期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 64, 期 2, 页码 290-305出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2015.2480041
关键词
Distributed optimization; convergence rate; optimal step-size
资金
- Swedish Foundation for Strategic Research through ICT-Psi project
- Swedish Research Council [2013-5523, 2014-6282]
- McKenzie Fellowship
This paper presents optimal parameter selection and preconditioning of the alternating direction method of multipliers (ADMM) algorithm for a class of distributed quadratic problems, which can be formulated as equality-constrained quadratic programming problems. The parameter selection focuses on the ADMM step-size and relaxation parameter, while the preconditioning corresponds to selecting the edge weights of the underlying communication graph. We optimize these parameters to yield the smallest convergence factor of the iterates. Explicit expressions are derived for the step-size and relaxation parameter, as well as for the corresponding convergence factor. Numerical simulations justify our results and highlight the benefits of optimal parameter selection and preconditioning for the ADMM algorithm.
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