21 papers
A localized consensus-based sampling algorithm
Arne Bouillon, Alexander Bodard, Panagiotis Patrinos +2
We propose a localized consensus-based method for sampling from non-Gaussian distributions, a task that frequently arises when solving Bayesian inverse problems. Our method arises…
Parametric Nonconvex Optimization via Convex Surrogates
Renzi Wang, Panagiotis Patrinos, Alberto Bemporad
This paper presents a novel learning-based approach to construct a surrogate problem that approximates a given parametric nonconvex optimization problem. The surrogate function is…
Exact worst-case convergence rates of gradient descent: a complete analysis for all constant stepsizes over nonconvex and convex functions
Teodor Rotaru, François Glineur, Panagiotis Patrinos
We consider gradient descent with constant stepsizes and derive exact worst-case convergence rates on the minimum gradient norm of the iterates. Our analysis covers all possible st…
Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems
Ibrahim K. Ozaslan, Panagiotis Patrinos, Mihailo R. JovanoviÄ
We examine stability properties of primal-dual gradient flow dynamics for composite convex optimization problems with multiple, possibly nonsmooth, terms in the objective function…
EM++: A parameter learning framework for stochastic switching systems
Renzi Wang, Alexander Bodard, Mathijs Schuurmans +1
This paper proposes a general switching dynamical system model, and a custom majorization-minimization-based algorithm EM++ for identifying its parameters. For certain families of…
Anisotropic Proximal Point Algorithm
Emanuel Laude, Panagiotis Patrinos
In this paper we study a nonlinear dual space preconditioning approach for the relaxed Proximal Point Algorithm (PPA) with application to monotone and relatively cohypomonotone inc…