Evaluation of Optimization Algorithms and Noise Robustness of Sparsity-Promoting Dynamic Mode Decomposition
Evaluation of Optimization Algorithms and Noise Robustness of Sparsity-Promoting Dynamic Mode Decomposition
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DOI：
10.1109/access.2022.3193157
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发表时间：
2022-01-01
期刊：
影响因子：
3.9
通讯作者：
Asai, Keisuke
中科院分区：
文献类型：
--
作者：
Iwasaki, Yuto;Nonomura, Taku;Asai, Keisuke
在本研究中，我们组织现有的稀疏促进动态模式分解（DMDsp）的噪声鲁棒性，提出快速优化算法DMDsp，并评估其特性。研究了基于系统的DMDsp（sDMDsp）和基于观测的DMDsp（oDMDsp）两类DMDsp，以及快速迭代收缩阈值法（FISTA）、交替方向乘子法（ADMM）和贪婪算法三种优化算法。对于sDMDsp和oDMDsp，FISTA产生最短的处理时间。具有FISTA的sDMDsp的处理时间短于具有FISTA的oDMDsp的处理时间。三种优化算法对sDMDsp和oDMDsp的原始数据重构误差相似。sDMDsp和oDMDsp的噪声鲁棒性进行了评估。sDMDsp和oDMDsp对观测噪声具有类似的鲁棒性，除了具有大的系统和观测噪声的系统之外，oDMDsp优于sDMDsp。
In the present study, we organize the existing sparsity-promoting dynamic mode decomposition (DMDsp) in terms of noise robustness, propose faster optimization algorithm for DMDsp, and evaluate its characteristics. Two kinds of DMDsp, namely system-based DMDsp (sDMDsp) and observation-based DMDsp (oDMDsp), combined with three kinds of optimization algorithm, namely the fast iterative shrinkage thresholding algorithm (FISTA), the alternating direction method of multipliers (ADMM), and a greedy algorithm, are investigated. For both sDMDsp and oDMDsp, FISTA yields the shortest processing time. The processing time for sDMDsp with FISTA is shorter than that for oDMDsp with FISTA. The original data reconstruction errors for sDMDsp and oDMDsp are similar among the three optimization algorithms. The noise robustness for sDMDsp and oDMDsp is evaluated. sDMDsp and oDMDsp have similar robustness to observation noise, except for a system with large system and observation noise, for which oDMDsp outperforms sDMDsp.