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math.OC2021
A dual-primal balanced augmented Lagrangian method for linearly constrained convex programming
Shengjie Xu
Most recently, He and Yuan [arXiv:2108.08554, 2021] have proposed a balanced augmented Lagrangian method (ALM) for the canonical convex programming problem with linear constraints,…
math.OC2021
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…
math.OC2021
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…