The Proximal Alternating Direction Method of Multipliers in the Nonconvex Setting: Convergence Analysis and Rates

Radu Ioan Bot () and Dang-Khoa Nguyen ()
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Radu Ioan Bot: Faculty of Mathematics, University of Vienna, 1090 Vienna, Austria; and Faculty of Mathematics and Computer Sciences, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
Dang-Khoa Nguyen: Faculty of Mathematics, University of Vienna, 1090 Vienna, Austria;

Mathematics of Operations Research, 2020, vol. 45, issue 2, 682-712

Abstract: We propose two numerical algorithms in the fully nonconvex setting for the minimization of the sum of a smooth function and the composition of a nonsmooth function with a linear operator. The iterative schemes are formulated in the spirit of the proximal alternating direction method of multipliers and its linearized variant, respectively. The proximal terms are introduced via variable metrics, a fact that allows us to derive new proximal splitting algorithms for nonconvex structured optimization problems, as particular instances of the general schemes. Under mild conditions on the sequence of variable metrics and by assuming that a regularization of the associated augmented Lagrangian has the Kurdyka–Łojasiewicz property, we prove that the iterates converge to a Karush–Kuhn–Tucker point of the objective function. By assuming that the augmented Lagrangian has the Łojasiewicz property, we also derive convergence rates for both the augmented Lagrangian and the iterates.

Keywords: nonconvex complexly structured optimization problems; alternating direction method of multipliers; proximal splitting algorithms; variable metric; convergence analysis; convergence rates; Kurdyka–Łojasiewicz property; Łojasiewicz exponent (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormoor:v:45:y:2020:i:2:p:682-712

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