815 citations · 3.2k across the 142 of their papers we have counts for
12 papers · 1 filter
Robust discretization in quantized tensor train format for elliptic problems in two dimensions
A. V. Chertkov, I. V. Oseledets, M. V. Rakhuba
In this work we propose an efficient black-box solver for two-dimensional stationary diffusion equations, which is based on a new robust discretization scheme. The idea is to formu…
What Lies Beneath the Surface: Topological-Shape Optimization With the Kernel-Independent Fast Multipole Method
Igor Ostanin, Ivan Tsybulin, Mikhail Litsarev +2
The paper presents a new method for shape and topology optimization based on an efficient and scalable boundary integral formulation for elasticity. To optimize topology, our appro…
Desingularization of bounded-rank matrix sets
Valentin Khrulkov, Ivan Oseledets
Conventional ways to solve optimization problems on low-rank matrix sets which appear in great number of applications ignore its underlying structure of an algebraic variety and ex…
Time- and memory-efficient representation of complex mesoscale potentials
Grigory Drozdov, Igor Ostanin, Ivan Oseledets
We apply the modern technique of approximation of multivariate functions - tensor train cross approximation - to the problem of the description of physical interactions between com…
Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering
Alexander Fonarev, Alexander Mikhalev, Pavel Serdyukov +2
Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new ite…
Low-Rank Tensor Networks for Dimensionality Reduction and Large-Scale Optimization Problems: Perspectives and Challenges PART 1
A. Cichocki, N. Lee, I. V. Oseledets +3
Machine learning and data mining algorithms are becoming increasingly important in analyzing large volume, multi-relational and multi--modal datasets, which are often conveniently…