural networks;Computer networks;Voltage;Complexity theory;Computational modeling;Circuit simulation;Output feedback
Recurrent artificial neural network (ANN) models are presented for solving primal-dual linear programming problems. The theoretical background is introduced based on the nonlinear analysis of an ANN. A general procedure to synthesize an ANN for optimization problems is discussed. A method to reduce the circuit complexity of the proposed ANN from the order of O(mn) to O(m+n) is developed. Simulation results are presented through an example of up to 20 variables.

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