Parents (Total: 52)

The searched file hash is included in 52 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	2C43326C617C2ACE56455E20EB5F5509
PackageArch	x86_64
PackageDescription	suitesparse is a collection of libraries for computations involving sparse matrices. The package includes the following libraries: AMD approximate minimum degree ordering BTF permutation to block triangular form (beta) CAMD constrained approximate minimum degree ordering COLAMD column approximate minimum degree ordering CCOLAMD constrained column approximate minimum degree ordering CHOLMOD sparse Cholesky factorization CSparse a concise sparse matrix package CXSparse CSparse extended: complex matrix, int and long int support KLU sparse LU factorization, primarily for circuit simulation LDL a simple LDL factorization SQPR a multithread, multifrontal, rank-revealing sparse QR factorization method UMFPACK sparse LU factorization SuiteSparse_config configuration file for all the above packages. RBio read/write files in Rutherford/Boeing format
PackageMaintainer	Fedora Project
PackageName	suitesparse
PackageRelease	5.fc33
PackageVersion	5.4.0
SHA-1	06D371D6EBB8E577DD4BF23D49DE048B3807B1F7
SHA-256	EA3F7D8D0D6264775E29DF28CB2474D0B5F9F1549251740B6462744D15473AC0

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
MD5	DEA6BA35AB03D3DA921C1A6847E72862
PackageArch	aarch64
PackageDescription	suitesparse is a collection of libraries for computations involving sparse matrices. The package includes the following libraries: AMD approximate minimum degree ordering BTF permutation to block triangular form (beta) CAMD constrained approximate minimum degree ordering COLAMD column approximate minimum degree ordering CCOLAMD constrained column approximate minimum degree ordering CHOLMOD sparse Cholesky factorization CSparse a concise sparse matrix package CXSparse CSparse extended: complex matrix, int and long int support KLU sparse LU factorization, primarily for circuit simulation LDL a simple LDL factorization SQPR a multithread, multifrontal, rank-revealing sparse QR factorization method UMFPACK sparse LU factorization SuiteSparse_config configuration file for all the above packages. RBio read/write files in Rutherford/Boeing format
PackageMaintainer	Fedora Project
PackageName	suitesparse
PackageRelease	5.fc33
PackageVersion	5.4.0
SHA-1	0A8E05A260AF4977FCA7890A0B89A710E8427180
SHA-256	A2BC8D58F7E8D56EFCFC3D10F126B5FDAA2E0FB167FE23D96A88F6A896C4B2C0

Key	Value
MD5	14E83CECAADA5D85B02174822FB1CAA1
PackageArch	i686
PackageDescription	suitesparse is a collection of libraries for computations involving sparse matrices. The package includes the following libraries: AMD approximate minimum degree ordering BTF permutation to block triangular form (beta) CAMD constrained approximate minimum degree ordering COLAMD column approximate minimum degree ordering CCOLAMD constrained column approximate minimum degree ordering CHOLMOD sparse Cholesky factorization CSparse a concise sparse matrix package CXSparse CSparse extended: complex matrix, int and long int support KLU sparse LU factorization, primarily for circuit simulation LDL a simple LDL factorization SQPR a multithread, multifrontal, rank-revealing sparse QR factorization method UMFPACK sparse LU factorization SuiteSparse_config configuration file for all the above packages. RBio read/write files in Rutherford/Boeing format
PackageMaintainer	Fedora Project
PackageName	suitesparse
PackageRelease	6.fc34
PackageVersion	5.4.0
SHA-1	0EED438F5CEE0BD7061DD38ABEF57E7BD3A39438
SHA-256	8FEF5182E19843B5989DC01637CC4839678C8B7FF67E7A05F353AA4E21B54689

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	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	B9F56469CEA5D762B3A9C2424051C04E
PackageArch	i686
PackageDescription	suitesparse is a collection of libraries for computations involving sparse matrices. The package includes the following libraries: AMD approximate minimum degree ordering BTF permutation to block triangular form (beta) CAMD constrained approximate minimum degree ordering COLAMD column approximate minimum degree ordering CCOLAMD constrained column approximate minimum degree ordering CHOLMOD sparse Cholesky factorization CSparse a concise sparse matrix package CXSparse CSparse extended: complex matrix, int and long int support KLU sparse LU factorization, primarily for circuit simulation LDL a simple LDL factorization SQPR a multithread, multifrontal, rank-revealing sparse QR factorization method UMFPACK sparse LU factorization SuiteSparse_config configuration file for all the above packages. RBio read/write files in Rutherford/Boeing format
PackageMaintainer	Fedora Project
PackageName	suitesparse
PackageRelease	3.fc32
PackageVersion	5.4.0
SHA-1	2625D2027D52772DE416A4F6EB3B0DB5BB0BC6D4
SHA-256	07CFE01177ED481017D3BA9534F9F96C62BC3B8D7A33FBC62BE306F1CFEE43FC

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