Zobrazeno 1 - 10
of 7 535
pro vyhledávání: '"A. Sadok"'
Autor:
Dutta, Siddhant, Karanth, Pavana P, Xavier, Pedro Maciel, de Freitas, Iago Leal, Innan, Nouhaila, Yahia, Sadok Ben, Shafique, Muhammad, Neira, David E. Bernal
The widespread deployment of products powered by machine learning models is raising concerns around data privacy and information security worldwide. To address this issue, Federated Learning was first proposed as a privacy-preserving alternative to c
Externí odkaz:
http://arxiv.org/abs/2409.11430
In this paper, a novel augmented Lagrangian preconditioner based on global Arnoldi for accelerating the convergence of Krylov subspace methods applied to linear systems of equations with a block three-by-three structure, these systems typically arise
Externí odkaz:
http://arxiv.org/abs/2409.02652
The growing computational demands of artificial intelligence (AI) in addressing climate change raise significant concerns about inefficiencies and environmental impact, as highlighted by the Jevons paradox. We propose an attention-enhanced quantum ph
Externí odkaz:
http://arxiv.org/abs/2409.01626
Financial market prediction and optimal trading strategy development remain challenging due to market complexity and volatility. Our research in quantum finance and reinforcement learning for decision-making demonstrates the approach of quantum-class
Externí odkaz:
http://arxiv.org/abs/2408.03088
Autor:
AlSharawi, Ziyad, Kallel, Sadok
We consider the Ricker model with delay and constant or periodic stocking. We found that the high stocking density tends to neutralize the delay effect on stability. Conditions are established on the parameters to ensure the global stability of the e
Externí odkaz:
http://arxiv.org/abs/2407.21551
Autor:
Kallel, Sadok
This extensive survey is an invited contribution to the Encyclopedia of Mathematical Physics, 2nd edition. It covers both classical and more modern aspects of configuration spaces of points on a "ground space" $M$. Most results pertain to $M$ a manif
Externí odkaz:
http://arxiv.org/abs/2407.11092
A fully stochastic second-order adaptive-regularization method for unconstrained nonconvex optimization is presented which never computes the objective-function value, but yet achieves the optimal $\mathcal{O}(\epsilon^{-3/2})$ complexity bound for f
Externí odkaz:
http://arxiv.org/abs/2407.08018
A parametric class of trust-region algorithms for constrained nonconvex optimization is analyzed, where the objective function is never computed. By defining appropriate first-order stationarity criteria, we are able to extend the Adagrad method to t
Externí odkaz:
http://arxiv.org/abs/2406.15793
We consider $k$-dimensional discrete-time systems of the form $x_{n+1}=F(x_n,\ldots,x_{n-k+1})$ in which the map $F$ is continuous and monotonic in each one of its arguments. We define a partial order on $\mathbb{R}^{2k}_+$, compatible with the monot
Externí odkaz:
http://arxiv.org/abs/2402.14127
We have presented a fast method for solving a specific type of block four-by-four saddlepoint problem arising from the finite element discretization of the generalized 3D Stokes problem. We analyze the eigenvalue distribution and the eigenvectors of
Externí odkaz:
http://arxiv.org/abs/2402.13373