Result for C617ABBEA787ED45EDEE22BB74D972E5FA191219

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.13
FileSize1064496
MD5AA6BC51403824DF1C3E8A8B722F25F2C
SHA-1C617ABBEA787ED45EDEE22BB74D972E5FA191219
SHA-256E37E3ECECC16926BDF09369E114E7EE1783358BF61E5F63D336413713DC148FB
SSDEEP24576:JgP+2F/EkhkpF/tkh10L8C7bdPCLfz8c0K3w4+/BGwW5iJUL:JrC93+vW
TLSHT1D0353B57F09204ACC0ABF9345AB97553BA723848832925762FA79D382F7EF116D1B703
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
MD5CD987399C8D97104AF04B58EA9B3EFBD
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.
PackageNamelibcholmod3
PackageRelease59.1
PackageVersion3.0.13
SHA-12F521AF0FE6CF336FF1D427E9E3D4569699F871F
SHA-2563FF0C07778CE78AB9426A777CFD586F6A1EAA076BECC9CC308868E45307CB6EA