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Dynamic Supernodes in Sparse Cholesky Update/Downdate and Triangular Solves

机译:稀疏的Cholesky更新/降级和三角求解中的动态超节点

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The supernodal method for sparse Cholesky factorization represents the factor L as a set of supernodes, each consisting of a contiguous set of columns of L with identical nonzero pattern. A conventional supernode is stored as a dense submatrix. While this is suitable for sparse Cholesky factorization where the nonzero pattern of L does not change, it is not suitable for methods that modify a sparse Cholesky factorization after a low-rank change to A (an update/downdate, A = A ± WW~T). Supernodes merge and split apart during an update/downdate. Dynamic supernodes are introduced which allow a sparse Cholesky update/downdate to obtain performance competitive with conventional supernodal methods. A dynamic supernodal solver is shown to exceed the performance of the conventional (BLAS-based) supernodal method for solving triangular systems. These methods are incorporated into CHOLMOD, a sparse Cholesky factorization and update/downdate package which forms the basis of x=Ab in MATLAB when A is sparse and symmetric positive definite.
机译:用于稀疏Cholesky分解的超节点方法将因子L表示为一组超节点,每个超节点由具有相同非零模式的L列的连续集合组成。常规的超节点存储为密集的子矩阵。虽然这适用于L的非零模式不变的稀疏Cholesky因式分解,但不适用于在对A进行低秩更改后修改稀疏Cholesky因式分解的方法(更新/降级,A = A±WW〜 T)。在更新/降级期间,超节点合并并拆分。引入了动态超节点,该动态超节点允许稀疏的Cholesky更新/降级来获得与常规超节点方法相竞争的性能。动态超节点求解器显示出超过了传统的(基于BLAS的)超节点方法求解三角系统的性能。这些方法被合并到CHOLMOD中,这是一个稀疏的Cholesky因式分解和更新/降级程序包,当A为稀疏且对称正定时,它构成了MATLAB中x = Ab的基础。

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