MPI Leipzig

The aim of applied topology is to use and develop topological methods for applied mathematics, science and engineering. One of the main tools is persistent homology, an adaptation of classical homology, which assigns a barcode, i.e., a collection of intervals, to a finite metric space. Because of the nature of the invariant, barcodes are not well adapted for use by practitioners in machine learning tasks. We can circumvent this problem by assigning numerical quantities to barcodes, and these outputs can then be used as input to standard algorithms. I will explain how we can use tropical-like functions to coordinatize the space of persistence barcodes. These coordinates are stable with respect to the bottleneck and Wasserstein distances. I will also show how they can be used in practice.

Tensor numerical methods provide the efficient separable representation of multivariate functions and operators discretized on large $n^{\otimes d}$-grids, providing a base for the solution of $d$-dimensional PDEs with linear complexity scaling in the dimension, $O(d n)$. Modern methods of separable approximation combine the canonical, Tucker, matrix product states (MPS) and tensor train (TT) low-parametric data formats.

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The recent quantized-TT (QTT) approximation method is proven to provide the logarithmic data-compression on a wide class of functions and operators. Furthermore, QTT-approximation makes it possible to represent multi-dimensional steady-state and dynamical equations in quantized tensor spaces with the log-volume complexity scaling in the full-grid size, $O(d \log n)$, instead of $O(n^d)$.

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We show how the grid-based tensor approximation in quantized tensor spaces applies to super-compressed representation of functions and operators (super-fast convolution and FFT, spectrally close preconditioners) as well to hard problems arising in electronic structure calculations, such as multi-dimensional convolution, and two-electron integrals factorization in the framework of Hartree-Fock calculations. The QTT method also applies to the time-dependent molecular Schr{\"o}dinger, Fokker-Planck and chemical master equations.

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Numerical tests are presented indicating the efficiency of tensor methods in approximation of functions, operators and PDEs in many dimensions.

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http://personal-homepages.mis.mpg.de/bokh