Develops an adaptive proximal ADMM that achieves state-of-the-art iteration complexity for approximate first-order stationary points in nonconvex composite problems with linear constraints, without rank assumptions and allowing inexact subproblem solves.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.OC 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
An Adaptive Proximal ADMM for Nonconvex Linearly Constrained Composite Programs
Develops an adaptive proximal ADMM that achieves state-of-the-art iteration complexity for approximate first-order stationary points in nonconvex composite problems with linear constraints, without rank assumptions and allowing inexact subproblem solves.