Tight computationally efficient approximation of matrix norms with applications

We address the problems of computing operator norms of matrices induced by given norms on the argument and the image space. It is known that aside of a fistful of “solvable cases”, most notably, the case when both given norms are Euclidean, computing operator norm of a matrix is NP-hard. We specify...

Full description

Saved in:
Bibliographic Details
Main Authors: Juditsky, Anatoli, Kotsalis, Georgios, Nemirovski, Arkadi
Format: Article
Language:English
Published: Université de Montpellier 2022-11-01
Series:Open Journal of Mathematical Optimization
Subjects:
Online Access:https://ojmo.centre-mersenne.org/articles/10.5802/ojmo.19/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We address the problems of computing operator norms of matrices induced by given norms on the argument and the image space. It is known that aside of a fistful of “solvable cases”, most notably, the case when both given norms are Euclidean, computing operator norm of a matrix is NP-hard. We specify rather general families of norms on the argument and the images space (“ellitopic” and “co-ellitopic”, respectively) allowing for reasonably tight computationally efficient upper-bounding of the associated operator norms. We extend these results to bounding “robust operator norm of uncertain matrix with box uncertainty”, that is, the maximum of operator norms of matrices representable as a linear combination, with coefficients of magnitude $\le 1$, of a collection of given matrices. Finally, we consider some applications of norm bounding, in particular, (1) computationally efficient synthesis of affine non-anticipative finite-horizon control of discrete time linear dynamical systems under bounds on the peak-to-peak gains, (2) signal recovery with uncertainties in sensing matrix, and (3) identification of parameters of time invariant discrete time linear dynamical systems via noisy observations of states and inputs on a given time horizon, in the case of “uncertain-but-bounded” noise varying in a box.
ISSN:2777-5860