Projects per year
Personal profile
Research interests
Numerical analysis, linear and multilinear algebra, matrix and tensor analysis, fast algorithms, tensor product formats, dynamical problems, stochastic models
Education/Academic qualification
Mathematics, Doctor rerum naturalium, University of Leipzig
30 Jun 2011 → 20 Aug 2014
Fingerprint Dive into the research topics where Sergey Dolgov is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
Tensors
Engineering & Materials Science
Tensor Decomposition
Mathematics
Tensor Product
Mathematics
Tensor
Mathematics
tensors
Physics & Astronomy
Model Order Reduction
Mathematics
Minimal Energy
Mathematics
iterative solution
Physics & Astronomy
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Projects 2016 2018
- 1 Finished
Tensor product numerical methods for high-dimensional problems in probability and quantum calculations
1/01/16 → 31/12/18
Project: Research council
Research Output 2014 2020
11
Downloads
(Pure)
A hybrid Alternating Least Squares - TT Cross algorithm for parametric PDEs
Dolgov, S. & Scheichl, R., 2019, In : SIAM/ASA Journal on Uncertainty Quantification. 7, 1, p. 260-291 32 p.Research output: Contribution to journal › Article
Open Access
File
Tensors
Maximum entropy methods
Probability density function
Mathematical operators
Decomposition
Approximation and sampling of multivariate probability distributions in the tensor train decomposition
Dolgov, S., Anaya-Izquierdo, K., Fox, C. & Scheichl, R., 2 Nov 2019, In : Statistics and Computing.Research output: Contribution to journal › Article
Open Access
Tensor Decomposition
Multivariate Distribution
Rejection
Probability Distribution
Tensor
1
Citation
(Scopus)
4
Downloads
(Pure)
A Tensor Decomposition Algorithm for Large ODEs with Conservation Laws
Dolgov, S. V., 1 Jan 2019, In : Computational Methods in Applied Mathematics. 19, 1, p. 23-38 16 p.Research output: Contribution to journal › Article
Open Access
File
Tensor Decomposition
Decomposition Algorithm
Conservation Laws
Tensors
Conservation
Greedy low-rank algorithm for spatial connectome regression
Kürschner, P., Dolgov, S., Harris, K. D. & Benner, P., 26 Oct 2019, (Accepted/In press) In : The Journal of Mathematical Neuroscience.Research output: Contribution to journal › Article
Factorization
Brain
Experiments
Guaranteed a posteriori error bounds for low rank tensor approximate solutions
Dolgov, S. & Vejchodský, T., 21 May 2019, In : arXiv. 1905.08572Research output: Contribution to journal › Article
File