A proximal alternating direction method of multipliers for a minimization problem with nonconvex constraints

Zheng Peng (), Jianli Chen and Wenxing Zhu

Journal of Global Optimization, 2015, vol. 62, issue 4, 728 pages

Abstract: In this paper, a proximal alternating direction method of multipliers is proposed for solving a minimization problem with Lipschitz nonconvex constraints. Such problems are raised in many engineering fields, such as the analytical global placement of very large scale integrated circuit design. The proposed method is essentially a new application of the classical proximal alternating direction method of multipliers. We prove that, under some suitable conditions, any subsequence of the sequence generated by the proposed method globally converges to a Karush–Kuhn–Tucker point of the problem. We also present a practical implementation of the method using a certain self-adaptive rule of the proximal parameters. The proposed method is used as a global placement method in a placer of very large scale integrated circuit design. Preliminary numerical results indicate that, compared with some state-of-the-art global placement methods, the proposed method is applicable and efficient. Copyright Springer Science+Business Media New York 2015

Keywords: Alternating direction method of multipliers; Proximal point method; Nonconvex minimization; VLSI; Global convergence (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10898-015-0287-2

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