Adaptive randomized coordinate descent for solving sparse systems

Alexandru Onose, B. Dumitrescu
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引用次数: 2

Abstract

Randomized coordinate descent (RCD), attractive for its robustness and ability to cope with large scale problems, is here investigated for the first time in an adaptive context. We present an RCD adaptive algorithm for finding sparse least-squares solutions to linear systems, in particular for FIR channel identification. The algorithm has low and tunable complexity and, as a special feature, adapts the probabilities with which the coordinates are chosen at each time moment. We show through simulation that the algorithm has tracking properties near those of the best current methods and investigate the trade-offs in the choices of the parameters.
求解稀疏系统的自适应随机坐标下降
随机坐标下降算法(RCD)以其鲁棒性和处理大规模问题的能力而备受关注,首次在自适应环境下进行了研究。我们提出了一种RCD自适应算法,用于寻找线性系统的稀疏最小二乘解,特别是用于FIR信道识别。该算法具有复杂度低、可调的特点,并能自适应各时刻坐标选择的概率。我们通过仿真表明,该算法具有接近当前最佳方法的跟踪特性,并研究了参数选择中的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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