Result for 1EA664F29D29220A7DCF8C818AEF2BA427DD527E

Query result

Key Value
FileName./usr/lib/libcholmod.so.3.0.14
FileSize846008
MD5998EA38BE38A86EE71BB1CF66063C9F9
SHA-11EA664F29D29220A7DCF8C818AEF2BA427DD527E
SHA-256D796810D644397BCE75416418A8392EE16F9E37F09C56C7B3D08F5D089A4C2C3
SSDEEP24576:8dWkLHUkbFvoJjAO0S46nhIC+lsoUjsHYeAJC:sZvoJjX4wP7J
TLSHT1F6054AA4EEC741F1F68358F21267A72B8B342F128029F6F1FB4AA707B575652BD1D210
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
MD56386504BB6444FC219E45F2627F0D925
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.
PackageNamelibcholmod3
PackageRelease45.3
PackageVersion3.0.14
SHA-14ECDA9DC8056C818B9C005DAD42560F331ED4242
SHA-256418F6C07F64523498F525E3939C5943897FF0046752C1663382970A59C63A071