Please, see also our software product teneva which provides a very compact implementation of basic operations in the TT-format.

Abstract: Multivariate Time Series Classification (MTSC) has important research significance and practical value. Deep learning models have achieved considerable success in addressing MTSC problems.

Recent advances in estimation techniques have underscored the growing importance of shrinkage estimation and balanced loss functions in the analysis of multivariate normal distributions. These ...

Introduction: We present Quantum Adaptive Search (QAGS), a hybrid quantum-classical algorithm for global optimization of multivariate functions. The method employs an adaptive mechanism that ...

Four new methods for classification of multivariate binary data are presented, based on an orthogonal expansion of the density in terms of discrete Fourier series. The performance of these methods in ...

For the preparation of high-dimensional functions on quantum computers, we introduce tensor network algorithms that are efficient with regard to dimensionality, optimize circuits composed of ...

In the paper, the authors extend a function arising from the Bernoulli trials in probability and involving the gamma function to its largest ranges, nd logarithmically complete monotonicity of these ...