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

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. Keywords: Tensor networks, tensor train (TT) decompositions, matrix product states (MPS), matrix product operators (MPO), basic tensor operations, tensorization, distributed representation od data optimization problems for very large-scale problems: generalized eigenvalue decomposition (GEVD), PCA/SVD, canonical correlation analysis (CCA).Comment: arXiv admin note: text overlap with arXiv:1403.204

Core-Core Dynamics in Spin Vortex Pairs

We investigate magnetic nano-pillars, in which two thin ferromagnetic nanoparticles are separated by a nanometer thin nonmagnetic spacer and can be set into stable spin vortex-pair configurations. The 16 ground states of the vortex-pair system are characterized by parallel or antiparallel chirality and parallel or antiparallel core-core alignment. We detect and differentiate these individual vortex-pair states experimentally and analyze their dynamics analytically and numerically. Of particular interest is the limit of strong core-core coupling, which we find can dominate the spin dynamics in the system. We observe that the 0.2 GHz gyrational resonance modes of the individual vortices are replaced with 2-6 GHz range collective rotational and vibrational core-core resonances in the configurations where the cores form a bound pair. These results demonstrate new opportunities in producing and manipulating spin states on the nanoscale and may prove useful for new types of ultra-dense storage devices where the information is stored as multiple vortex-core configurations

Tensor-based dynamic mode decomposition

Dynamic mode decomposition (DMD) is a recently developed tool for the analysis of the behavior of complex dynamical systems. In this paper, we will propose an extension of DMD that exploits low-rank tensor decompositions of potentially high-dimensional data sets to compute the corresponding DMD modes and eigenvalues. The goal is to reduce the computational complexity and also the amount of memory required to store the data in order to mitigate the curse of dimensionality. The efficiency of these tensor-based methods will be illustrated with the aid of several different fluid dynamics problems such as the von K\'arm\'an vortex street and the simulation of two merging vortices