@inproceedings{bc4a0111e1e4401d8856f268793b812c,
title = "The use of the r∗ heuristic in covariance completion problems",
abstract = "We consider a class of structured covariance completion problems which aim to complete partially known sample statistics in a way that is consistent with the underlying linear dynamics. The statistics of stochastic inputs are unknown and sought to explain the given correlations. Such inverse problems admit many solutions for the forcing correlations, but can be interpreted as an optimal low-rank approximation problem for identifying forcing models of low complexity. On the other hand, the quality of completion can be improved by utilizing information regarding the magnitude of unknown entries. We generalize theoretical results regarding the r∗ norm approximation and demonstrate the performance of this heuristic in completing partially available statistics using stochastically-driven linear models.",
keywords = "Convex optimization, k-support-norm, low-rank approximation, nuclear norm regularization, state covariances, structured matrix completion problems",
author = "Christian Grussler and Armin Zare and Jovanovi{\'c}, \{Mihailo R.\} and Anders Rantzer",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 55th IEEE Conference on Decision and Control, CDC 2016 ; Conference date: 12-12-2016 Through 14-12-2016",
year = "2016",
month = dec,
day = "27",
doi = "10.1109/CDC.2016.7798554",
language = "الإنجليزيّة",
series = "2016 IEEE 55th Conference on Decision and Control, CDC 2016",
pages = "1978--1983",
booktitle = "2016 IEEE 55th Conference on Decision and Control, CDC 2016",
}