A new alternating direction method for linearly constrained nonconvex optimization problems

X. Wang (), S. Li (), X. Kou () and Q. Zhang ()

Journal of Global Optimization, 2015, vol. 62, issue 4, 695-709

Abstract: In this paper, we study the classical nonconvex linearly constrained optimization problem. Under some mild conditions, we obtain that the penalization sequence is nonincreasing and the sequence generated by our algorithm has finite length. Based on the assumption that the objective functions have Kurdyka–Lojasiewicz property, we prove the convergence of the algorithm. We also show the numerical efficiency of our method by the concrete applications in the areas of image processing and statistics. Copyright Springer Science+Business Media New York 2015

Keywords: Nonconvex; Constrained problem; Convergence theorem; Kurdyka–Lojasiewicz property (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s10898-015-0268-5

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