Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems

Interesting review of the TT approach:

Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems by Andrzej Cichocki

In this paper we review basic and emerging models and associated algorithms for large-scale tensor networks, especially Tensor Train (TT) decompositions using novel mathematical and graphical representations. We discus the concept of tensorization (i.e., creating very high-order tensors from lower-order original data) and super compression of data achieved via quantized tensor train (QTT) networks. %The purpose of a tensorization and quantization is to achieve, via low-rank tensor approximations %"super" compression, and meaningful, compact representation of structured data. The main objective of this paper is to show how tensor networks can be used to solve a wide class of big data optimization problems (that are far from tractable by classical numerical methods) by applying tensorization and performing all operations using relatively small size matrices and tensors and applying iteratively optimized and approximative tensor contractions.

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