This paper proposes a neurodynamic approach to zero-one quadratic programming with linear constraints. Compared with one existing neurodynamic approach to such problems, the proposed one has lower dimensions of state variables and less computational cost, which makes the implementation easier. Under some suitable conditions, the stability and convergence properties of the proposed approach are established. Numerical simulation results and related comparisons show the efficiency of this proposed method in practical computation.

中文翻译：

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零一二次规划的神经动力学方法

本文提出了一种线性约束为零的二次规划的神经动力学方法。与现有的针对此类问题的神经动力学方法相比，所提出的方法具有较小的状态变量维数和较少的计算成本，这使得实现起来更加容易。在某些合适的条件下，建立了该方法的稳定性和收敛性。数值仿真结果和相关比较表明，该方法在实际计算中是有效的。