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曲率障碍下四阶变分不等式的交替方向乘子法

张霖森 程兰 张守贵

张霖森, 程兰, 张守贵. 曲率障碍下四阶变分不等式的交替方向乘子法[J]. 应用数学和力学, 2023, 44(5): 595-604. doi: 10.21656/1000-0887.430243
引用本文: 张霖森, 程兰, 张守贵. 曲率障碍下四阶变分不等式的交替方向乘子法[J]. 应用数学和力学, 2023, 44(5): 595-604. doi: 10.21656/1000-0887.430243
ZHANG Linsen, CHENG Lan, ZHANG Shougui. An Alternating Direction Multiplier Method for 4th-Order Variational Inequalities With Curvature Obstacle[J]. Applied Mathematics and Mechanics, 2023, 44(5): 595-604. doi: 10.21656/1000-0887.430243
Citation: ZHANG Linsen, CHENG Lan, ZHANG Shougui. An Alternating Direction Multiplier Method for 4th-Order Variational Inequalities With Curvature Obstacle[J]. Applied Mathematics and Mechanics, 2023, 44(5): 595-604. doi: 10.21656/1000-0887.430243

曲率障碍下四阶变分不等式的交替方向乘子法

doi: 10.21656/1000-0887.430243
基金项目: 

国家自然科学基金项目 11971085

重庆市自然科学基金项目 cstc2020jcyj-msxmX0066

重庆市研究生教育教学改革研究项目 yjg213071

重庆市研究生科研创新项目 CYS22561

详细信息
    作者简介:

    张霖森(1997—),男,硕士生(E-mail: 398780730@qq.com)

    通讯作者:

    张守贵(1973—),男,教授,博士(通讯作者. E-mail: shgzhang@cqnu.edu.cn)

  • 中图分类号: O241.82

An Alternating Direction Multiplier Method for 4th-Order Variational Inequalities With Curvature Obstacle

  • 摘要: 对于重调和算子和曲率障碍表示的变分不等式,提出了自适应交替方向乘子数值解法(SADMM). 对问题引入一个辅助变量表示曲率函数的增广Lagrange函数,导出一个约束极小值问题,并且该问题等价于一个鞍点问题. 然后采用交替方向乘子法(ADMM)求解这个鞍点问题. 通过采用平衡原理和迭代函数,得到了自动调整罚参数的自适应法则,从而提高了计算效率. 证明了该方法的收敛性,并给出了利用迭代函数近似罚参数的具体方法. 最后,用数值计算结果验证了该方法的有效性.
  • 图  1  u的数值解

    Figure  1.  Numerical solutions of u

    图  2  -Δu的数值解

    Figure  2.  Numerical solutions of -Δu

    图  3  精确解u

    Figure  3.  Analytical solutions of u

    图  4  逐点误差

    Figure  4.  Pointwise errors between numerical and analytical solutions

    表  1  算法随步长变化所需迭代次数的情况

    Table  1.   The numbers of iterations required for the algorithm to change with the step size

    ρ algorithm 1 (ADMM) algorithm 2 (SADMM)
    h=1/10 h=1/20 h=1/40 h=1/80 h=1/10 h=1/20 h=1/40 h=1/80
    10-2 * * * * 25 28 24 28
    10-1 * * * * 26 34 29 34
    100 47 59 93 101 30 39 33 39
    101 * * * * 32 41 35 41
    102 * * * * 33 42 36 42
    103 * * * * 34 43 37 43
    104 * * * * 35 44 38 44
    下载: 导出CSV

    表  2  算法随步长变化所需CPU时间情况

    Table  2.   CPU times required for the algorithm to change with the step size

    ρ algorithm 1 (ADMM) algorithm 2 (SADMM)
    h=1/10 h=1/20 h=1/40 h=1/80 h=1/10 h=1/20 h=1/40 h=1/80
    10-2 * * * * 0.117 5 0.255 1 1.911 9 80.095 4
    10-1 * * * * 0.020 0 0.174 4 2.126 9 95.011 4
    100 0.035 5 0.276 9 6.770 4 280.995 9 0.021 8 0.178 1 2.521 3 109.381 1
    101 * * * * 0.024 8 0.168 2 2.647 1 114.965 5
    102 * * * * 0.024 2 0.181 6 2.620 6 117.895 8
    103 * * * * 0.024 3 0.172 3 2.791 4 120.384 8
    104 * * * * 0.025 6 0.183 3 2.875 5 123.236 0
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-23
  • 修回日期:  2022-09-19
  • 刊出日期:  2023-05-01

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