2 citations · 2 across the 12 of their papers we have counts for
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Empirical and computer-aided robustness analysis of long-step and accelerated methods in smooth convex optimization
Pierre Vernimmen, François Glineur
This work assesses both empirically and theoretically, using the performance estimation methodology, how robust different first-order optimization methods are when subject to relat…
Performance Estimation of second-order optimization methods on classes of univariate functions
Anne Rubbens, Nizar Bousselmi, Julien M. Hendrickx +1
We develop a principled approach to obtain exact computer-aided worst-case guarantees on the performance of second-order optimization methods on classes of univariate functions. We…
Worst-case convergence analysis of relatively inexact gradient descent on smooth convex functions
Pierre Vernimmen, François Glineur
We consider the classical gradient descent algorithm with constant stepsizes, where some error is introduced in the computation of each gradient. More specifically, we assume some…
Presolve techniques for quasi-convex chance constraints with finite-support low-dimensional uncertainty
Guillaume Van Dessel, François Glineur
Chance-constrained programs (CCP) represent a trade-off between conservatism and robustness in optimization. In many CCPs, one optimizes an objective under a probabilistic constrai…
Dual first-order methods for efficient computation of convex hull prices
Sofiane Tanji, Yassine Kamri, François Glineur +1
Convex Hull (CH) pricing, used in US electricity markets and raising interest in Europe, is a pricing rule designed to handle markets with non-convexities such as startup costs and…
Global and local approaches for the minimization of a sum of pointwise minima of convex functions
Guillaume Van Dessel, François Glineur
Numerous machine learning and industrial problems can be modeled as the minimization of a sum of so-called clipped convex functions (SCC), i.e. each term of the sum stems as th…
Global minimization of a minimum of a finite collection of functions
Guillaume Van Dessel, François Glineur
We consider the global minimization of a particular type of minimum structured optimization problems wherein the variables must belong to some basic set, the feasible domain is des…
Exact Convergence rate of the subgradient method by using Polyak step size
Moslem Zamani, François Glineur
This paper studies the last iterate of subgradient method with Polyak step size when applied to the minimization of a nonsmooth convex function with bounded subgradients. We show t…
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…
Improved convergence rates for the Difference-of-Convex algorithm
Teodor Rotaru, Panagiotis Patrinos, François Glineur
We consider a difference-of-convex formulation where one of the terms is allowed to be hypoconvex (or weakly convex). We first examine the precise behavior of a single iteration of…
Tight convergence rates of the gradient method on smooth hypoconvex functions
Teodor Rotaru, François Glineur, Panagiotis Patrinos
We perform the first tight convergence analysis of the gradient method with varying step sizes when applied to smooth hypoconvex (weakly convex) functions. Hypoconvex functions are…