Research

Our research focuses on low-rank tensor approximation and its applications, with emphasis on numerical tensor calculus, nonlinear optimization methods, underlying approximability principles, as well as algebraic and geometric foundations. Some keywords:

• Tensors: geometry of low-rank varieties, tensor networks, higher-order singular values, tensor product operators
• Approximation: functional analytic foundations, approximation rates, singular value estimates, spectral and nuclear norm
• Optimization: block coordinate methods, truncated iterations, Riemannian optimization, rank adaptivity
• Applications: high-dimensional linear equations and eigenvalue problems, signal processing, tensor completion

• Kuznetsov, Maxim (30.04.2019 - 04.05.2019)
  • Affiliation: Skolkovo Institute of Science and Technology (Moskau), Center for Computational and Data-Intensive Science and Engineering, Russia
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