Key | Value |
---|---|
FileName | ./usr/lib64/libcholmod.so.3.0.14 |
FileSize | 1086224 |
MD5 | 7A752AD328F5834915256E33FC1E62EB |
SHA-1 | 199189BEA0769CE2E0D8BE14C5DFFA75B4D447CE |
SHA-256 | C372F1D34BF39B72217201DD8E8D30A07C01673BC29F8A96C19516C6B782EC67 |
SSDEEP | 24576:O9OiF/EkhHBF/tkh1oBq0GZfwoynFEtUXBrh+M3GSvedkTH:WotU3+D |
TLSH | T121353A57F49204ACC0ABF9305AB97553B6723848832925762FA79D382F7EF116E1B703 |
hashlookup:parent-total | 2 |
hashlookup:trust | 60 |
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 |
---|---|
MD5 | B031265A052427FF6E054DDAA83E1667 |
PackageArch | x86_64 |
PackageDescription | CHOLMOD 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. |
PackageName | libcholmod3 |
PackageRelease | bp150.44.1 |
PackageVersion | 3.0.14 |
SHA-1 | 8292EF2135F9C42DCE32E982061AA82FFC9F20C9 |
SHA-256 | 773510A02E8F190DFF99CE2F57E3ACFD32A4FD3BB9789920F5F43F73E652E1EF |
Key | Value |
---|---|
MD5 | A4E647D07CB53F8DD54326DE6741F34C |
PackageArch | x86_64 |
PackageDescription | CHOLMOD 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. |
PackageName | libcholmod3 |
PackageRelease | bp151.44.1 |
PackageVersion | 3.0.14 |
SHA-1 | 094E1602C2ED2D971F4DFFE045C8B8833794A520 |
SHA-256 | CDA294767969A938A52C489AE33EF1015770B3981422B65CC030408EBE3DD68E |