Variable projection methods for approximate GCD computations. This paper presents optimization methods and software for the approximate GCD problem of multiple univariate polynomials in the weighted 2-norm. Backward error minimization and Sylvester low-rank approximation formulations of the problem are solved by the variable projection method. Optimization methods are implemented in publicly available C++ software package with an interface to MATLAB. Results on computational complexity are presented.
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References in zbMATH (referenced in 12 articles , 1 standard article )
Showing results 1 to 12 of 12.
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- Usevich, Konstantin; Markovsky, Ivan: Optimization on a Grassmann manifold with application to system identification (2014)
- Usevich, Konstantin; Markovsky, Ivan: Variable projection for affinely structured low-rank approximation in weighted $2$-norms (2014)
- Markovsky, Ivan; Usevich, Konstantin: Structured low-rank approximation with missing data (2013)
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