Conference proceeding
A primal-dual linear programming solver with linear order complexity
1992 IEEE International Symposium on Circuits and Systems (ISCAS), v 4, pp 1697-1700 vol.4
1992
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.< >
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4 citations in Scopus
Details
- Title
- A primal-dual linear programming solver with linear order complexity
- Creators
- H.-D Chiang - Cornell UniversityJ.-L Yuan - Cornell UniversityC.-C Chu - Cornell UniversityIEEERobert Fischl
- Publication Details
- 1992 IEEE International Symposium on Circuits and Systems (ISCAS), v 4, pp 1697-1700 vol.4
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Web of Science ID
- WOS:A1992BW69A00413
- Scopus ID
- 2-s2.0-84997707554
- Other Identifier
- 991019350675704721
InCites Highlights
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- Web of Science research areas
- Engineering, Electrical & Electronic