NeurIPS 2020
### Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control

### Meta Review

The paper proposes a new method for learning a stable linear dynamical system from data, based on a recent paper [22] that shows that a matrix is stable if and only if it can be written as a product involving positive definite and orthogonal matrices. The proposed algorithm uses O(n^2) space in contrast to O(n^4) space used by previous similar methods, where n is the state dimension. The authors show that the new algorithm gets lower reconstruction error compared to baselines.
Reviewers recommend acceptance and weren't concerned that the paper relies heavily on [22]. I agree and I suggest it be accepted as a poster.