Result for 8A27862E3F3D4F0380F8DFD617855C8347B52384

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.14
FileSize1085624
MD503048077AA4380966B0ADBF7793F1365
SHA-18A27862E3F3D4F0380F8DFD617855C8347B52384
SHA-2567F7E3B23CE80CC318A77316ADDA859EE771F1FE24D67C7F08DF94AC98EF4793B
SSDEEP24576:QBhCF/EkhnWF/tkh/HKN+vJAqTUnuakcaw4M+P4bRL5dkQ9:/TXcp+4
TLSHT12E353A57F49204ACC0ABF9305AB97553B6723848832925762FA79D382F7EF116E1B703
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5767D599CC12E0EEA9420648C7CB05A65
PackageArchx86_64
PackageDescriptionCHOLMOD is a set of ANSI C routines for sparse Cholesky factorization and update/downdate. A MATLAB interface is provided. The performance of CHOLMOD was compared with 10 other codes in a paper by Nick Gould, Yifan Hu, and Jennifer Scott. see also their raw data. Comparing BCSLIB-EXT, CHOLMOD, MA57, MUMPS, Oblio, PARDISO, SPOOLES, SPRSBLKLLT, TAUCS, UMFPACK, and WSMP, on 87 large symmetric positive definite matrices, they found CHOLMOD to be fastest for 42 of the 87 matrices. Its run time is either fastest or within 10% of the fastest for 73 out of 87 matrices. Considering just the larger matrices, it is either the fastest or within 10% of the fastest for 40 out of 42 matrices. It uses the least amount of memory (or within 10% of the least) for 35 of the 42 larger matrices. Jennifer Scott and Yifan Hu also discuss the design considerations for a sparse direct code. CHOLMOD is part of the SuiteSparse sparse matrix suite.
PackageNamelibcholmod3
PackageReleasebp152.44.3
PackageVersion3.0.14
SHA-17AB555F31BCDE8D5C02F8C9B51EBED38E2D9469C
SHA-2561E7DCF4B2A2F2E743FD5D3EB1995ECC9E6FCDA0CB4F5E0B7DF114B3A08FB44C8