Result for B2CDFEBD12BA09EE7EBEAF3097E13A069B3709B7

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.14
FileSize1064496
MD582CEC269F66A5E37A0AC4302A4F40A73
SHA-1B2CDFEBD12BA09EE7EBEAF3097E13A069B3709B7
SHA-256CA3F2E697251149C696857A54BBE90F7FEFB821296FD8D8D0289ABA12521D6DE
SSDEEP24576:muiF/EkhnhF/tkh1ov6k2JHEb43Hs0fvm393M+GeMqDWdD:zw4zXmu+4
TLSHT18A353B57F09204ACC0ABF9345AB97553B6723848832925762FA79D382F7EF116E1B703
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
MD50EE7F85740BF35C237C50E8C439248E3
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
PackageReleaselp151.84.1
PackageVersion3.0.14
SHA-1202350ADB91E2977204CB81391FD94572582A6F8
SHA-256824E832965B9D460100E82889958D24D9D1F74161FADEA873F8A0E417E53407F