Block Sparse Cholesky Algorithms on Advanced Uniprocessor Computers

By E. Ng, B. Peyton. 1991

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Citations in the corpus

Numerical Linear Algebra (2011, methodology): “Moreover, the positions of nonzero elements can be determined in advance (Ng and Peyton, 1993).”
The university of Florida sparse matrix collection (2011, methodology): “It includes two sparse Cholesky factorization methods: a BLAS-based supernodal method [Ng and Peyton 1993] and an up-looking non-BLAS-based method [Davis 2005].”
Solving unsymmetric sparse systems of linear equations with PARDISO (2002, methodology): “Like other modern sparse factorization codes [4,7,10,11,14,20], PARDISO relies heavily on supernodes to efficiently utilize the memory hierarchies in the hardware.”
A Robust Algorithm for Power System State Estimation With Equality Constraints (2010, methodology): “No numerical pivoting is needed and all the features of modern supernodal sparse Cholesky factorization can be exploited [27].”
PENNON: A code for convex nonlinear and semidefinite programming (2003, methodology): “Then we factorize the Hessian by the sparse Cholesky solver of Ng and Peyton [15], which is very e(cid:14)cient for sparse problems with constant sparsity structure.”
Solving semidefinite-quadratic-linear programs using SDPT3 (2003, methodology): “It should be noted that LIPSOL uses Fortran programs developed by Esmond Ng and Barry Peyton for sparse Cholesky factorization [18].”
Neurobiology of Aging: Contents (2005, unknown): “Since no pivoting is used in the numeric phase, it is possible to exploit all the features of modern supernodal sparse Cholesky factorization codes (Ng and Peyton 1993).”