Result for 5DD92327FC157DD57BB2EDE96188467EF515E7D0

Query result

Key Value
FileName./usr/lib/libcholmod.so.3.0.14
FileSize846008
MD5B96A1C4A931FE755C027AA9B25CD431F
SHA-15DD92327FC157DD57BB2EDE96188467EF515E7D0
SHA-25623D0ABC42DDBD4E8FAA413D45C0B5438A0EE10879B5FD1CB7D07AF59D1944E70
SSDEEP24576:C9WErH7EMIYQkMjvSGSBjPgXWxSiTKDM1GMnG:ySYQkMjoBjXQAn
TLSHT158054B94EEC741F1F68358F21267A72B8B342F128029F6F1FB4AA707B575A52BD1D210
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
MD55A1CF3E368C35B0CFCB82B2DFDC9D769
PackageArchi586
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
PackageRelease43.6
PackageVersion3.0.14
SHA-129EDCB6A1347B836F69615A5657B6B366E78B9E0
SHA-2564832938A6386D1787C669ADB6D9DEFD6AED9050F53FBA774191B4AB25D1AE615