1 citations · 1 across the 6 of their papers we have counts for
6 papers
Heavy-ball dynamics with Hessian-driven damping for non-convex optimization under the Łojasiewicz condition
Vassilis Apidopoulos, Vasiliki Mavrogeorgou, Theodoros G. Tsironis
In this paper, we examine the convergence properties of heavy-ball dynamics with Hessian-driven damping in smooth non-convex optimization problems satisfying a Łojasiewicz conditio…
Preconditioned primal-dual dynamics in convex optimization: non-ergodic convergence rates
Vassilis Apidopoulos, Cesare Molinari, Juan Peypouquet +1
We introduce and analyze a continuous primal-dual dynamical system in the context of the minimization problem , where and are convex functions and is a line…
Variance reduction techniques for stochastic proximal point algorithms
Cheik Traoré, Vassilis Apidopoulos, Saverio Salzo +1
In the context of finite sums minimization, variance reduction techniques are widely used to improve the performance of state-of-the-art stochastic gradient methods. Their practica…
Regularization properties of dual subgradient flow
Vassilis Apidopoulos, Cesare Molinari, Lorenzo Rosasco +1
Dual gradient descent combined with early stopping represents an efficient alternative to the Tikhonov variational approach when the regularizer is strongly convex. However, for ma…
Iterative regularization in classification via hinge loss diagonal descent
Vassilis Apidopoulos, Tomaso Poggio, Lorenzo Rosasco +1
Iterative regularization is a classic idea in regularization theory, that has recently become popular in machine learning. On the one hand, it allows to design efficient algorithms…
Convergence rates for the Heavy-Ball continuous dynamics for non-convex optimization, under Polyak-Łojasiewicz condition
Vassilis Apidopoulos, Nicolò Ginatta, Silvia Villa
We study convergence of the trajectories of the Heavy Ball dynamical system, with constant damping coefficient, in the framework of convex and non-convex smooth optimization. By us…