2012
DOI: 10.1109/tap.2011.2180310
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A Novel Multilevel Matrix Compression Method for Analysis of Electromagnetic Scattering From PEC Targets

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Cited by 11 publications
(21 citation statements)
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“…In this paper, we use two different and complementary fast factorization methods: The recently introduced MLMCM [11] is used to factorize the near-field region part, and the MLFMA is used for the far field region. For this class of hybrid MLFMA methods addressing multiscale problems, the state of the art is essentially in [13]- [22].…”
Section: Doubly Hierarchical Reciprocal Mlmcm (Rmlmcm) /Mlfma Anmentioning
confidence: 99%
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“…In this paper, we use two different and complementary fast factorization methods: The recently introduced MLMCM [11] is used to factorize the near-field region part, and the MLFMA is used for the far field region. For this class of hybrid MLFMA methods addressing multiscale problems, the state of the art is essentially in [13]- [22].…”
Section: Doubly Hierarchical Reciprocal Mlmcm (Rmlmcm) /Mlfma Anmentioning
confidence: 99%
“…Straightforward application of MoM would yield a dense matrix whose storage and computation efforts prohibit use with large practical problems. This calls for iterative solutions and fast factorizations; this is a largely studied field, with several solutions available for different frequency regimes [4]- [9], [11]- [30] (and references therein).…”
Section: Introductionmentioning
confidence: 99%
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“…Fast factorizations, based on the compression of memory requirements and on the speed-up of Matrix-Vector (MV) products, obviously do not address the illconditioning problem (their intrinsic kernel approximation tends to worsen the conditioning of the system indeed). In particular, the approach proposed in [1], where the MultiLevel Matrix Compression Method (MLMCM) is combined with a MultiLevel Fast Multipole Approach (MLFMA) is a fast solver particularly well suited for multiscale problems, as it handles differently high and low frequency couplings, via MLFMA and MLMCM respectively.…”
Section: Introductionmentioning
confidence: 99%
“…In this paper we fully ibridize the MLMCM/MLFMA fast solver proposed in [1] with the MR-ILU preconditioner proposed in [5].…”
Section: Introductionmentioning
confidence: 99%