Alternatives with stronger convergence than coordinate-descent iterative LMI algorithms
(2012) IEEE Transactions on Automatic Control — p. 0 (0) (2012)
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- In this note we aim at putting more emphasis on the fact that trying to solve non-convex optimization problems with coordinate-descent iterative linear matrix inequality algorithms leads to suboptimal solutions, and put forward other optimization methods better equipped to deal with such problems (having theoretical convergence guarantees and/or being more efficient in practice). This fact, already outlined at several places in the literature, still appears to be disregarded by a sizable part of the systems and control community. Thus, main elements on this issue and better optimization alternatives are presented and illustrated by means of an example. Preprint on http://arxiv.org/abs/1110.2615. For personal use only. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
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Simon, E., & Wertz, V. (2012). Alternatives with stronger convergence than coordinate-descent iterative LMI algorithms. IEEE Transactions on Automatic Control, 0 (0). https://hdl.handle.net/2078.5/160619 (Original work published 2012)