1998
DOI: 10.1109/mcse.1998.7102081
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N-body problems: IES3: Efficient electrostatic and electromagnetic simulation

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Cited by 95 publications
(81 citation statements)
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“…A hybrid method for kernel independent matrix-vector multiplication algorithm was proposed in [16] and [17]. Based on the fact that large blocks of the particle interaction matrix are low rank, this method uses singular value decomposition to sample and sparsify these blocks.…”
Section: Synopsis Of the New Methodmentioning
confidence: 99%
“…A hybrid method for kernel independent matrix-vector multiplication algorithm was proposed in [16] and [17]. Based on the fact that large blocks of the particle interaction matrix are low rank, this method uses singular value decomposition to sample and sparsify these blocks.…”
Section: Synopsis Of the New Methodmentioning
confidence: 99%
“…There exist a lot of fast integral equation methods that can be used to enhance the efficiency of the solution, for instance, FMM [12,[19][20][21], CGFFT [22,23], Pre-corrected FFT [24], SMCG [25], AIM [26], IceCube [27], IMLMQRF [11,28], and MLGFIM [1,2] and so on. In them, 1) FMM is the fastest method with O(N ) complexity for quasistatic problems; 2) SMCG, CGFFT, Pre-corrected FFT, SMCG, and AIM are FFT based methods with O(N log N ) complexity; 3) Ice Cube and IMLMQRF are the methods based on matrix compression technique, i.e., QR factorization, matrix merging, and matrix column and row sampling techniques; 4) MLGFIM is based on a hierarchical structure that is similar to FMM but using the Green's function matrix interpolation method with QR factorization technique.…”
Section: Introductionmentioning
confidence: 99%
“…But this computation can be accelerated using a low-rank QR approximation. Low-rank QR approximation has been demonstrated to be effective at accelerating a variety of electromagnetic integral equations [20] [21] [22] [23]. We do not prove that the low-rank QR approximation is superior to multipole expansions, panel methods, or other acceleration schemes; we employ the low-rank QR approximation because the algorithm is independent of the Green's function and can thus be applied to either form of the Biot-Savart law.…”
Section: Introductionmentioning
confidence: 99%