Conjugate gradient algorithms for best rank‐1 approximation of tensors

By O. Curtef, Gunther Dirr, Uwe Helmke. 2007

6 citations recorded by the source corpus.

Subfields: Computational Mathematics, Computational Theory and Mathematics, Radiology, Nuclear Medicine and Imaging

Citations in the corpus

• A literature survey of low‐rank tensor approximation techniques (2013, neutral): “Examples include nonlinear conjugate gradient or Newton-like methods on manifolds of low-rank matrices [35,191,198,257,258] or low-rank tensors [52,53,131,132,255], see also Section 3.4.”