Result for 832E7D17A80C4CE493B60D281E0102C25E2C8BF0

Query result

Key Value
FileName./usr/lib64/libcholmod.so.3.0.13
FileSize1064496
MD5DC1A4BB78C87C8ED21B0E52A725ADEF9
SHA-1832E7D17A80C4CE493B60D281E0102C25E2C8BF0
SHA-256EEA7754FDF4D39AA713F96F21ADEEC015FA4CB8EB283D7BFEF783B97979DF59C
SSDEEP24576:ngP+2F/EkhkpF/tkh10L8C7bdPCLfz8c0K3w4+/BGwW5iJUL:nrC93+vW
TLSHT108353B57F09204ACC0ABF9345AB97553BA723848832925762FA79D382F7EF116D1B703
hashlookup:parent-total1
hashlookup:trust55

Network graph view

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
MD55698970664F3A2CD2F3E1D693FC11877
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
PackageReleaselp150.59.1
PackageVersion3.0.13
SHA-1CBF2BB42AF275CE26D48EDC3724D7CEF2F861435
SHA-2560D71184AEAD16B087F29D708D3800DBE31F45B2EA1339C7EA1E5DDA5A1FC7609