6 citations · 11 across the 5 of their papers we have counts for
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Tight Convergence Rate in Subgradient Norm of the Proximal Point Algorithm
Guoyong Gu, Junfeng Yang
Proximal point algorithm has found many applications, and it has been playing fundamental roles in the understanding, design, and analysis of many first-order methods. In this pape…
On the dual step length of the alternating direction method of multipliers
Guoyong Gu, Junfeng Yang
The alternating direction method of multipliers (ADMM) is a most widely used optimization scheme for solving linearly constrained separable convex optimization problems. The conver…
On the optimal linear convergence factor of the relaxed proximal point algorithm for monotone inclusion problems
Guoyong Gu, Junfeng Yang
Finding a zero of a maximal monotone operator is fundamental in convex optimization and monotone operator theory, and \emph{proximal point algorithm} (PPA) is a primary method for…
On the Optimal Ergodic Sublinear Convergence Rate of the Relaxed Proximal Point Algorithm for Variational Inequalities
Guoyong Gu, Junfeng Yang
This paper investigates the optimal ergodic sublinear convergence rate of the relaxed proximal point algorithm for solving monotone variational inequality problems. The exact worst…
Optimal Nonergodic Sublinear Convergence Rate of Proximal Point Algorithm for Maximal Monotone Inclusion Problems
Guoyong Gu, Junfeng Yang
We establish the optimal nonergodic sublinear convergence rate of the proximal point algorithm for maximal monotone inclusion problems. First, the optimal bound is formulated by th…