Activity: Talk or presentation › Talk at an external academic organisation
Description
Low-rank matrix approximations appear in a wide range of applications: signal and image processing, systems and control theory, symbolic-numeric computations, etc. In many cases, the matrices are structured (for example, Hankel/Toeplitz, block-Hankel, Sylvester, quasi-Hankel), and a low-rank approximation preserving the matrix structure is desirable. This problem is known as structured low-rank approximation, or SLRA.
The two-day workshop will feature invited talks by renowned experts in SLRA and related topics, including development of efficient algorithmic approaches to deal with these difficult nonconvex problems, analysis of their convergence and theoretical guarantees, convex relaxations of SLRA, applications of SLRA and relations to other techniques like tensor decompositions.