During the regularized COVID-19 epidemic control, the regional TCM intelligent cloud system is extended to the isolated medical observation sites through 5G technology. Combined with the advantages of local Chinese medicine hospitals, TCM services such as TCM preparations and TCM remote diagnosis and treatment were provided to personnel at isolated medical observation sites. These services not only reduce the risk of COVID-19 infection, but also relieve the anxiety of those under isolation and medical observation. “5G + TCM services” gives full play to the important role of TCM in the prevention and control of epidemics and provides epidemic prevention and control solutions suitable for China.

Recently, solving rank minimization problems by leveraging nonconvex relaxations has received significant attention. Some theoretical analyses demonstrate that it can provide a better approximation of original problems than convex relaxations. However, designing an effective algorithm to solve nonconvex optimization problems remains a big challenge. In this paper, we propose an Iterative Shrinkage-Thresholding and Reweighted Algorithm (ISTRA) to solve rank minimization problems using the nonconvex weighted nuclear norm as a low rank regularizer. We prove theoretically that under certain assumptions our method achieves a high-quality local optimal solution efficiently. Experimental results on synthetic and real data show that the proposed ISTRA algorithm outperforms state-of-the-art methods in both accuracy and efficiency.