Feature selection via Boolean independent component analysis
Information Sciences: an International Journal
A neural network methodology of quadratic optimization with quadratic equality constraints
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A neural network algorithm for second-order conic programming
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Research on reservation allocation decision method based on neural network
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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A new gradient-based neural network is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory, and LaSalle invariance principle to solve linear and quadratic programming problems. In particular, a new function F(x, y) is introduced into the energy function E(x, y) such that the function E(x, y) is convex and differentiable, and the resulting network is more efficient. This network involves all the relevant necessary and sufficient optimality conditions for convex quadratic programming problems. For linear programming and quadratic programming (QP) problems with unique and infinite number of solutions, we have proven strictly that for any initial point, every trajectory of the neural network converges to an optimal solution of the QP and its dual problem. The proposed network is different from the existing networks which use the penalty method or Lagrange method, and the inequality constraints are properly handled. The simulation results show that the proposed neural network is feasible and efficient