2015/09/08 by Adams Wei Yu, Wanli Ma, Yu, Adams Wei +7
Engineering · #Advanced SAR Imaging Techniques #FOS: Electrical engineering #FOS: Mathematics #Microwave Imaging and Scattering Analysis #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1509.02447
openalex publication_date 2015/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study the problem of finding structured low-rank matrices using nuclear norm regularization where the structure is encoded by a linear map. In contrast to most known approaches for linearly structured rank minimization, we do not (a) use the full SVD, nor (b) resort to augmented Lagrangian techniques, nor (c) solve linear systems per iteration. Instead, we formulate the problem differently so that it is amenable to a generalized conditional gradient method, which results in a practical improvement with low per iteration computational cost. Numerical results show that our approach significantly outperforms state-of-the-art competitors in terms of running time, while effectively recovering low rank solutions in stochastic system realization and spectral compressed sensing problems.