170 citations · 395 across the 10 of their papers we have counts for
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stat.ML2017★ 15 cited
Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
Hongzhou Lin, Julien Mairal, Zaid Harchaoui
We introduce a generic scheme for accelerating gradient-based optimization methods in the sense of Nesterov. The approach, called Catalyst, builds upon the inexact accelerated prox…
stat.ML2017★ 21 cited
Catalyst Acceleration for Gradient-Based Non-Convex Optimization
Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy +2
We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions. Even though these method…