GNMR: A Provable One-Line Algorithm for Low Rank Matrix Recovery

Low rank matrix recovery problems appear in a broad range of applications. In this work we present GNMR---an extremely simple iterative algorithm for low rank matrix recovery, based on a Gauss--Newton linearization. On the theoretical front, we derive recovery guarantees for GNMR in both matrix sensing and matrix completion settings. Some of these results improve upon the best currently known for other methods. A key property of GNMR is that it implicitly keeps the factor matrices approximately balanced throughout its iterations. On the empirical front, we show that for matrix completion with uniform sampling, GNMR performs better than several popular methods, especially when given very few observations close to the information limit.