A projection based recurrent neural network approach to nonconvex optimization
首发时间:2015-12-23
Abstract:In this paper, we propose a projection based recurrent neural network for solving nonconvex programming problems subjected to nonlinear equality and bound constraints. The proposed neural network makes use of a gradient projection onto the tagent space of the constraints and the well-known projection theorem. It is shown here that the proposed neural network is stable and globally convergent to an optimal solution within a finite time. Global convergence analysis are established for nonconvex problems. Numerical examples are provided to show the applicability of the proposed neural network. And the performance proved its effective and efficiency.
keywords: Binary quadratic problem, Recurrent neural network, Projection theorem, Nonconvex optimization
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一种求解非凸优化问题的基于投影函数的反馈神经网络算法
摘要:在本文中,我们提出了一种基于投影函数的反馈神经网络求解带非线性等式约束和边界约束的非凸优化问题。该神经网络利用约束条件的切线空间的梯度投影和知名的投影定理。如文中所示,本文提出的神经网络是稳定的,并能在有限时间内全局收敛到一个最佳的解决方案。文中对于非凸性优化问题进行了全局收敛性分析。数值算例显示了提出的神经网络模型的适用性和高效性。
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No.4671758111719214****
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