Publication | Closed Access
On a Variational Formulation of the Generalized Singular Value Decomposition
24
Citations
7
References
1997
Year
Mathematical ProgrammingGsvd VectorsEngineeringGeneralized FunctionVariational AnalysisMatrix AnalysisMultilinear Subspace LearningInverse ProblemsDuality TheoryGsvd Duality TheoryMatrix TheoryFunctional AnalysisRegularization (Mathematics)Matrix MethodApproximation TheoryLow-rank ApproximationVariational Formulation
A variational formulation for the generalized singular value decomposition (GSVD) of a pair of matrices $A \in R^{m \times n}$ and $B \in R^{p \times n}$ is presented. In particular, a duality theory analogous to that of the SVD provides new understanding of left and right generalized singular vectors. It is shown that the intersection of row spaces of A and B plays a key role in the GSVD duality theory. The main result that characterizes left GSVD vectors involves a generalized singular value deflation process.
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