Neural network for quadratic optimization with bound constraints

1993 ◽  
Vol 4 (2) ◽  
pp. 293-304 ◽  
Author(s):  
A. Bouzerdoum ◽  
T.R. Pattison
Keyword(s):  
Neural Network ◽  
Quadratic Optimization ◽  
Bound Constraints

scholarly journals Robust Linear Neural Network for Constrained Quadratic Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Zixin Liu ◽  
Yuanan Liu ◽  
Lianglin Xiong
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Perturbation Theory ◽  
Optimization Problems ◽  
Quadratic Optimization ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Analysis Method ◽  
Linear Neural Network ◽  
Constrained Quadratic Optimization

Based on the feature of projection operator under box constraint, by using convex analysis method, this paper proposed three robust linear systems to solve a class of quadratic optimization problems. Utilizing linear matrix inequality (LMI) technique, eigenvalue perturbation theory, Lyapunov-Razumikhin method, and LaSalle’s invariance principle, some stable criteria for the related models are also established. Compared with previous criteria derived in the literature cited herein, the stable criteria established in this paper are less conservative and more practicable. Finally, a numerical simulation example and an application example in compressed sensing problem are also given to illustrate the validity of the criteria established in this paper.

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