Robust Identification of 'Sparse Plus Low-rank' Graphical Models: An Optimization Approach
| Autor: | Mattia Zorzi, Valentina Ciccone, Augusto Ferrante |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Optimization problem Computer science Covariance matrix Covariance matrices 020206 networking & telecommunications 02 engineering and technology Positive-definite matrix Graph theory Matrix (mathematics) Matrix decomposition 020901 industrial engineering & automation Robustness (computer science) Optimization and Control (math.OC) Sparse matrices 0202 electrical engineering electronic engineering information engineering FOS: Mathematics Graphical model Variational analysis Graphical models Covariance matrices Graph theory Sparse matrices Robust identification Graphical models Matrix decomposition Robust identification Algorithm Mathematics - Optimization and Control |
| Zdroj: | CDC |
| Popis: | Motivated by graphical models, we consider the "Sparse Plus Low-rank" decomposition of a positive definite concentration matrix -- the inverse of the covariance matrix. This is a classical problem for which a rich theory and numerical algorithms have been developed. It appears, however, that the results rapidly degrade when, as it happens in practice, the covariance matrix must be estimated from the observed data and is therefore affected by a certain degree of uncertainty. We discuss this problem and propose an alternative optimization approach that appears to be suitable to deal with robustness issues in the "Sparse Plus Low-rank" decomposition problem.The variational analysis of this optimization problem is carried over and discussed. |
| Databáze: | OpenAIRE |
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