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A practical determination strategy of optimal threshold parameter for matrix compression in wavelet BEM

机译:小波边界元法中矩阵压缩最优阈值参数的实用确定策略

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摘要

A practical strategy is developed to determine the optimal threshold parameter for wavelet-based boundary element (BE) analysis. The optimal parameter is determined so that the amount of storage (and computational work) is minimized without reducing the accuracy of the BE solution. In the present study, the Beylkin-type truncation scheme is used in the matrix assembly. To avoid unnecessary integration concerning the truncated entries of a coefficient matrix, a priori estimation of the matrix entries is introduced and thus the truncated entries are determined twice: before and after matrix assembly. The optimal threshold parameter is set based on the equilibrium of the truncation and discretization errors. These errors are estimated in the residual sense. For Laplace problems the discretization error is, in particular, indicated with the potential's contribution‖c‖ to the residual norm ‖R‖ used in error estimation for mesh adaptation. Since the normalized residual norm ‖c‖ /‖u‖ (u: the potential components of BE solution) cannot be computed without main BE analysis, the discretization error is estimated by the approximate expression constructed through subsidiary BE calculation with smaller degree of freedom (DOF). The matrix compression using the proposed optimal threshold parameter enables us to generate a sparse matrix with O(N~(1+γ) (0 ≤ γ < 1) non-zero entries. Although the quasi-optimal memory requirements and complexity are not attained, the compression rate of a few per cent can be achieved for N ~ 1000.
机译:为确定基于小波的边界元素(BE)分析的最佳阈值参数,开发了一种实用的策略。确定最佳参数,以便在不降低BE解决方案准确性的情况下,最大程度地减少存储量(和计算工作量)。在本研究中,在矩阵装配中使用了Beylkin型截断方案。为了避免关于系数矩阵的截断条目的不必要的积分,引入了对矩阵条目的先验估计,因此对截断的条目进行两次确定:在矩阵组装之前和之后。基于截断误差和离散误差的平衡设置最佳阈值参数。这些误差是在剩余意义上估计的。对于拉普拉斯问题,离散化误差特别表示为对网格自适应误差估计中使用的残差范数“ R”的潜在贡献“ c”。由于没有主BE分析就无法计算归一化残差范数“ c” /“ u”(u:BE解的潜在分量),因此离散化误差是通过具有较小自由度的通过辅助BE计算构造的近似表达式来估计的(自由度)。使用建议的最佳阈值参数进行矩阵压缩,使我们能够生成具有O(N〜(1 +γ)(0≤γ<1)个非零条目的稀疏矩阵,尽管未达到准最佳存储要求和复杂性N〜1000时,压缩率可达到百分之几。

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