|Stochastic Systems Group|
Matrix Decompositions for Model and System Identification
We discuss some formulations for decomposing a matrix into components that have certain desirable properties (e.g., low-rank). Some of these methods permit efficient approximate solution based on convex optimization. We highlight the potential applicability of these approaches to problems in model and system identification through simulation results.
This is joint work with Sujay Sanghavi and Alan Willsky.
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