3 citations · 6 across the 7 of their papers we have counts for
7 papers
Adaptive Inertial Method
Han Long, Bingsheng He, Yinyu Ye +1
In this paper, we introduce the Adaptive Inertial Method (AIM), a novel framework for accelerated first-order methods through a customizable inertial term. We provide a rigorous co…
A rank-two relaxed parallel splitting version of the augmented Lagrangian method with step size in (0,2) for separable convex programming
Bingsheng He, Feng Ma, Shengjie Xu +1
The augmented Lagrangian method (ALM) is classic for canonical convex programming problems with linear constraints, and it finds many applications in various scientific computing a…
On construction of splitting contraction algorithms in a prediction-correction framework for separable convex optimization
Bingsheng He, Xiaoming Yuan
In the past decade, we had developed a series of splitting contraction algorithms for separable convex optimization problems, at the root of the alternating direction method of mul…
The Influence of Boreal Summer Madden-Julian Oscillation on Precipitation Extremes in Indonesia
Fadhil R. Muhammad, Sandro W. Lubis, Sonni Setiawan
This study examines the influence of Madden-Julian Oscillation (MJO) on Indonesian precipitation during the extended boreal summer (May to September). The MJO is one of the dominan…
Balanced Augmented Lagrangian Method for Convex Programming
Bingsheng He, Xiaoming Yuan
We consider the convex minimization model with both linear equality and inequality constraints, and reshape the classic augmented Lagrangian method (ALM) by balancing its subproble…
Extensions of ADMM for Separable Convex Optimization Problems with Linear Equality or Inequality Constraints
Bingsheng He, Shengjie Xu, Xiaoming Yuan
The alternating direction method of multipliers (ADMM) proposed by Glowinski and Marrocco is a benchmark algorithm for two-block separable convex optimization problems with linear…
Indefinite linearized augmented Lagrangian method for convex programming with linear inequality constraints
Bingsheng He, Shengjie Xu, Jing Yuan
The augmented Lagrangian method (ALM) is a benchmark for convex programming problems with linear constraints; ALM and its variants for linearly equality-constrained convex minimiza…