Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization

1 code implementation • 25 May 2022 • Benjamin Dubois-Taine, Francis Bach, Quentin Berthet, Adrien Taylor

We consider the problem of minimizing the sum of two convex functions.

PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python

1 code implementation • 11 Jan 2022 • Baptiste Goujaud, Céline Moucer, François Glineur, Julien Hendrickx, Adrien Taylor, Aymeric Dieuleveut

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, proximal, or linear optimization oracles, along with their approximate, or Bregman variants.

Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms

no code implementations • NeurIPS 2021 • Mathieu Even, Raphaël Berthier, Francis Bach, Nicolas Flammarion, Hadrien Hendrikx, Pierre Gaillard, Laurent Massoulié, Adrien Taylor

We introduce the ``continuized'' Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter.

A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip

1 code implementation • 10 Jun 2021 • Mathieu Even, Raphaël Berthier, Francis Bach, Nicolas Flammarion, Pierre Gaillard, Hadrien Hendrikx, Laurent Massoulié, Adrien Taylor

We introduce the continuized Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter.

A Continuized View on Nesterov Acceleration

no code implementations • 11 Feb 2021 • Raphaël Berthier, Francis Bach, Nicolas Flammarion, Pierre Gaillard, Adrien Taylor

We introduce the "continuized" Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter.

Distributed, Parallel, and Cluster Computing Optimization and Control

Acceleration Methods

1 code implementation • 23 Jan 2021 • Alexandre d'Aspremont, Damien Scieur, Adrien Taylor

This monograph covers some recent advances in a range of acceleration techniques frequently used in convex optimization.

Principled Analyses and Design of First-Order Methods with Inexact Proximal Operators

1 code implementation • 10 Jun 2020 • Mathieu Barré, Adrien Taylor, Francis Bach

In this work, we survey notions of inaccuracies that can be used when solving those intermediary optimization problems.

Optimization and Control Numerical Analysis Numerical Analysis

Complexity Guarantees for Polyak Steps with Momentum

1 code implementation • 3 Feb 2020 • Mathieu Barré, Adrien Taylor, Alexandre d'Aspremont

In smooth strongly convex optimization, knowledge of the strong convexity parameter is critical for obtaining simple methods with accelerated rates.

Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions

1 code implementation • 3 Feb 2019 • Adrien Taylor, Francis Bach

We use the approach for analyzing deterministic and stochastic first-order methods under different assumptions on the nature of the stochastic noise.

Worst-case convergence analysis of inexact gradient and Newton methods through semidefinite programming performance estimation

1 code implementation • 15 Sep 2017 • Etienne de Klerk, Francois Glineur, Adrien Taylor

To illustrate the applicability of the tools, we demonstrate a novel complexity analysis of short step interior point methods using inexact search directions.

Optimization and Control 90C22, 90C26, 90C30