An efficient simplified neural network for solving linear and quadratic programming problems
An efficient simplified neural network for solving linear and quadratic programming problems
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DOI：
10.1016/j.amc.2005.07.025
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发表时间：
2006-04
期刊：
影响因子：
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通讯作者：
Hasan Ghasabi-Oskoei;N. Mahdavi-Amiri
中科院分区：
文献类型：
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作者：
Hasan Ghasabi-Oskoei;N. Mahdavi-Amiri
针对一般线性规划和二次规划问题，提出了一种高性能、高效简化的新型神经网络。该网络不需要参数设置，结果是一个不需要模拟乘法器的简单硬件，被证明是稳定的，并且全局收敛到精确解。此外，利用该网络可以同时求解线性规划问题、二次规划问题及其对偶问题。获得的解决方案的高精度和低实施成本是该网络的特点之一。我们用解析的方法证明了网络的全局收敛，并用数值方法验证了结果。
We present a high-performance and efficiently simplified new neural network which improves the existing neural networks for solving general linear and quadratic programming problems. The network, having no need for parameter setting, results in a simple hardware requiring no analog multipliers, is shown to be stable and converges globally to the exact solution. Moreover, using this network we can solve both linear and quadratic programming problems and their duals simultaneously. High accuracy of the obtained solutions and low cost of implementation are among the features of this network. We prove the global convergence of the network analytically and verify the results numerically.