Zobrazeno 1 - 10
of 873
pro vyhledávání: '"Bullo, Francesco"'
In this letter, we study the proximal gradient dynamics. This recently-proposed continuous-time dynamics solves optimization problems whose cost functions are separable into a nonsmooth convex and a smooth component. First, we show that the cost func
Externí odkaz:
http://arxiv.org/abs/2409.10664
The Deffuant-Weisbuch (DW) model is a well-known bounded-confidence opinion dynamics that has attracted wide interest. Although the heterogeneous DW model has been studied by simulations over $20$ years, its convergence proof is open. Our previous pa
Externí odkaz:
http://arxiv.org/abs/2409.01593
Recent studies on stability and contractivity have highlighted the importance of semi-inner products, which we refer to as ``pairings'', associated with general norms. A pairing is a binary operation that relates the derivative of a curve's norm to t
Externí odkaz:
http://arxiv.org/abs/2408.17350
Multistationarity - the existence of multiple equilibrium points - is a common phenomenon in dynamical systems from a variety of fields, including neuroscience, opinion dynamics, systems biology, and power systems. A recently proposed generalization
Externí odkaz:
http://arxiv.org/abs/2408.12790
In competitive resource allocation formulations multiple agents compete over different contests by committing their limited resources in them. For these settings, contest games offer a game-theoretic foundation to analyze how players can efficiently
Externí odkaz:
http://arxiv.org/abs/2408.00883
Autor:
Davydov, Alexander, Bullo, Francesco
Contraction theory is a mathematical framework for studying the convergence, robustness, and modularity properties of dynamical systems and algorithms. In this opinion paper, we provide five main opinions on the virtues of contraction theory. These o
Externí odkaz:
http://arxiv.org/abs/2404.11707
Autor:
Davydov, Alexander, Bullo, Francesco
In this letter, we investigate sufficient conditions for the exponential stability of LTI systems driven by controllers derived from parametric optimization problems. Our primary focus is on parametric projection controllers, namely parametric progra
Externí odkaz:
http://arxiv.org/abs/2403.08159
We analyze the convergence behavior of \emph{globally weakly} and \emph{locally strongly contracting} dynamics. Such dynamics naturally arise in the context of convex optimization problems with a unique minimizer. We show that convergence to the equi
Externí odkaz:
http://arxiv.org/abs/2403.07572
Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Linearized Contracting Dynamics (ELCD), the first neural network-based dyn
Externí odkaz:
http://arxiv.org/abs/2402.08090
Compressing large neural networks with minimal performance loss is crucial to enabling their deployment on edge devices. (Cho et al., 2022) proposed a weight quantization method that uses an attention-based clustering algorithm called differentiable
Externí odkaz:
http://arxiv.org/abs/2312.07759