3 papers
math.OC2025
High-dimensional Optimization with Low Rank Tensor Sampling and Local Search
Konstantin Sozykin, Andrei Chertkov, Anh-Huy Phan +2
We present a novel method called TESALOCS (TEnsor SAmpling and LOCal Search) for multidimensional optimization, combining the strengths of gradient-free discrete methods and gradie…
cs.LG2024
Black-Box Approximation and Optimization with Hierarchical Tucker Decomposition
Gleb Ryzhakov, Andrei Chertkov, Artem Basharin +1
We develop a new method HTBB for the multidimensional black-box approximation and gradient-free optimization, which is based on the low-rank hierarchical Tucker decomposition with…
math.NA2022
Black box approximation in the tensor train format initialized by ANOVA decomposition
Andrei Chertkov, Gleb Ryzhakov, Ivan Oseledets
Surrogate models can reduce computational costs for multivariable functions with an unknown internal structure (black boxes). In a discrete formulation, surrogate modeling is equiv…