Stochastic Block Mirror Descent Methods for Nonsmooth and Stochastic Optimization

  title={Stochastic Block Mirror Descent Methods for Nonsmooth and Stochastic Optimization},
  author={Cong D. Dang and Guanghui Lan},
  journal={SIAM Journal on Optimization},
In this paper, we present a new stochastic algorithm, namely the stochastic block mirror descent (SBMD) method for solving large-scale nonsmooth and stochastic optimization problems. The basic idea of this algorithm is to incorporate the block-coordinate decomposition and an incremental block averaging scheme into the classic (stochastic) mirror-descent method, in order to significantly reduce the cost per iteration of the latter algorithm. We establish the rate of convergence of the SBMD… CONTINUE READING
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