Catalyst Acceleration for Gradient-Based Non-Convex Optimization - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2017

Catalyst Acceleration for Gradient-Based Non-Convex Optimization


We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions. When the objective is convex, the proposed approach enjoys the same properties as the Catalyst approach of Lin et al, 2015. When the objective is nonconvex, it achieves the best known convergence rate to stationary points for first-order methods. Specifically, the proposed algorithm does not require knowledge about the convexity of the objective; yet, it obtains an overall worst-case efficiency of O(ε−2) and, if the function is convex, the complexity reduces to the near-optimal rate O(ε −2/3). We conclude the paper by showing promising experimental results obtained by applying the proposed approach to SVRG and SAGA for sparse matrix factorization and for learning neural networks.
Fichier principal
Vignette du fichier
arxiv_v2.pdf (766.54 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01536017 , version 1 (09-06-2017)


  • HAL Id : hal-01536017 , version 1


Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaid Harchaoui. Catalyst Acceleration for Gradient-Based Non-Convex Optimization. 2017. ⟨hal-01536017⟩
479 View
257 Download


Gmail Facebook X LinkedIn More