Distributed Optimization and Statistical Learning via the ...

Foundations and TrendsR inMachine LearningVol. 3, No. 1 (2010) 1 122c 2011 S. Boyd, N. Parikh, E. Chu, B. Peleatoand J. EcksteinDOI: Optimization and StatisticalLearning via the Alternating DirectionMethod of MultipliersStephen Boyd1, Neal Parikh2, Eric Chu3 Borja Peleato4and Jonathan Eckstein51 Electrical Engineering Department, Stanford University, Stanford, CA94305, USA, Science Department, Stanford University, Stanford, CA 94305,USA, Engineering Department, Stanford University, Stanford, CA94305, USA, Engineering Department, Stanford University, Stanford, CA94305, USA, Science and Information Systems Department andRUTCOR, Rutgers University, Piscataway, NJ 08854, Introduction32 Dual Dual Augmented Lagrangians and the Method of Multipliers103 Alternating Direction Method of Optimality Conditions and Stopping Extensions and Notes and References234 General Proximity Quadratic Objective Smooth Objective Decomposition315 Constrained Convex Convex Linear and Quadratic Programming366 1-Norm Least Absolute Basis General 1 Regularized Loss Sparse Inverse Covariance Selection457 Consensus and Global Variable Cons

general frameworks for distributed optimization. In §8, we consider distributed methods for generic model fitting problems, including reg-ularized regression models like the lasso and classification models like support vector machines. In §9, we consider the use of ADMM as a heuristic for solving some nonconvex problems.

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  Model, Methods, Distributed, Optimization, Distributed optimization

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