2017
DOI: 10.1137/16m1055530
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Improved Pointwise Iteration-Complexity of A Regularized ADMM and of a Regularized Non-Euclidean HPE Framework

Abstract: This paper describes a regularized variant of the alternating direction method of multipliers (ADMM) for solving linearly constrained convex programs. It is shown that the pointwise iteration-complexity of the new variant is better than the corresponding one for the standard ADMM method and that, up to a logarithmic term, is identical to the ergodic iteration-complexity of the latter method. Our analysis is based on first presenting and establishing the pointwise iteration-complexity of a regularized non-Eucli… Show more

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Cited by 18 publications
(42 citation statements)
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“…The first and second identities in (17) give that they also satisfy (10). Altogether, we have that the iteration defined in (17) is generated by Algorithm 1 for solving (5) with T given in (16). On the other hand, it follows from (17) and the assumption σ > 2/5 that…”
Section: Lemma 210 ([14 Lemma 2])mentioning
confidence: 88%
See 1 more Smart Citation
“…The first and second identities in (17) give that they also satisfy (10). Altogether, we have that the iteration defined in (17) is generated by Algorithm 1 for solving (5) with T given in (16). On the other hand, it follows from (17) and the assumption σ > 2/5 that…”
Section: Lemma 210 ([14 Lemma 2])mentioning
confidence: 88%
“…The HPE method has been used for many authors [3,4,5,6,7,8,9,10,11,12,13,14] as a framework for the design and analysis of several algorithms for monotone inclusion problems, variational inequalities, saddlepoint problems and convex optimization. Its iteration-complexity has been established recently by Monteiro and Svaiter [15] and, as a consequence, it has proved the iteration-complexity of various important algorithms in optimization (which use the HPE method as a framework) including Tseng's forward-backward method, Korpelevich extragradient method and the alternating direction method of multipliers (ADMM) [12,15,16].…”
Section: Introductionmentioning
confidence: 99%
“…Since z * is an arbitrary solution of (13), the result follows from the definition of ρ k , d 0 , and η 0 given in (11), (18) and (30), respectively.…”
Section: Iteration-complexity Bounds For the Generalized Admmmentioning
confidence: 96%
“…Recently, researchers have discovered some useful convergence properties of the optimization algorithms for solving nonconvex minimization problems [24,47,48,53]. In particular, the paper [48] established the global convergence (to a stationary point) of the alternating direction method of multipliers (ADMM) for nonconvex nonsmooth optimization with linear constraints.…”
Section: Introductionmentioning
confidence: 99%