3 citations · 4 across the 3 of their papers we have counts for
4 papers
Scalable Pseudospectral Analysis via Low-Rank Approximations of Dynamical Systems
Vladimir R. Kostic, Dragana Lj. Cvetkovic, Ljiljana Cvetkovic
Pseudospectral analysis is fundamental for quantifying the sensitivity and transient behavior of nonnormal matrices, yet its computational cost scales cubically with dimension, ren…
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic +3
The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems. Estimators such as principal component…
Learning invariant representations of time-homogeneous stochastic dynamical systems
Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi +2
We consider the general class of time-homogeneous stochastic dynamical systems, both discrete and continuous, and study the problem of learning a representation of the state that f…
The method of Bregman projections in deterministic and stochastic convex feasibility problems
Vladimir Kostic, Saverio Salzo
In this work we study the method of Bregman projections for deterministic and stochastic convex feasibility problems with three types of control sequences for the selection of sets…