Result for 6B41C179E15E4B27D412ED080181D2359C41681B

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.12
FileSize1064496
MD5C5BF15AC4A597E82C234F8D8251EBAF3
SHA-16B41C179E15E4B27D412ED080181D2359C41681B
SHA-2560629DBD1C55F7A653B19920C5D99ACE265A276CB86BEFE85A80DB17265CE9FC0
SSDEEP24576:SQBWF/EkhgWF/tkh/b63EpnWlNT3ffxf03G+Jj0nFG7J:INNh+l
TLSHT1AF353B57F49204ACC0ABF9305AB97553B6723858832825762FA79D382F7EF116E1B703
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
MD5487136EF099668A4B5147AE23FC95F80
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.
PackageMaintainerhttps://bugs.opensuse.org
PackageNamelibcholmod3
PackageReleaselp152.5.10
PackageVersion3.0.12
SHA-1533A27AC1F6E7E87C034802016015A63D6F65B4E
SHA-256517BA5EDF48C4FA6DAD857B54A51E7E93FC700781CD227D6BDF2EE24FE487374