3 citations · 7 across the 14 of their papers we have counts for
4 papers · 1 filter
Beyond Nonconvexity: A Universal Trust-Region Method with New Analyses
Yuntian Jiang, Chang He, Chuwen Zhang +3
The trust-region (TR) method is renowned historically for its robustness in nonconvex problems and extraordinary numerical performance, but the study of its performance in convex o…
Trust Region Methods For Nonconvex Stochastic Optimization Beyond Lipschitz Smoothness
Chenghan Xie, Chenxi Li, Chuwen Zhang +3
In many important machine learning applications, the standard assumption of having a globally Lipschitz continuous gradient may fail to hold. This paper delves into a more general…
A Homogenization Approach for Gradient-Dominated Stochastic Optimization
Jiyuan Tan, Chenyu Xue, Chuwen Zhang +3
Gradient dominance property is a condition weaker than strong convexity, yet sufficiently ensures global convergence even in non-convex optimization. This property finds wide appli…
Homogeneous second-order descent framework: a fast alternative to Newton-type methods
Chang He, Yuntian Jiang, Chuwen Zhang +3
This paper proposes a homogeneous second-order descent framework (HSODF) for nonconvex and convex optimization based on the generalized homogeneous model (GHM). In comparison to th…