An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization

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dc.contributor.authorLi, Chengboen_US
dc.contributor.authorYin, Wotaoen_US
dc.contributor.authorJiang, Hongen_US
dc.contributor.authorZhang, Yinen_US
dc.date.accessioned2018-06-19T17:48:00Zen_US
dc.date.available2018-06-19T17:48:00Zen_US
dc.date.issued2012-07en_US
dc.date.noteJuly 2012en_US
dc.description.abstractBased on the classic augmented Lagrangian multiplier method, we propose, analyze and test an algorithm for solving a class of equality-constrained non-smooth optimization problems (chiefly but not necessarily convex programs) with a particular structure. The algorithm effectively combines an alternating direction technique with a nonmonotone line search to minimize the augmented Lagrangian function at each iteration. We establish convergence for this algorithm, and apply it to solving problems in image reconstruction with total variation regularization. We present numerical results showing that the resulting solver, called TVAL3, is competitive with, and often outperforms, other state-of-the-art solvers in the field.en_US
dc.format.extent26 ppen_US
dc.identifier.citationLi, Chengbo, Yin, Wotao, Jiang, Hong, et al.. "An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization." (2012) <a href="https://hdl.handle.net/1911/102202">https://hdl.handle.net/1911/102202</a>.en_US
dc.identifier.digitalTR12-13en_US
dc.identifier.urihttps://hdl.handle.net/1911/102202en_US
dc.language.isoengen_US
dc.titleAn Efficient Augmented Lagrangian Method with Applications to Total Variation Minimizationen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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