给出了二次网络规划基解的一个优化方向；并获得二次网络规划的一个近似算法和有效算法。
An optimal direction of bases for quadratic network programming is given. There by, an approximate and an effective algorithms are obtained.
这些新的结论都表明了该神经网络在求解有界约束二次规划问题时的有效性。
All these new results show the validity of the network in solving quadratic optimization with bound constraints.
考虑了广义二次规划问题，基于其鞍点的充要条件，提出了求解它的一个神经网络。
This paper considers the extended quadratic programming problem. Based on the necessary and sufficient conditions for a saddle point, a neural network for solving it is proposed.
提出了基于线性变分不等式的原对偶神经网络，并将其作为所对应的二次型规划方案的实时求解器。
A primal-dual neural network(PDNN)based on linear variational inequalities(LVIs)is introduced as the real-time solver for the resultant quadratic programming scheme.
提出了基于线性变分不等式的原对偶神经网络，并将其作为所对应的二次型规划方案的实时求解器。
A primal-dual neural network(PDNN)based on linear variational inequalities(LVIs)is introduced as the real-time solver for the resultant quadratic programming scheme.
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