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Variable metric proximal stochastic variance reduced gradient methods for nonconvex nonsmooth optimization

  • * Corresponding author: Xin-Wei Liu

    * Corresponding author: Xin-Wei Liu 
Abstract Full Text(HTML) Figure(4) / Table(4) Related Papers Cited by
  • We study the problem of minimizing the sum of two functions. The first function is the average of a large number of nonconvex component functions and the second function is a convex (possibly nonsmooth) function that admits a simple proximal mapping. With a diagonal Barzilai-Borwein stepsize for updating the metric, we propose a variable metric proximal stochastic variance reduced gradient method in the mini-batch setting, named VM-SVRG. It is proved that VM-SVRG converges sublinearly to a stationary point in expectation. We further suggest a variant of VM-SVRG to achieve linear convergence rate in expectation for nonconvex problems satisfying the proximal Polyak-Łojasiewicz inequality. The complexity of VM-SVRG is lower than that of the proximal gradient method and proximal stochastic gradient method, and is the same as the proximal stochastic variance reduced gradient method. Numerical experiments are conducted on standard data sets. Comparisons with other advanced proximal stochastic gradient methods show the efficiency of the proposed method.

    Mathematics Subject Classification: Primary: 90C06; Secondary: 90C30.


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  • Figure 1.  Comparison of VM-SVRG with different mini-batch sizes

    Figure 2.  Comparison of VM-SVRG and other modern methods for solving LR problem

    Figure 3.  Comparison of VM-SVRG with different mini-batch sizes

    Figure 4.  Comparison of VM-SVRG and mS2GD for solving SVM problem

    Algorithm 2 PL-VM-SVRG($ w^0,m,b,U_0 $)
    Input: Number of inner iterations $ m $, initial point $ \tilde{w}_0=w^0 \in \mathbb{R}^d $, initial metric $ U_0 $, mini-batch size $ b\in \{1,2,\ldots,n\} $;
    1: for $ s=0,1,\ldots,S-1 $ do
    2:    $ w^{s+1} = $ VM-SVRG($ w^s,m,b,U_0 $).
    3: end for
    Output: $ w^S $.
     | Show Table
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    Table 1.  Comparison of the SFO and PO complexity.

    Complexity Prox-GD Prox-SGD Prox-SVRG VM-SVRG
    SFO $ \mathcal{O}(n/\epsilon) $ $ \mathcal{O}(1/\epsilon^2) $ $ \mathcal{O}(n+(n^{2/3}/\epsilon)) $ $ \mathcal{O}(n+(n^{2/3}/\epsilon)) $
    PO $ \mathcal{O}(1/\epsilon) $ $ \mathcal{O}(1/\epsilon) $ $ \mathcal{O}(1/\epsilon) $ $ \mathcal{O}(1/\epsilon) $
    SFO(PL) $ \mathcal{O}(n\kappa\log(1/\epsilon)) $ $ \mathcal{O}(1/\epsilon^2) $ $ \mathcal{O}((n+\kappa n^{2/3})\log(1/\epsilon)) $ $ \mathcal{O}((n+\kappa n^{2/3})\log(1/\epsilon)) $
    PO(PL) $ \mathcal{O}(\kappa \log(1/\epsilon)) $ $ \mathcal{O}(1/\epsilon) $ $ \mathcal{O}(\kappa \log(1/\epsilon)) $ $ \mathcal{O}(\kappa \log(1/\epsilon)) $
     | Show Table
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    Table 2.  The information of data sets

    Data sets $ n $ $ d $
    ijcnn1 49, 990 22
    rcv1 20, 242 47, 236
    real-sim 72, 309 20, 958
    covtype 581, 012 54
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    Table 3.  Best choices of parameters for the methods

    Data sets mS2GD$ (m,\eta) $ mS2GD-BB mSARAH$ (m,\eta) $ mSARAH-BB VM-SVRG
    ijcnn1 $ (0.02n,\frac{4}{L}) $ $ 0.04n $ $ (0.05n,\frac{1.8}{L}) $ $ 0.04n $ $ 0.04n $
    rcv1 $ (0.1n,\frac{4}{L}) $ $ 0.11n $ $ (0.1n,\frac{3.5}{L}) $ $ 0.09n $ $ 0.25n $
    real-sim $ (0.12n,\frac{0.6}{L}) $ $ 0.15n $ $ (0.07n,\frac{2}{L}) $ $ 0.06 $ $ 0.11n $
    covtype $ (0.07n,\frac{21}{L}) $ $ 0.03n $ $ (0.07n,\frac{25}{L}) $ $ 0.008n $ $ 0.01n $
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