From the 1 of 15 linked papers with an AI index.
9 papers · 1 filter
Learning to control switching nonlinear systems with Koopman operator regression
Edoardo Caldarelli, Oleksii Kachaiev, Cesare Molinari +1
The paper proposes using Koopman operator regression in a reproducing kernel Hilbert space to identify and control nonlinear systems with finite action spaces, creating a linear sw…
Frank-Wolfe with Moreau Envelope Smoothing for Nonsmooth Nonconvex Problems
Antonio Silveti-Falls, Cesare Molinari, Zev Woodstock
We present and analyze Frank-Wolfe with Moreau Envelope Smoothing (FRAMES) for solving nonsmooth nonconvex constrained optimization problems, taking advantage of iterative smoothin…
Convergence of zeroth-order proximal point algorithms in the high-temperature regime
Emanuele Naldi, Hippolyte Labarrière, Cesare Molinari +1
Efficient methods for non-convex black-box optimization largely rely on sampling. In this context, the Zeroth-Order Proximal Operator (ZOPO) and the corresponding Zeroth-Order Prox…
Model Consistency of the Iterative Regularization of Dual Ascent for Low-Complexity Regularization
Jie Gao, Cesare Molinari, Silvia Villa +1
Regularization is a core component of modern inverse problems, as it helps establish the well-posedness of the solution of interest. Popular regularization approaches include varia…
A Structured Proximal Stochastic Variance Reduced Zeroth-order Algorithm
Marco Rando, Cheik Traoré, Cesare Molinari +2
Minimizing finite sums of functions is a central problem in optimization, arising in numerous practical applications. Such problems are commonly addressed using first-order optimiz…
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…