Structure of the Hessian matrix and an economical implementation of Newton’s method in the problem of canonical approximation of tensors

By Vladimir A. Kazeev, E. E. Tyrtyshnikov. 2010

10 citations recorded by the source corpus.

Subfields: Computational Mathematics, Computational Theory and Mathematics, Numerical Analysis

How researchers see this paper

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

In Computational Mathematics

1. Yun Raymond Fu
2. Chengcheng Jia
3. Tae-Kyun Kim
4. Zhiyuan Shi

In Computational Theory and Mathematics

1. Yun Raymond Fu

In Numerical Analysis

1. Guoqiang Zhong
2. Yun Raymond Fu

Citations in the corpus

• A literature survey of low‐rank tensor approximation techniques (2013, neutral): “…recent developments for general tensors include work on increasing the efficiency and robustness of gradient-based and Newton-like methods [2,72,74,140,223,224], modifying and improving ALS (alternating least squares) [41, 57, 58, 90, 225], studying the convergence of ALS [51, 194, 253] and…”
• Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery (2014, background): “…tensor approximation approaches have been proposed as well, which, in general, estimate a rank-one tensor via vector outer production (Espig 2007; Kazeev and Tyrtyshnikov 2010; Acar, Dunlavy, and Kolda 2011; Espig and Hackbusch 2012; Phan, Anh Huy and Tichavskỳ, Petr and Cichocki, Andrzej 2012;…”