{"title":"揭示秩轮廓矩阵的对称不定三角分解","authors":"J. Dumas, Clément Pernet","doi":"10.1145/3208976.3209019","DOIUrl":null,"url":null,"abstract":"We present a novel recursive algorithm for reducing a symmetric matrix to a triangular factorization which reveals the rank profile matrix. That is, the algorithm computes a factorization P TA P = L D L T where P is a permutation matrix, L is lower triangular with a unit diagonal and D is symmetric block diagonal with 1 x 1 and 2 x 2 antidiagonal blocks. This algorithm requires O(n2rømega-2) arithmetic operations, with n the dimension of the matrix, r its rank and ømega an admissible exponent for matrix multiplication. Furthermore, experimental results demonstrate that our algorithm has very good performance: its computational speed matches that of its numerical counterpart and is twice as fast as the unsymmetric exact Gaussian factorization. By adapting the pivoting strategy developed in the unsymmetric case, we show how to recover the rank profile matrix from the permutation matrix and the support of the block-diagonal matrix. We also note that there is an obstruction in characteristic 2 for revealing the rank profile matrix, which requires to relax the shape of the block diagonal by allowing the 2-dimensional blocks to have a non-zero bottom-right coefficient. This relaxed decomposition can then be transformed into a standard PLDLTP T decomposition at a negligible cost.","PeriodicalId":105762,"journal":{"name":"Proceedings of the 2018 ACM International Symposium on Symbolic and Algebraic Computation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Symmetric Indefinite Triangular Factorization Revealing the Rank Profile Matrix\",\"authors\":\"J. Dumas, Clément Pernet\",\"doi\":\"10.1145/3208976.3209019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a novel recursive algorithm for reducing a symmetric matrix to a triangular factorization which reveals the rank profile matrix. That is, the algorithm computes a factorization P TA P = L D L T where P is a permutation matrix, L is lower triangular with a unit diagonal and D is symmetric block diagonal with 1 x 1 and 2 x 2 antidiagonal blocks. This algorithm requires O(n2rømega-2) arithmetic operations, with n the dimension of the matrix, r its rank and ømega an admissible exponent for matrix multiplication. Furthermore, experimental results demonstrate that our algorithm has very good performance: its computational speed matches that of its numerical counterpart and is twice as fast as the unsymmetric exact Gaussian factorization. By adapting the pivoting strategy developed in the unsymmetric case, we show how to recover the rank profile matrix from the permutation matrix and the support of the block-diagonal matrix. We also note that there is an obstruction in characteristic 2 for revealing the rank profile matrix, which requires to relax the shape of the block diagonal by allowing the 2-dimensional blocks to have a non-zero bottom-right coefficient. This relaxed decomposition can then be transformed into a standard PLDLTP T decomposition at a negligible cost.\",\"PeriodicalId\":105762,\"journal\":{\"name\":\"Proceedings of the 2018 ACM International Symposium on Symbolic and Algebraic Computation\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2018 ACM International Symposium on Symbolic and Algebraic Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3208976.3209019\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 ACM International Symposium on Symbolic and Algebraic Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3208976.3209019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
摘要
我们提出了一种新的递归算法,将一个对称矩阵化为一个揭示秩轮廓矩阵的三角形分解。即算法计算一个分解P TA P = L D L T,其中P为置换矩阵,L为具有单位对角的下三角形,D为具有1 × 1和2 × 2反对角块的对称块对角。该算法需要O(n2rømega-2)次算术运算,其中n为矩阵的维数,r为矩阵的秩,而ømega是矩阵乘法的可接受指数。此外,实验结果表明,我们的算法具有非常好的性能,其计算速度与数值对应的计算速度相当,并且是非对称精确高斯分解的两倍。通过采用在非对称情况下开发的旋转策略,我们展示了如何从排列矩阵和块对角矩阵的支持中恢复秩轮廓矩阵。我们还注意到,在特征2中有一个阻碍,用于显示秩轮廓矩阵,这需要通过允许二维块具有非零的右下系数来放松块对角线的形状。然后可以将这种松弛分解转换为标准PLDLTP T分解,成本可以忽略不计。
Symmetric Indefinite Triangular Factorization Revealing the Rank Profile Matrix
We present a novel recursive algorithm for reducing a symmetric matrix to a triangular factorization which reveals the rank profile matrix. That is, the algorithm computes a factorization P TA P = L D L T where P is a permutation matrix, L is lower triangular with a unit diagonal and D is symmetric block diagonal with 1 x 1 and 2 x 2 antidiagonal blocks. This algorithm requires O(n2rømega-2) arithmetic operations, with n the dimension of the matrix, r its rank and ømega an admissible exponent for matrix multiplication. Furthermore, experimental results demonstrate that our algorithm has very good performance: its computational speed matches that of its numerical counterpart and is twice as fast as the unsymmetric exact Gaussian factorization. By adapting the pivoting strategy developed in the unsymmetric case, we show how to recover the rank profile matrix from the permutation matrix and the support of the block-diagonal matrix. We also note that there is an obstruction in characteristic 2 for revealing the rank profile matrix, which requires to relax the shape of the block diagonal by allowing the 2-dimensional blocks to have a non-zero bottom-right coefficient. This relaxed decomposition can then be transformed into a standard PLDLTP T decomposition at a negligible cost.