Efficient architectures to recover the regularized least squares solution

R. Sundaram

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Efficient architectures to recover the regularized least squares solution

机译：用于恢复正则化最小二乘解的高效架构

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Several practical applications are concerned with the identification of the least squares (LS) solution. The objective is to attain this solution accurately and efficiently while conserving resources. The computational and storage requirements todetermine the LS solution by any iterative procedure become prohibitively large as the problem dimensions grow. This brief presents some architectures based on thresholded binary networks which recover regularized LS solutions by partitioning suchnetworks and adopting a switching operation between active and inactive partitions to optimize the objective function. Also, an iterative method based on steepest descent is briefly discussed and implemented. It yields reliable estimates of theregularized LS solution, while providing savings in computation and storage.

机译：一些实际应用与最小二乘（LS）解的识别有关。目标是在节约资源的同时准确有效地实现该解决方案。随着问题维度的增加，通过任何迭代过程确定 LS 解决方案的计算和存储要求变得非常大。本文介绍了一些基于阈值二进制网络的架构，这些架构通过对此类网络进行分区并在活动和非活动分区之间采用切换操作来优化目标函数来恢复正则化 LS 解。此外，还简要讨论并实现了一种基于最陡下降的迭代方法。它对正则化 LS 解进行了可靠的估计，同时节省了计算和存储成本。

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《IEEE Transactions on Automatic Control》 |1999年第6期|828-831|共4页
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R. Sundaram;
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Efficient architectures to recover the regularized least squares solution

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