Computing Inner Eigenvalues of Matrices in Tensor Train Matrix Format

By Thomas Mach. 2013

18 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mathematics, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hoang Duong Tuan
2. Minh N. Do
3. Ho N. Phien
4. Johann A. Bengua

In Computational Mathematics

1. Hoang Duong Tuan
2. Minh N. Do
3. Ho N. Phien
4. Johann A. Bengua
5. Shutao Li
6. Leyuan Fang
7. Renwei Dian

In Computational Theory and Mathematics

1. Hoang Duong Tuan
2. Minh N. Do
3. Ho N. Phien
4. Johann A. Bengua

Citations in the corpus

• A literature survey of low‐rank tensor approximation techniques (2013, neutral): “…power and Lanczos methods combined with CP and Tucker [11,114]; • a restarted Lanczos method combined with TT [126]; • conjugate gradient type-methods for symmetric eigenvalue problems combined with truncation for low-rank matrices [180], as well as for TT [187], HT [172] and QTT [181].”
• Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train (2016, methodology): “Low TT rank tensors were used for the singular value decomposition (SVD) of large-scale matrices in [32], [33].”