Stochastic Systems Group
Home Research Group Members Programs  
Demos Calendar Publications Mission Statement Alumni

SSG Seminar Abstract

Matrix Decompositions for Model and System Identification

Venkat Chandrasekaran

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.

Problems with this site should be emailed to