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A proximal ADMM with the Broyden family for convex optimization problems

This work is supported by Grant-in-Aid for Scientific Research (C) (17K00032) from Japan Society for the Promotion of Science.

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  • Alternating direction methods of multipliers (ADMM) have been well studied and effectively used in various application fields. The classical ADMM must solve two subproblems exactly at each iteration. To overcome the difficulty of computing the exact solution of the subproblems, some proximal terms are added to the subproblems. Recently, {{a special proximal ADMM has been studied}} whose regularized matrix in the proximal term is generated by the BFGS update (or limited memory BFGS) at every iteration for a structured quadratic optimization problem. {{The numerical experiments also showed}} that the numbers of iterations were almost same as those by the exact ADMM. In this paper, we propose such a proximal ADMM for more general convex optimization problems, and extend the proximal term by the Broyden family update. We also show the convergence of the proposed method under standard assumptions.

    Mathematics Subject Classification: Primary: 90C25, 90C53, 65K05.

    Citation:

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  • Figure 1.  Evolution of the objective function values with respect to CPU time for small problems

    Table 1.  Comparison on iteration steps and CPU time (seconds) for different $ t $ of ADM-Broyden

    Setting ADM-Broyden $ t=-0.1 $ ADM-Broyden $ t=0 $ ADM-Broyden $ t=0.1 $
    $ m $ $ n $ $ p $ $ \beta $ Iter. Int.Iter. Time $ \beta $ Iter. Int.Iter. Time $ \beta $ Iter. Int.Iter. Time
    500 200 0.1 2.4 72.0 216.0 0.16 2.3 74.0 222.0 0.16 2.4 72.0 216.0 0.16
    500 500 0.1 3.3 81.0 243.0 0.46 3.5 83.0 249.0 0.49 2.2 82.0 246.0 0.44
    1000 500 0.1 2.3 158.0 474.0 1.14 2.2 155.0 465.0 1.11 2.2 157.0 471.0 1.12
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    Table 2.  Comparison on iteration steps and CPU time (seconds) among the four methods

    Setting ADMM-1 ADMM-2 ADM-PRO ADM-BFGS
    $ m $ $ n $ $ p $ $ \beta $ Iter. Int.Iter. Time $ \beta $ Iter. Int.Iter. Time $ \beta $ Iter. Int.Iter. Time $ \beta $ Iter. Int.Iter. Time
    500 200 0.1 4.0 15.0 60.0 0.35 0.6 77.0 306.0 0.14 0.3 131.0 524.0 0.23 2.3 74.0 222.0 0.16
    500 500 0.1 4.0 20.0 100.0 2.50 0.6 105.0 418.0 0.26 0.3 390.0 1559.0 0.75 3.5 83.0 249.0 0.49
    1000 500 0.1 9.0 19.0 85.0 4.59 0.8 128.0 510.0 0.51 0.3 233.0 931.0 0.88 2.2 155.0 465.0 1.11
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    Table 3.  Comparison on iteration steps and CPU time (seconds) among the five methods

    Algorithm $ m=1000 $, $ n=500 $, $ p=0.1 $ $ m=1000 $, $ n=1000 $, $ p=0.1 $ $ m=1000 $, $ n=2000 $, $ p=0.1 $
    $ \beta $ Iter. Int.Iter. Time T-A T-M $ \beta $ Iter. Int.Iter. Time T-A T-M $ \beta $ Iter. Int.Iter. Time T-A T-M
    ADMM-1 9.0 17.0 85.0 4.59 $ - $ $ - $ 10.0 19.0 95.0 13.46 $ - $ $ - $ 9.0 23.0 115.0 53.47 $ - $ $ - $
    [3pt] ADMM-2 0.8 128.0 510.0 0.51 0.46 0.05 0.8 159.0 633.0 1.18 0.99 0.19 0.6 223.0 891.0 3.13 2.93 0.20
    [3pt] ADM-PRO 0.3 233.0 931.0 0.86 0.83 0.03 0.4 550.0 2199.0 2.64 2.59 0.05 0.2 941.0 3764.0 10.68 10.61 0.07
    [3pt] ADM-LBFGS 0.7 143.0 570.0 0.55 0.52 0.03 0.8 184.0 733.0 0.94 0.89 0.05 0.8 305.0 1217.0 3.75 3.68 0.07
    [3pt] ADM-ILBFGS 0.7 139.0 554.0 0.55 0.52 0.03 1.0 185.0 736.0 0.95 0.90 0.05 0.9 294.0 1173.0 3.58 3.51 0.07
    $ m=5000 $, $ n=1000 $, $ p=0.1 $ $ m=5000 $, $ n=1000 $, $ p=0.5 $ $ m=5000 $, $ n=1000 $, $ p=1.0 $
    $ \beta $ Iter. Int.Iter. Time T-A T-M $ \beta $ Iter. Int.Iter. Time T-A T-M $ \beta $ Iter. Int.Iter. Time T-A T-M
    ADMM-1 40 16.0 80.0 153.79 $ - $ $ - $ 161 15.0 74.0 612.32 $ - $ $ - $ 250 15.0 74.0 984.23 $ - $ $ - $
    [3pt] ADMM-2 1.7 266.0 1057.0 11.67 11.41 0.26 3.9 526.0 1578.0 81.18 79.56 1.62 4.6 665.0 1995.0 142.19 138.47 3.72
    [3pt] ADM-PRO 1.1 344.0 1373.0 14.40 14.19 0.12 2.1 719.0 2859.0 111.96 111.41 0.55 2.6 920.0 3625.0 196.81 195.91 0.90
    [3pt] ADM-LBFGS 1.7 268.0 1064.0 11.22 11.10 0.12 3.7 531.0 1593.0 80.30 79.75 0.55 4.6 669.0 2007.0 142.71 141.81 0.90
    [3pt] ADM-ILBFGS 1.7 267.0 1061.0 11.02 10.90 0.12 3.8 528.0 1584.0 79.80 79.25 0.55 4.6 667.0 2001.0 141.63 140.73 0.90
    $ m=10000 $, $ n=5000 $, $ p=0.1 $ $ m=10000 $, $ n=5000 $, $ p=0.5 $ $ m=10000 $, $ n=5000 $, $ p=1.0 $
    $ \beta $ Iter. Int.Iter. Time T-A T-M $ \beta $ Iter. Int.Iter. Time T-A T-M $ \beta $ Iter. Int.Iter. Time T-A T-M
    ADMM-2 2.2 475.0 1892.0 193.10 181.67 11.43 4.3 1005.0 3015.0 1407.36 1316.69 90.67 5.3 1258.0 3774.0 2769.45 2554.13 215.32
    [5pt] ADM-PRO 0.6 736.0 2944.0 262.77 259.87 2.90 0.9 1405.0 5618.0 1843.21 1834.80 8.41 1.0 1780.0 7118.0 3664.39 3653.84 10.55
    [5pt] ADM-LBFGS 2.2 486.0 1935.0 178.95 176.05 2.90 4.0 1038.0 3114.0 1370.20 1361.79 8.41 4.5 1319.0 3957.0 2712.25 2701.70 10.55
    [5pt] ADM-ILBFGS 2.3 477.0 1898.0 175.98 173.08 2.90 4.2 1024.0 3072.0 1359.35 1350.94 8.41 5.0 1278.0 3834.0 2629.89 2619.34 10.55
     | Show Table
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