Sergey Dolgov

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 40

Top Topics

Decomposition Methods

Constrained Optimization

Top Venues

SIAM J. Sci. Comput.

SIAM/ASA J. Uncertain. Quantification

Appl. Math. Comput.

Publications

Sergey Dolgov, Dmitry V. Savostyanov
Tensor product approach to modelling epidemics on networks. Appl. Math. Comput. 460 (2024)
Sergey Dolgov, Dmitry V. Savostyanov
Tensor product algorithms for inference of contact network from epidemiological data. CoRR (2024)
Tiangang Cui, Sergey Dolgov, Robert Scheichl
Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events. SIAM J. Sci. Comput. 46 (1) (2024)
V. Abronin, A. Naumov, D. Mazur, D. Bystrov, K. Tsarova, Artem Melnikov, Ivan V. Oseledets, Sergey Dolgov, R. Brasher, Michael Perelshtein
TQCompressor: improving tensor decomposition methods in neural networks via permutations. CoRR (2024)
Harbir Antil, Sergey Dolgov, Akwum Onwunta
TTRISK: Tensor train decomposition algorithm for risk averse optimization. Numer. Linear Algebra Appl. 30 (3) (2023)
Harbir Antil, Sergey Dolgov, Akwum Onwunta
State-constrained Optimization Problems under Uncertainty: A Tensor Train Approach. CoRR (2023)
Kirandeep Kour, Sergey Dolgov, Peter Benner, Martin Stoll, Max Pfeffer
A weighted subspace exponential kernel for support tensor machines. CoRR (2023)
Egor Kornev, Sergey Dolgov, Karan Pinto, Markus Pflitsch, Michael Perelshtein, Artem Melnikov
Numerical solution of the incompressible Navier-Stokes equations for chemical mixers via quantum-inspired Tensor Train Finite Element Method. CoRR (2023)
Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner
Efficient Structure-preserving Support Tensor Train Machine. J. Mach. Learn. Res. 24 (2023)
Sergey Dolgov, Dante Kalise, Luca Saluzzi
Data-Driven Tensor Train Gradient Cross Approximation for Hamilton-Jacobi-Bellman Equations. SIAM J. Sci. Comput. 45 (5) (2023)
Sergey Dolgov, Dante Kalise, Luca Saluzzi
Statistical Proper Orthogonal Decomposition for model reduction in feedback control. CoRR (2023)
Tiangang Cui, Sergey Dolgov, Olivier Zahm
Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction. J. Comput. Phys. 485 (2023)
Tiangang Cui, Sergey Dolgov, Olivier Zahm
Self-reinforced polynomial approximation methods for concentrated probability densities. CoRR (2023)
Sergey Dolgov, Dante Kalise, Luca Saluzzi
Data-driven Tensor Train Gradient Cross Approximation for Hamilton-Jacobi-Bellman Equations. CoRR (2022)
Nikita Gourianov, Michael Lubasch, Sergey Dolgov, Quincy Y. van den Berg, Hessam Babaee, Peyman Givi, Martin Kiffner, Dieter Jaksch
A quantum-inspired approach to exploit turbulence structures. Nat. Comput. Sci. 2 (1) (2022)
Tiangang Cui, Sergey Dolgov, Robert Scheichl
Deep importance sampling using tensor-trains with application to a priori and a posteriori rare event estimation. CoRR (2022)
Tiangang Cui, Sergey Dolgov
Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports. Found. Comput. Math. 22 (6) (2022)
Paul B. Rohrbach, Sergey Dolgov, Lars Grasedyck, Robert Scheichl
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format. SIAM/ASA J. Uncertain. Quantification 10 (1) (2022)
Sergey Dolgov, Dante Kalise, Luca Saluzzi
Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control. CoRR (2022)
Oliver Townsend, Silvia Gazzola, Sergey Dolgov, Paul Quinn
Undersampling Raster Scans in Spectromicroscopy for reduced dose and faster measurements. CoRR (2022)
Sergey Dolgov, Daniel Kressner, Christoph Strössner
Functional Tucker Approximation Using Chebyshev Interpolation. SIAM J. Sci. Comput. 43 (3) (2021)
Tiangang Cui, Sergey Dolgov, Olivier Zahm
Conditional Deep Inverse Rosenblatt Transports. CoRR (2021)
