Streaming randomized techniques for low-rank approximation of tensors with applications

Alberto Bucci develops single-pass randomized and streaming low-rank approximation, beginning with large matrices and the strengths and limitations of streaming algorithms. The discussion extends to Tucker, tensor-train, and tree tensor-network representations. Tensor-train approximations are then incorporated into Krylov solvers, including sketched GMRES, to reduce expensive intermediate contractions. The framework is presented as applicable beyond tensor trains to other tensor-network architectures.