Efficient Structured Matrix Rank Minimization
For practitioners in signal processing and control, this provides a faster alternative to existing nuclear norm regularization methods.
The paper introduces a new method for structured matrix rank minimization that avoids full SVD, augmented Lagrangian, and per-iteration linear systems, achieving significantly faster running times while effectively recovering low-rank solutions in stochastic system realization and spectral compressed sensing.
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.