Result for 6AF23D24906A2A4137A0D0E9111D25BE3F37E771

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.12
FileSize1064480
MD5F7B6D5BD402CCCE9E7E3B0CC737198B3
SHA-16AF23D24906A2A4137A0D0E9111D25BE3F37E771
SHA-256A0DB5CC4D09CC9D4F32408E6690BD1897C4546113403B3CC66C85F25C7D6D047
SSDEEP24576:NpMmF/EkhE5F/tkh1kYkKDT7doMmjjaBauF4+f2Sa0uja:jo+Q+8
TLSHT1B9353A57F09204ACC0ABF9345AB97553B6723858832825762FA79D382F7EF116E1B703
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
MD5CA0F9E4CF5E915238FAA33A2AEF9E2B3
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://www.suse.com/
PackageNamelibcholmod3
PackageRelease150100.9.2.3
PackageVersion3.0.12
SHA-17D5E64A666B262765DCC6D37B4DCDAA68D67A042
SHA-2569DD9DBD412900A1ADF3B3D59BE99A10B792684DAECE454A633378DBC4D574040