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.
Iteration-complexity of a Jacobi-type non-Euclidean ADMM for multi-block linearly constrained nonconvex programs
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abstract
This paper establishes the iteration-complexity of a Jacobi-type non-Euclidean proximal alternating direction method of multipliers (ADMM) for solving multi-block linearly constrained nonconvex programs. The subproblems of this ADMM variant can be solved in parallel and hence the method has great potential to solve large scale multi-block linearly constrained nonconvex programs. Moreover, our analysis allows the Lagrange multiplier to be updated with a relaxation parameter in the interval (0, 2).
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math.OC 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
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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.