基于线性化Bregman算法的稀疏化步宽自适应

M. Lunglmayr, M. Huemer
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引用次数: 3

摘要

基于线性布雷格曼迭代的迭代算法可以有效地解决稀疏估计问题。特别是Kaczmarz滤波器和稀疏最小均方滤波器(LMS)变体非常适合在数字硬件和软件中实现。然而,当分析这种算法在迭代中的误差时,人们意识到,特别是在早期的迭代中,只有很小的误差减少发生。为了改善这种行为,我们建议使用稀疏启用的步宽自适应。仿真结果表明,该方法显著提高了稀疏Kaczmarz和稀疏LMS算法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sparsity-Enabled Step Width Adaption For Linearized Bregman Based Algorithms
Iterative algorithms based on linearized Bregman iterations allow efficiently solving sparse estimation problems. Especially the Kaczmarz and sparse least mean squares filter (LMS) variants are very suitable for implementation in digital hardand software. However, when analyzing the error of such algorithms over the iterations one realizes that especially at early iterations only small error reductions occur. To improve this behavior, we propose to use sparsity-enabled step width adaption. We show simulations results demonstrating that this approach significantly improves the performance of sparse Kaczmarz and sparse LMS algorithms.
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