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Software package for mosaic-Hankel structured low-rank approximation
Book Contribution - Book Chapter Conference Contribution
This paper presents the SLRA package (http://slra.github.io)-C software with interface to MATLAB, Octave, and R for solving low-rank approximation problems with the following features: mosaic Hankel structured approximating matrix, weighted 2-norm approximation criterion, and fixed and missing elements in the approximating matrix.
The package has applications in system identification, machine learning, and computer algebra. The paper gives an overview of the features of the package, including the wrapper functions for system identification (IDENT package) and approximate greatest common divisor (AGCD) computations. The addendum to the paper, available from http://homepages.vub.ac.be/ imarkovs/slra-demo, includes examples that demonstrate the usage, versatility, and efficiency of the software.
The package has applications in system identification, machine learning, and computer algebra. The paper gives an overview of the features of the package, including the wrapper functions for system identification (IDENT package) and approximate greatest common divisor (AGCD) computations. The addendum to the paper, available from http://homepages.vub.ac.be/ imarkovs/slra-demo, includes examples that demonstrate the usage, versatility, and efficiency of the software.
Book: IEEE Conference on Decision and Control
Series: Proceedings of the IEEE Conference on Decision and Control
Pages: 7165-7170
Number of pages: 6
ISBN:978-1-7281-1397-5
Publication year:2019
Keywords:System identification, low-rank approximation, mosaic Hankel matrix, missing data, approximate greatest common factor, software
Accessibility:Closed