Publication | Open Access
Globally Convergent Jacobi-Type Algorithms for Simultaneous Orthogonal Symmetric Tensor Diagonalization
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Citations
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References
2018
Year
Numerical AnalysisGlobal ConvergenceEngineeringSimultaneous Orthogonal DiagonalizationConvergent Jacobi-type AlgorithmsMultilinear Subspace Learning3Rd-order TensorsInverse ProblemsMatrix MethodMatrix TheoryFunctional AnalysisMatrix AnalysisApproximation TheoryLow-rank Approximation
In this paper, we consider a family of Jacobi-type algorithms for a simultaneous orthogonal diagonalization problem of symmetric tensors. For the Jacobi-based algorithm of [M. Ishteva, P.-A. Absil, and P. Van Dooren, SIAM J. Matrix Anal. Appl., 34 (2013), pp. 651--672], we prove its global convergence for simultaneous orthogonal diagonalization of symmetric matrices and 3rd-order tensors. We also propose a new Jacobi-based algorithm in the general setting and prove its global convergence for sufficiently smooth functions.
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