An algorithm for fast constrained nuclear norm minimization and applications to systems identification

This paper presents a novel algorithm for efficiently minimizing the nuclear norm of a matrix subject to structural and semi-definite constraints. It requires performing only thresholding and eigenvalue decomposition steps and converges Q-superlinearly to the optimum. Thus, this algorithm offers sub...

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Bibliographic Details
Published in2012 IEEE 51st IEEE Conference on Decision and Control (CDC) pp. 3469 - 3475
Main Authors Ayazoglu, M., Sznaier, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2012
Subjects
Computational modeling
Eigenvalues and eigenfunctions
Minimization
Noise
Noise measurement
Optimization
Vectors
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Summary:This paper presents a novel algorithm for efficiently minimizing the nuclear norm of a matrix subject to structural and semi-definite constraints. It requires performing only thresholding and eigenvalue decomposition steps and converges Q-superlinearly to the optimum. Thus, this algorithm offers substantial advantages, both in terms of memory requirements and computational time over conventional semi-definite programming solvers. These advantages are illustrated using as an example the problem of finding the lowest order system that interpolates a collection of noisy measurements.
ISBN:9781467320658
146732065X
ISSN:0191-2216
DOI:10.1109/CDC.2012.6426520