Publications associées à Convergence simple

Data-Driven Convergence Prediction of Astrobots Swarms

Denis Gillet, Jean-Paul Richard Kneib, Matin Macktoobian, Francesco Basciani

Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired observations. Such coordination\footnote{\z{A coordination sa ...

2021

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

This article studies a class of nonsmooth decentralized multiagent optimization problems where the agents aim at minimizing a sum of local strongly-convex smooth components plus a common nonsmooth term. We propose a general primal-dual algorithmic framewor ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Nicolas Henri Bernard Flammarion, Xiang Cheng

We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective functional. We show that an under-damped form of the Langevin algorithm perfor ...

INT STATISTICAL INST2021

Coupling-dependent convergence behavior of phase oscillators with tegotae-control

Auke Ijspeert, Jonathan Patrick Arreguit O'Neill, Simon Lukas Hauser, Matthieu Guillaume Marie Dujany

A bio-inspired way to model locomotion is using a network of coupled phase oscillators to create a Central Pattern Generator (CPG). The recently developed feedback control method tegotae includes exteroceptive force feedback into the governing phase update ...

IEEE2021

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

Volkan Cevher, Ali Kavis, Shaul Nadav Hallak

This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm’s convergence properties in non-convex problems. We first show that the sequence of iterates generated by SGD remains bounded and converges with prob ...

2020

Variance-Reduced Stochastic Learning Under Random Reshuffling

Ali H. Sayed, Bicheng Ying, Kun Yuan

Several useful variance-reduced stochastic gradient algorithms, such as SVRG, SAGA, Finito, and SAG, have been proposed to minimize empirical risks with linear convergence properties to the exact minimizer. The existing convergence results assume uniform d ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Strong convergence of multivariate maxima

Simone Padoan, Stefano Rizzelli

It is well known and readily seen that the maximum of n independent and uniformly on [0, 1] distributed random variables, suitably standardised, converges in total variation distance, as n increases, to the standard negative exponential distribution. We ex ...

CAMBRIDGE UNIV PRESS2020

Pointwise error estimate in difference setting for the two-dimensional nonlinear fractional complex Ginzburg-Landau equation

Jan Sickmann Hesthaven, Qifeng Zhang

In this paper, we propose a three-level linearized implicit difference scheme for the two-dimensional spatial fractional nonlinear complex Ginzburg-Landau equation. We prove that the difference scheme is uniquely solvable, stable and convergent under mild ...

2020

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Convergence simple

Data-Driven Convergence Prediction of Astrobots Swarms

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Coupling-dependent convergence behavior of phase oscillators with tegotae-control

On the nonlinear Dirichlet-Neumann method and preconditioner for Newton's method

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

On the asymptotic behavior of the solutions to parabolic variational inequalities

Variance-Reduced Stochastic Learning Under Random Reshuffling

Strong convergence of multivariate maxima

Pointwise error estimate in difference setting for the two-dimensional nonlinear fractional complex Ginzburg-Landau equation

Graph Chatbot

Chattez avec Graph Search

Variance-Reduced Stochastic Learning Under Random Reshuffling

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

On the asymptotic behavior of the solutions to parabolic variational inequalities

Pointwise error estimate in difference setting for the two-dimensional nonlinear fractional complex Ginzburg-Landau equation

Data-Driven Convergence Prediction of Astrobots Swarms

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Coupling-dependent convergence behavior of phase oscillators with tegotae-control

On the nonlinear Dirichlet-Neumann method and preconditioner for Newton's method

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

Strong convergence of multivariate maxima