Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization

We propose two new alternating direction methods to solve “fully” nonsmooth constrained convex problems. Our algorithms have the best known worst-case iteration-complexity guarantee under mild assumptions for both the objective residual and feasibility gap. Through theoretical analysis, we show how to update all the algorithmic parameters automatically with clear impact on the convergence performance. We also provide a representative numerical example showing the advantages of our methods over the classical alternating direction methods using a well-known feasibility problem.


Published in:
Large-Scale and Distributed Optimization
Year:
2018
Publisher:
Springer
ISBN:
9783319974781
Keywords:
Laboratories:




 Record created 2018-11-20, last modified 2019-04-15

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