【摘 要】
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Computing a few eigenpairs from large-scale symmetric eigenvalue problems is far beyond the tractability of classic eigensolvers when the storage of the eig
【出 处】
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2016年张量和矩阵学术研讨会(International conference on Tensor, Matrix a
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Computing a few eigenpairs from large-scale symmetric eigenvalue problems is far beyond the tractability of classic eigensolvers when the storage of the eigenvectors in the classical way is impossible. We consider a tractable case in which both the coeffcient matrix and its eigenvectors can be represented in the low-rank tensor train formats. We propose a subspace optimization method combined with some suitable truncation steps to the given low-rank Tensor Train formats. Its performance can be further improved if the alternating minimization method is used to refine the intermediate solutions lo-cally. Preliminary numerical experiments show that our algorithm is competitive to the state-of-the-art methods on problems arising from the discretization of the stationary Schrodinger equation.
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