Result for 9609BDBEAF4C132270973BD5BEACBC587E2C0850

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.14
FileSize1085624
MD5A92C7C8538C83539CD89B135205C607E
SHA-19609BDBEAF4C132270973BD5BEACBC587E2C0850
SHA-2569862828DDA41929A6B8B26EE793C8F0D640B55AB761DA4E5640B8584653E35EA
SSDEEP24576:WAiF/EkhnhF/tkh1ov6k2JH8b43Hs0/tBJzk2+lbpfK+dJf:xY4zVBv+v
TLSHT18C353A57F49204ACC0ABF9305AB97553B6723848832925762FA79D382F7EF116E1B703
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
MD59370CBE8EB01C1036150A37FA56C84B1
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
PackageReleasebp153.44.4
PackageVersion3.0.14
SHA-11633BB64B57529B30B6B8C9DF1C42D467D70BF5B
SHA-25688CB6F19AB0E5AE5265F5F8F97B5AC2997F3E2FEC9B1833572C0B0E8C6ADEAB3