5 papers
UGM: A Unified Framework and New Perspectives for Accelerated Gradient Methods in Smooth and Strongly Convex Optimization
Danqing Zhou, Shiqian Ma, Junfeng Yang
In this paper, we propose a unified framework for accelerated gradient methods, dubbed UGM, which subsumes a wide range of accelerated and conventional gradient-type methods design…
On the Convergence of Constrained Gradient Method
Danqing Zhou, Hongmei Chen, Shiqian Ma +1
The constrained gradient method (CGM) has recently been proposed to solve convex optimization and monotone variational inequality (VI) problems with general functional constraints.…
A Simple Adaptive Proximal Gradient Method for Nonconvex Optimization
Zilong Ye, Shiqian Ma, Junfeng Yang +1
Consider composite nonconvex optimization problems where the objective function consists of a smooth nonconvex term (with Lipschitz-continuous gradient) and a convex (possibly nons…
Relaxed Proximal Point Algorithm: Tight Complexity Bounds and Acceleration without Momentum
Bofan Wang, Shiqian Ma, Junfeng Yang +1
In this paper, we focus on the relaxed proximal point algorithm (RPPA) for solving convex (possibly nonsmooth) optimization problems. We conduct a comprehensive study on three type…
AdaBB: Adaptive Barzilai-Borwein Method for Convex Optimization
Danqing Zhou, Shiqian Ma, Junfeng Yang
In this paper, we propose AdaBB, an adaptive gradient method based on the Barzilai-Borwein stepsize. The algorithm is line-search-free and parameter-free, and essentially provides…