6 citations · 8 across the 8 of their papers we have counts for
8 papers
On the Convex Interpolation for Linear Operators
Nizar Bousselmi, Zhicheng Deng, Jie Lu +2
The worst-case performance of an optimization method on a problem class can be analyzed using a finite description of the problem class, known as interpolation conditions. In this…
Numerical Design of Optimized First-Order Algorithms
Yassine Kamri, Julien M. Hendrickx, François Glineur
We derive several numerical methods for designing optimized first-order algorithms in unconstrained convex optimization settings. Our methods are based on the Performance Estimatio…
On the Worst-Case Analysis of Cyclic Block Coordinate Descent type Algorithms
Yassine Kamri, François Glineur, Julien M. Hendrickx +1
We study the worst-case behavior of Block Coordinate Descent (BCD) type algorithms for unconstrained minimization of coordinate-wise smooth convex functions. This behavior is indee…
Comparison of Proximal First-Order Primal and Primal-Dual algorithms via Performance Estimation
Nizar Bousselmi, Nelly Pustelnik, Julien M. Hendrickx +1
Selecting the fastest algorithm for a specific signal/image processing task is a challenging question. We propose an approach based on the Performance Estimation Problem framework…
On the Set of Possible Minimizers of a Sum of Convex Functions
Moslem Zamani, François Glineur, Julien M. Hendrickx
Consider a sum of convex functions, where the only information known about each individual summand is the location of a minimizer. In this work, we give an exact characterization o…
Exact convergence rate of the last iterate in subgradient methods
Moslem Zamani, François Glineur
We study the convergence of the last iterate in subgradient methods applied to the minimization of a nonsmooth convex function with bounded subgradients. We first introduce a proof…
Interpolation Conditions for Linear Operators and Applications to Performance Estimation Problems
Nizar Bousselmi, Julien M. Hendrickx, François Glineur
The Performance Estimation Problem methodology makes it possible to determine the exact worst-case performance of an optimization method. In this work, we generalize this framework…
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
Baptiste Goujaud, Céline Moucer, François Glineur +3
PEPit is a Python package aiming at simplifying the access to worst-case analyses of a large family of first-order optimization methods possibly involving gradient, projection, pro…