Iteration-complexity of a Jacobi-type non-Euclidean ADMM for multi-block linearly constrained nonconvex programs
arXiv:1705.07229
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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- Accelerated Stochastic Algorithms for Nonconvex Finite-sum and Multi-block Optimization
- Dual Descent ALM and ADMM
- An Incremental Path-Following Splitting Method for Linearly Constrained Nonconvex Nonsmooth Programs