Abstract
This paper studies the data redundancy of the coefficient matrix of the corresponding discrete system which forms a basis for fast algorithms of solving the integral equation whose kernel includes a convolution function factor. We develop lossless matrix compression strategies, which reduce the cost of integral evaluations and the storage to linear complexity, i.e., the same order of the approximation space dimensions. We establish that this algorithm preserves the convergence order of the approximate solution. We also propose a hardware-aware parallel algorithm for these strategies.
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Communicated by Charles A. Micchelli.
This research is supported in part by the Natural Science Foundation of China under grants 11071264, 11171359 and supported in part by the Guangdong Province Key Laboratory of Computational Science and the Guangdong Province Computational Science Innovative Research Team.
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Ye, W., Zhang, Y. A fast solver for integral equations with convolution-type Kernel. Adv Comput Math 39, 45–67 (2013). https://doi.org/10.1007/s10444-011-9268-2
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DOI: https://doi.org/10.1007/s10444-011-9268-2