Titlebook: Low-Rank Approximation; Algorithms, Implemen Ivan Markovsky Book 2019Latest edition Springer International Publishing AG, part of Springer

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0178-5354 utions to the problem of data modelling by linear systems frThis book is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured

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Introduction,rank. The chapter proceeds with review of applications in systems and control, signal processing, computer algebra, chemometrics, psychometrics, machine learning, and computer vision that lead to low-rank approximation problems. Finally, classes of generic solution methods for solving low-rank appro

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From Data to Modelsodel. Important questions considered are: “How to represent a model by equations?” and “How to convert one representation into another one?” When two models fit the data equally well, we prefer the simpler model. For this purpose, we define the notion of model complexity. Two principles—misfit and l

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Exact Modeling of ARMAX identification splits into three subproblems: (1) identification of the deterministic part, (2) identification of the AR-part, and (3) identification of the MA-part. Subproblems 1 and?2 are equivalent to deterministic identification problem. The last topic considered in the chapter is comp

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