Result for A36D5A9F2F9FBC1A1B64BEF61DA51393E1750C22

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.14
FileSize1064496
MD55160B7B22BADF57BCC96477383D90EE7
SHA-1A36D5A9F2F9FBC1A1B64BEF61DA51393E1750C22
SHA-2560A988333A0359C49FA5469123665658B308A1350BD2107B07F35231FEBA99948
SSDEEP24576:l2iF/EkhHBF/tkh1oBq0GZYwoynFTlEXB4h+MFbavedk:UocEk+O
TLSHT136353B57F49204ACC0ABF9345AB97553B6723848832825762FA79D382F7EF116E1B703
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
MD5755E16B96250F75984C4C02FD12B1E8C
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
PackageRelease85.3
PackageVersion3.0.14
SHA-108C4CEE983936BAF27DC80BF2A38B4EC17D2CD2C
SHA-256A890C4B91821AF3DD3B509B3325AFB55DCE11920122F26EC8B07D7613182DC3B