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Volume 6 Issue 1
Jan.  2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2019  >  6(1): 45-52
Jonathan Tuck, David Hallac and Stephen Boyd, "Distributed Majorization-Minimization for Laplacian Regularized Problems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 45-52, Jan. 2019. doi: 10.1109/JAS.2019.1911321
Citation: Jonathan Tuck, David Hallac and Stephen Boyd, "Distributed Majorization-Minimization for Laplacian Regularized Problems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 45-52, Jan. 2019. doi: 10.1109/JAS.2019.1911321
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Distributed Majorization-Minimization for Laplacian Regularized Problems

doi: 10.1109/JAS.2019.1911321

  • Department of Electrical Engineering, Stanford University, Stanford, CA 94305 USA

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    Abstract
  • We consider the problem of minimizing a block separable convex function (possibly nondifferentiable, and including constraints) plus Laplacian regularization, a problem that arises in applications including model fitting, regularizing stratified models, and multi-period portfolio optimization. We develop a distributed majorization-minimization method for this general problem, and derive a complete, self-contained, general, and simple proof of convergence. Our method is able to scale to very large problems, and we illustrate our approach on two applications, demonstrating its scalability and accuracy.

     

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