Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Nurbekyan, Levon"'
Autor:
Fung, Samy Wu, Nurbekyan, Levon
Control Barrier Functions (CBFs) are an effective methodology to ensure safety and performative efficacy in real-time control applications such as power systems, resource allocation, autonomous vehicles, robotics, etc. This approach ensures safety in
Externí odkaz:
http://arxiv.org/abs/2409.18945
Mean-field control (MFC) problems aim to find the optimal policy to control massive populations of interacting agents. These problems are crucial in areas such as economics, physics, and biology. We consider the non-local setting, where the interacti
Externí odkaz:
http://arxiv.org/abs/2405.10922
We propose a monotone splitting algorithm for solving a class of second-order non-potential mean-field games. Following [Achdou, Capuzzo-Dolcetta, "Mean Field Games: Numerical Methods," SINUM (2010)], we introduce a finite-difference scheme and obser
Externí odkaz:
http://arxiv.org/abs/2403.20290
Autor:
Nurbekyan, Levon
The note contains a direct extension of the Chambolle and Pock convergence proof of the primal-dual hybrid gradient (PDHG) algorithm to the case of monotone inclusions.
Externí odkaz:
http://arxiv.org/abs/2311.03689
We propose an adaptive step size with an energy approach for a suitable class of preconditioned gradient descent methods. We focus on settings where the preconditioning is applied to address the constraints in optimization problems, such as the Hessi
Externí odkaz:
http://arxiv.org/abs/2310.06733
Autor:
Negrini, Elisa, Nurbekyan, Levon
In this work, we investigate applications of no-collision transportation maps introduced in [Nurbekyan et. al., 2020] in manifold learning for image data. Recently, there has been a surge in applying transportation-based distances and features for da
Externí odkaz:
http://arxiv.org/abs/2304.00199
A normalizing flow (NF) is a mapping that transforms a chosen probability distribution to a normal distribution. Such flows are a common technique used for data generation and density estimation in machine learning and data science. The density estim
Externí odkaz:
http://arxiv.org/abs/2211.16757
In this work, we consider a novel inverse problem in mean-field games (MFG). We aim to recover the MFG model parameters that govern the underlying interactions among the population based on a limited set of noisy partial observations of the populatio
Externí odkaz:
http://arxiv.org/abs/2204.04851
Publikováno v:
J. Comput. Phys., 459(C), jun 2022
We propose an efficient solution approach for high-dimensional nonlocal mean-field game (MFG) systems based on the Monte Carlo approximation of interaction kernels via random features. We avoid costly space-discretizations of interaction terms in the
Externí odkaz:
http://arxiv.org/abs/2202.12529
We propose efficient numerical schemes for implementing the natural gradient descent (NGD) for a broad range of metric spaces with applications to PDE-based optimization problems. Our technique represents the natural gradient direction as a solution
Externí odkaz:
http://arxiv.org/abs/2202.06236