Result for DF2420D8D4EA58FF334270136C26D9A82C981DB5

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.14
FileSize990680
MD5EA85BEAFEEE1C17B6FD94EB5B3499629
SHA-1DF2420D8D4EA58FF334270136C26D9A82C981DB5
SHA-25647CF1965830301BD40E9CA80B013EE4B8306B760F744B363671CE2100DB1114A
SSDEEP12288:y/yw5m9CyKKXjhPyxXq5gsAWQFHueDJvo5+mJvLv4NASQSpqjx1342aGW0X8+2fE:yeKg0FzDJevLzxQf0Vs7dVLHFw
TLSHT1F82539C7FCE5C79BD4BCAD33E6E6EAB99353312515912F5CCA9CCB7204E32644209892
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
MD5BE0DDB6AD01F035DD4BB174B2C0D002C
PackageArchs390x
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
PackageRelease45.1
PackageVersion3.0.14
SHA-107F3F1BC6BFC7D0450E48C70DFB57B0B89B1BFFF
SHA-256142726F632A4DF5B8E12E4E22DCCFF35754EF869FB5AB16C46957FBBA65C5A49