Result for 3470F3A3E4C06AFE3C3EA19A5F38AF8A721D92C9

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.12
FileSize1007072
MD5CF2484FC37E5B6E7E53FD0D3B54E6944
SHA-13470F3A3E4C06AFE3C3EA19A5F38AF8A721D92C9
SHA-256A6838931D6B5D5E0D6D628016DCE14BF21D9301A3F8650A20AC0E7BE35727B4A
SSDEEP24576:inWnkCKa/eKNgcrhU9wLA38519a26Vqjqz:IMKyeygcrhU9wLAMX9NM
TLSHT1BD2519DBA8E0C7ABC4786C33D2E15AB78363253916D62F2CDBDDCB7208E36504709956
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
MD55107DE150A8233DFD7B2FD5C5AFB4BC1
PackageArchs390x
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.
PackageMaintainerhttps://www.suse.com/
PackageNamelibcholmod3
PackageRelease150100.9.2.3
PackageVersion3.0.12
SHA-1F24AF88B2F82CCB3CBE1B682018E344A97188B12
SHA-256C5029200453CE7226A9F7B76DCF8582DC303E0CB3337C067828EDAA0060C2D88