Harbir Antil, Sergey Dolgov, Akwum Onwunta
TTRISK: Tensor Train Decomposition Algorithm for Risk Averse Optimization. CoRR (2021)
Sergey Dolgov, Dante Kalise, Karl Kunisch
Tensor Decomposition Methods for High-dimensional Hamilton-Jacobi-Bellman Equations. SIAM J. Sci. Comput. 43 (3) (2021)
Tiangang Cui, Sergey Dolgov
Deep Composition of Tensor Trains using Squared Inverse Rosenblatt Transports. CoRR (2020)
Paul B. Rohrbach, Sergey Dolgov, Lars Grasedyck, Robert Scheichl
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format. CoRR (2020)
Alexandra Bünger, Sergey Dolgov, Martin Stoll
A Low-Rank Tensor Method for PDE-Constrained Optimization with Isogeometric Analysis. SIAM J. Sci. Comput. 42 (1) (2020)
Sergey Dolgov, Dmitry V. Savostyanov
Parallel cross interpolation for high-precision calculation of high-dimensional integrals. Comput. Phys. Commun. 246 (2020)
Sergey Dolgov, Daniel Kressner, Christoph Strössner
Functional Tucker approximation using Chebyshev interpolation. CoRR (2020)
Sergey Dolgov, Karim Anaya-Izquierdo, Colin Fox, Robert Scheichl
Approximation and sampling of multivariate probability distributions in the tensor train decomposition. Stat. Comput. 30 (3) (2020)
Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner
Efficient Structure-preserving Support Tensor Train Machine. CoRR (2020)
Sergey Dolgov, Robert Scheichl
A Hybrid Alternating Least Squares-TT-Cross Algorithm for Parametric PDEs. SIAM/ASA J. Uncertain. Quantification 7 (1) (2019)
Sergey Dolgov, Dante Kalise, Karl Kunisch
A Tensor Decomposition Approach for High-Dimensional Hamilton-Jacobi-Bellman Equations. CoRR (2019)
Tobias Breiten, Sergey Dolgov, Martin Stoll
Solving differential Riccati equations: A nonlinear space-time method using tensor trains. CoRR (2019)
Sergey Dolgov, John W. Pearson
Preconditioners and Tensor Product Solvers for Optimal Control Problems from Chemotaxis. SIAM J. Sci. Comput. 41 (6) (2019)
Sergey Dolgov, Vladimir A. Kazeev, Boris N. Khoromskij
Direct tensor-product solution of one-dimensional elliptic equations with parameter-dependent coefficients. Math. Comput. Simul. 145 (2018)
Sergey Dolgov, Martin Stoll
Low-Rank Solution to an Optimization Problem Constrained by the Navier-Stokes Equations. SIAM J. Sci. Comput. 39 (1) (2017)
Peter Benner, Sergey Dolgov, Venera Khoromskaia, Boris N. Khoromskij
Fast iterative solution of the Bethe-Salpeter eigenvalue problem using low-rank and QTT tensor approximation. J. Comput. Phys. 334 (2017)
Sergey Dolgov, John W. Pearson, Dmitry V. Savostyanov, Martin Stoll
Fast tensor product solvers for optimization problems with fractional differential equations as constraints. Appl. Math. Comput. 273 (2016)
Simon Etter, Sergey Dolgov, Boris N. Khoromskij
Erratum: Two-Level QTT-Tucker Format for Optimized Tensor Calculus. SIAM J. Matrix Anal. Appl. 37 (2) (2016)
Sergey Dolgov, Boris N. Khoromskij
Simultaneous state-time approximation of the chemical master equation using tensor product formats. Numer. Linear Algebra Appl. 22 (2) (2015)
Sergey Dolgov, Boris N. Khoromskij, Alexander Litvinenko, Hermann G. Matthies
Polynomial Chaos Expansion of Random Coefficients and the Solution of Stochastic Partial Differential Equations in the Tensor Train Format. SIAM/ASA J. Uncertain. Quantification 3 (1) (2015)
Sergey Dolgov, Dmitry V. Savostyanov
Alternating Minimal Energy Methods for Linear Systems in Higher Dimensions. SIAM J. Sci. Comput. 36 (5) (2014)
Sergey Dolgov, Boris N. Khoromskij
Two-Level QTT-Tucker Format for Optimized Tensor Calculus. SIAM J. Matrix Anal. Appl. 34 (2) (2013)
Sergey Dolgov, Dmitry V. Savostyanov
Corrected One-Site Density Matrix Renormalization Group and Alternating Minimal Energy Algorithm. ENUMATH (2013)