Zobrazeno 1 - 10
of 139
pro vyhledávání: '"Vaquero, Miguel"'
While the construction of symplectic integrators for Hamiltonian dynamics is well understood, an analogous general theory for Poisson integrators is still lacking. The main challenge lies in overcoming the singular and non-linear geometric behavior o
Externí odkaz:
http://arxiv.org/abs/2409.04342
This paper presents a general method to construct Poisson integrators, i.e., integrators that preserve the underlying Poisson geometry. We assume the Poisson manifold is integrable, meaning there is a known local symplectic groupoid for which the Poi
Externí odkaz:
http://arxiv.org/abs/2403.20139
Autor:
Peralta-Salas, Daniel, Vaquero, Miguel
In this work we study Beltrami fields with non-constant proportionality factor on $\mathbb{R}^3$. More precisely, we analyze the existence of vector fields $X$ satisfying the equations $curl(X)=fX$ and $div(X)=0$ for a given $f\in C^\infty(\mathbb R^
Externí odkaz:
http://arxiv.org/abs/2312.10511
This work presents a general geometric framework for simulating and learning the dynamics of Hamiltonian systems that are invariant under a Lie group of transformations. This means that a group of symmetries is known to act on the system respecting i
Externí odkaz:
http://arxiv.org/abs/2308.16331
Given a network of agents, we study the problem of designing a distributed algorithm that computes k independent weighted means of the network's initial conditions (namely, the agents agree on a k-dimensional space). Akin to average consensus, this p
Externí odkaz:
http://arxiv.org/abs/2208.08999
Autor:
Peralta-Salas, Daniel, Vaquero, Miguel
Publikováno v:
In Journal of Mathematical Analysis and Applications 15 December 2024 540(2)
This paper proposes a data-driven control framework to regulate an unknown, stochastic linear dynamical system to the solution of a (stochastic) convex optimization problem. Despite the centrality of this problem, most of the available methods critic
Externí odkaz:
http://arxiv.org/abs/2108.13040
This paper proposes a data-driven framework to solve time-varying optimization problems associated with unknown linear dynamical systems. Making online control decisions to regulate a dynamical system to the solution of an optimization problem is a c
Externí odkaz:
http://arxiv.org/abs/2103.16067
Autor:
Moya-Almeida, Vinicio, Diezma-Iglesias, Belén, Correa-Hernando, Eva, Vaquero-Miguel, Cristian, Alvarado-Arias, Natalia
Publikováno v:
In Engineering Applications of Artificial Intelligence January 2024 127 Part A
This paper tackles the problem of discretizing accelerated optimization flows while retaining their convergence properties. Inspired by the success of resource-aware control in developing efficient closed-loop feedback implementations on digital syst
Externí odkaz:
http://arxiv.org/abs/2009.09135