Tensorized Krylov subspace methods: Algorithms, analysis, and applications

Data Seminario: 
Ora Inizio: 
Ora Fine: 
Daniel Kressner

Tensorized Krylov subspace methods are a versatile tool in numerical linear algebra for addressing large-scale applications that involve tensor product structure. This includes the discretization of high-dimensional PDEs, the solution of linear matrix equations, as well as low-rank updates and Frechet derivatives for matrix functions. This talk gives an overview of such methods, discusses their theoretical properties, and highlights applications.