Title :
A primal-dual linear programming solver with linear order complexity
Author :
Chiang, Hsiao-Dong ; Yuan, Jen-Lun ; Chu, Chia-Chi
Author_Institution :
Sch. of Electr. Eng., Cornell Univ., Ithaca, NY, USA
Abstract :
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
Keywords :
linear programming; recurrent neural nets; artificial neural network; circuit complexity; linear order complexity; nonlinear analysis; optimization problems; primal-dual linear programming solver; recurrent ANN; Artificial neural networks; Circuit simulation; Complexity theory; Computational modeling; Computer networks; Linear programming; Neural networks; Neurons; Output feedback; Voltage;
Conference_Titel :
Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-0593-0
DOI :
10.1109/ISCAS.1992.230349
