Parents (Total: 44)

The searched file hash is included in 44 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	BE0DDB6AD01F035DD4BB174B2C0D002C
PackageArch	s390x
PackageDescription	CHOLMOD 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.
PackageName	libcholmod3
PackageRelease	45.1
PackageVersion	3.0.14
SHA-1	07F3F1BC6BFC7D0450E48C70DFB57B0B89B1BFFF
SHA-256	142726F632A4DF5B8E12E4E22DCCFF35754EF869FB5AB16C46957FBBA65C5A49

Key	Value
MD5	755E16B96250F75984C4C02FD12B1E8C
PackageArch	x86_64
PackageDescription	CHOLMOD 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.
PackageName	libcholmod3
PackageRelease	85.3
PackageVersion	3.0.14
SHA-1	08C4CEE983936BAF27DC80BF2A38B4EC17D2CD2C
SHA-256	A890C4B91821AF3DD3B509B3325AFB55DCE11920122F26EC8B07D7613182DC3B

Key	Value
MD5	A4E647D07CB53F8DD54326DE6741F34C
PackageArch	x86_64
PackageDescription	CHOLMOD 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.
PackageName	libcholmod3
PackageRelease	bp151.44.1
PackageVersion	3.0.14
SHA-1	094E1602C2ED2D971F4DFFE045C8B8833794A520
SHA-256	CDA294767969A938A52C489AE33EF1015770B3981422B65CC030408EBE3DD68E

Key	Value
FileName	http://dl-cdn.alpinelinux.org/alpine/latest-stable//community//armv7//suitesparse-doc-5.10.1-r1.apk
MD5	FFFE426AB66B96708625E43FBF1FAF6B
SHA-1	13000B39F5D66773AA4C8946A17C962DC0F6CCB4
SHA-256	1F9D8C45D490F5E61190C78A60592B80160E39AADAA90B986D5EC481083B35DA
SSDEEP	49152:CpcNn6detK4Y2HaIgnZsyOYZjfXJ0fJVI8ic56zeCQvdqWGoeCG3XCnebgD:GcNSeYP2x+pZrXci8iondLGpSneby
TLSH	T150B53321A5C98A244C09BAF7DA13BF8E9438BE57100BAF76FB2D85D4C35F1B16421E47

Key	Value
MD5	9370CBE8EB01C1036150A37FA56C84B1
PackageArch	x86_64
PackageDescription	CHOLMOD 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.
PackageName	libcholmod3
PackageRelease	bp153.44.4
PackageVersion	3.0.14
SHA-1	1633BB64B57529B30B6B8C9DF1C42D467D70BF5B
SHA-256	88CB6F19AB0E5AE5265F5F8F97B5AC2997F3E2FEC9B1833572C0B0E8C6ADEAB3

Key	Value
MD5	99336F50913B627DB503D5AD95ECE847
PackageArch	i586
PackageDescription	suitesparse is a collection of libraries for computations involving sparse matrices. The suitesparse-devel package contains files needed for developing applications which use the suitesparse libraries.
PackageName	suitesparse-devel
PackageRelease	45.3
PackageVersion	5.10.1
SHA-1	16F1FC834FBD37AA78AEF3C22FA40BD85719A89D
SHA-256	73A24FDFF8845141213F516CE6EBEF01FFEA127471FFE8985553E1A2B89ECABF

Key	Value
MD5	0EE7F85740BF35C237C50E8C439248E3
PackageArch	x86_64
PackageDescription	CHOLMOD 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.
PackageName	libcholmod3
PackageRelease	lp151.84.1
PackageVersion	3.0.14
SHA-1	202350ADB91E2977204CB81391FD94572582A6F8
SHA-256	824E832965B9D460100E82889958D24D9D1F74161FADEA873F8A0E417E53407F

Key	Value
MD5	C10750D178068E2A59E526D304E963F8
PackageArch	x86_64
PackageDescription	suitesparse is a collection of libraries for computations involving sparse matrices. The suitesparse-devel package contains files needed for developing applications which use the suitesparse libraries.
PackageName	suitesparse-devel
PackageRelease	85.70
PackageVersion	5.10.1
SHA-1	2391DBF2D4A849E2C00A8E4111477510748C3EC3
SHA-256	AA9DB1CE2025D4813F147D5EE18874A988C240181AA3AD3C0A34A9255F9946C7

Key	Value
MD5	5A1CF3E368C35B0CFCB82B2DFDC9D769
PackageArch	i586
PackageDescription	CHOLMOD 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.
PackageMaintainer	https://bugs.opensuse.org
PackageName	libcholmod3
PackageRelease	43.6
PackageVersion	3.0.14
SHA-1	29EDCB6A1347B836F69615A5657B6B366E78B9E0
SHA-256	4832938A6386D1787C669ADB6D9DEFD6AED9050F53FBA774191B4AB25D1AE615

Key	Value
MD5	E98CA68A89CAFF6204DC29430E3FDB90
PackageArch	x86_64
PackageDescription	suitesparse is a collection of libraries for computations involving sparse matrices. The suitesparse-devel package contains files needed for developing applications which use the suitesparse libraries.
PackageName	suitesparse-devel
PackageRelease	45.3
PackageVersion	5.10.1
SHA-1	2BB9CEA0209989AA114D5B5F9FEA82FA9567809E
SHA-256	81AAABA3A6FFB718900000FA9BC333EA9598EB58CD52916EBEBD6D69202D3853