Result for 199189BEA0769CE2E0D8BE14C5DFFA75B4D447CE

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.14
FileSize1086224
MD57A752AD328F5834915256E33FC1E62EB
SHA-1199189BEA0769CE2E0D8BE14C5DFFA75B4D447CE
SHA-256C372F1D34BF39B72217201DD8E8D30A07C01673BC29F8A96C19516C6B782EC67
SSDEEP24576:O9OiF/EkhHBF/tkh1oBq0GZfwoynFEtUXBrh+M3GSvedkTH:WotU3+D
TLSHT121353A57F49204ACC0ABF9305AB97553B6723848832925762FA79D382F7EF116E1B703
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5B031265A052427FF6E054DDAA83E1667
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
PackageReleasebp150.44.1
PackageVersion3.0.14
SHA-18292EF2135F9C42DCE32E982061AA82FFC9F20C9
SHA-256773510A02E8F190DFF99CE2F57E3ACFD32A4FD3BB9789920F5F43F73E652E1EF
Key Value
MD5A4E647D07CB53F8DD54326DE6741F34C
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
PackageReleasebp151.44.1
PackageVersion3.0.14
SHA-1094E1602C2ED2D971F4DFFE045C8B8833794A520
SHA-256CDA294767969A938A52C489AE33EF1015770B3981422B65CC030408EBE3DD68E