给出一类广义鞍点问题迭代解法的收敛性分析结果，降低了目前已有相关结论的适用条件，因而使得相关结果具有更广泛的应用性。
In this paper, we present a convergent result of the iterative solution methods for a class of generalized saddle point problem, which lowers the condition of the recent results.
研究广义状态系统中线性二次型微分对策鞍点策略的数值求解问题。
This paper studies the numerical problem of the saddle point strategy for linear quadratic differential game in generalized state systems.
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.
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