Software for weighted structured low-rank approximation

• Autores: I. Markovsky, Konstantin Usevich
• Localización: Journal of computational and applied mathematics, ISSN 0377-0427, Vol. 256, Nº 1, 2014, págs. 278-292
• Idioma: inglés
• DOI: 10.1016/j.cam.2013.07.048
• Texto completo no disponible (Saber más ...)
• Resumen

  • A software package is presented that computes locally optimal solutions to low-rank approximation problems with the following features:

    . mosaic Hankel structure constraint on the approximating matrix, . weighted 2-norm approximation criterion, . fixed elements in the approximating matrix, . missing elements in the data matrix, and . linear constraints on an approximating matrix�fs left kernel basis.

    It implements a variable projection type algorithm and allows the user to choose standard local optimization methods for the solution of the parameter optimization problem. For an m �~ n data matrix, with n > m, the computational complexity of the cost function and derivative evaluation is O(m2n). The package is suitable for applications with n �â m.

    In statistical estimation and data modeling . the main application areas of the package . n �â m corresponds to modeling of large amount of data by a low-complexity model.

    Performance results on benchmark system identification problems from the database DAISY and approximate common divisor problems are presented.