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Numerical comparisons of smoothing functions for optimal correction of an infeasible system of absolute value equations

  • * Corresponding author: Saeed Ketabchi

    * Corresponding author: Saeed Ketabchi
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  • Optimal correction of an infeasible system of absolute value equations (AVEs), leads into a nonconvex and nonsmooth fractional problem. Using Dinkelbach's approach, this problem can be reformulated to form a single variable equation. In this paper, first, we have smoothed the equation by considering four important and famous smoothing functions (see[2,23]) and thus, to solve it, a smoothing-type algorithm based on the Difference of Convex (DC) algorithm-Newtown methods is proposed. Finally, the randomly generated AVEs were compared to find the best smoothing function.

    Mathematics Subject Classification: Primary: 65D10; Secondary: 34M03, 90C25, 90C26.

    Citation:

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  • Figure 1.  Performance profile of computing time of Algorithm 2 for the example 1

    Figure 2.  Performance profile of computing time of Algorithm 2 for the example 2

    Table 1.  Numerical results for Example 1

    $ \phi_1 $ $ \phi_2 $ $ \phi_3 $ $ \phi_4 $
    $ n $ $ Me $ $ Merror $ $ Mtime $ $ Me $ $ Merror $ $ Mtime $ $ Me $ $ Merror $ $ Mtime $ $ Me $ $ Merror $ $ Mtime $
    100 8.27e-17 5.52e-11 0.04 9.84e-17 1.20e-10 0.03 8.29e-17 1.16e-10 0.04 7.88e-17 1.95e-11 0.01
    500 8.42e-17 1.21e-10 1.01 8.32e-17 3.04e-10 0.83 1.03e-16 3.52e-10 0.98 8.01e-17 1.35e-10 0.38
    1000 8.25e-17 1.41e-10 5.37 9.34e-17 6.02e-10 4.30 1.05e-16 4.17e-10 4.83 8.78e-17 9.15e-11 2.13
    1500 9.36e-17 2.01e-10 14.07 7.98e-17 7.67e-10 11.82 1.00e-16 3.98e-10 12.13 8.80e-17 2.24e-11 6.06
    2000 8.99e-17 2.68e-10 30.14 9.17e-17 4.94e-10 23.72 9.95e-17 4.78e-10 26.56 9.30e-17 6.71e-11 11.48
    2500 9.42e-17 3.10e-10 48.76 7.83e-17 5.78e-10 37.08 9.00e-17 7.10e-10 46.51 9.65e-17 1.18e-10 19.34
    3000 8.84e-17 3.79e-10 85.25 9.87e-17 6.94e-10 66.18 8.06e-17 6.69e-10 78.62 9.62e-17 2.07e-10 35.11
    3500 8.44e-17 4.18e-10 122.58 9.77e-17 8.11e-10 98.50 9.37e-17 7.81e-10 117.32 9.43e-17 2.82e-11 52.73
    4000 8.89e-17 4.67e-10 162.58 9.82e-17 9.08e-10 134.06 9.35e-17 8.94e-10 158.81 8.23e-17 4.70e-11 73.29
    4500 1.01e-16 5.23e-10 245.94 9.12e-17 9.87e-10 189.79 9.14e-17 1.02e-09 242.38 8.81e-17 6.53e-11 105.15
    5000 9.17e-17 5.58e-10 289.93 8.92e-17 1.09e-09 241.79 9.31e-17 1.09e-09 288.33 9.07e-17 8.83e-11 122.53
     | Show Table
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    Table 2.  Numerical results for Example 2

    $ \phi_1 $ $ \phi_2 $ $ \phi_3 $ $ \phi_4 $
    $ n $ $ Me $ $ Merror $ $ Mtime $ $ Me $ $ Merror $ $ Mtime $ $ Me $ $ Merror $ $ Mtime $ $ Me $ $ Merror $ $ Mtime $
    100 8.85e-17 2.71e-11 0.03 1.04e-16 2.09e-11 0.03 9.03e-17 5.04e-11 0.03 9.36e-17 3.17e-11 0.02
    500 8.30e-17 2.55e-11 1.06 8.38e-17 1.51e-11 1.01 8.70e-17 9.62e-11 1.05 8.91e-17 3.69e-10 0.48
    1000 9.17e-17 1.63e-11 6.77 8.96e-17 5.38e-12 6.42 8.11e-17 6.93e-11 6.71 8.58e-17 2.82e-10 2.63
    1500 8.66e-17 1.75e-11 18.49 8.76e-17 7.15e-12 18.40 8.94e-17 3.95e-11 18.49 8.76e-17 7.39e-11 7.69
    2000 8.91e-17 1.52e-11 34.22 8.68e-17 6.17e-12 32.40 9.38e-17 4.12e-11 33.71 8.62e-17 2.07e-10 17.75
    2500 9.45e-17 2.82e-11 63.48 9.18e-17 3.01e-12 60.78 7.82e-17 5.16e-11 62.92 8.66e-17 3.56e-10 33.42
    3000 8.06e-17 1.70e-11 113.18 8.08e-17 8.29e-12 100.24 9.50e-17 3.59e-11 112.44 9.35e-17 6.29e-10 52.83
    3500 8.56e-17 6.96e-12 169.34 1.01e-16 3.62e-12 162.04 1.02e-16 2.63e-11 162.79 7.56e-17 8.45e-11 81.62
    4000 8.21e-17 1.34e-11 245.96 9.40e-17 3.91e-12 244.70 8.69e-17 3.41e-11 245.37 9.57e-17 1.38e-10 126.02
    4500 7.84e-17 1.70e-11 351.13 9.31e-17 4.52e-12 313.81 9.19e-17 4.93e-11 328.67 8.42e-17 2.02e-10 175.69
    5000 1.11e-16 3.03e-11 476.20 8.51e-17 4.20e-12 437.35 1.03e-16 5.77e-11 444.11 6.94e-17 2.67e-10 232.96
     | Show Table
    DownLoad: CSV
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