Result for 545D4F70956ADAD19A38233A70AE36B2B1E71656

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.12
FileSize1007064
MD5C583EA431CA00DCDA28780DD19B51531
SHA-1545D4F70956ADAD19A38233A70AE36B2B1E71656
SHA-2564647826180BDE1FE22C0F3C17CCC9EEDD0C5A93398431A7CEB143A8660BCEB23
SSDEEP12288:FRy1SFmfiUeuCDy3GUvwOraUrCcf6MFfCK78/PO/W0csUxpEDC/9JDWhPx+8phtJ:FRymUeuCDyWwwmrCcfPaXPYV6DCxLXh
TLSHT11E2519DBE8E0C7ABC4786C33D2E15AB78363253816D62F2CDADDCB7218E36504709956
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
MD5414B876170CCF1306BE45C58B64EC259
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
PackageRelease7.9
PackageVersion3.0.12
SHA-1F66CC8C93EA5839E97DB10110DF515F169848F70
SHA-2566345D36EDF800CA91D83E55B4E3D1B211038E1CCA3A0C5D81496F5CDE6E3F0BC