Key | Value |
---|---|
FileName | ./usr/share/doc/libsuitesparse-dev/CHOLMOD_README.txt |
FileSize | 3171 |
MD5 | FACC21D5BF9BCBF3E57A8B3C7BD1CAA0 |
RDS:package_id | 293685 |
SHA-1 | CC039F87FF576179DD73AD3E1189569F48E17012 |
SHA-256 | 8EDAF18D26559B5AD4C66584AC5DC7BE98BE91E63772F400B0FF00A154D35C11 |
SSDEEP | 96:HngSFq2i1RC3meNvPlZtkJazjR8pZRCu90kACont8q1S:AWFSRC3bvPlZtkJazjRGeU0kACon5S |
TLSH | T10851634EBF8802759263C4D2D11D1D55EB30AAE2A8375C4D6858413C3BA2C7B477F766 |
insert-timestamp | 1678969810.5338743 |
source | RDS.db |
tar:gname | root |
tar:uname | root |
hashlookup:parent-total | 134 |
hashlookup:trust | 100 |
The searched file hash is included in 134 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 | A06B901C3AF9A7B9573BC0FA4F13FF4D |
PackageArch | i586 |
PackageDescription | This packages provides R-Matrix, one of the recommended packages. |
PackageName | R-Matrix |
PackageRelease | 160.46 |
PackageVersion | 1.3.4 |
SHA-1 | 081288AFD62C550D858B250F4895E7B9930FC079 |
SHA-256 | DC8BC89A620D131C179ED3BE28DA265B14D49ED7C805DBAEF93806E139B3B35B |
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 | 0B5472F718014AC4C5B8150047287B40 |
PackageArch | s390x |
PackageDescription | A language and environment for statistical computing and graphics. R is similar to the award-winning S system, which was developed at Bell Laboratories by John Chambers et al. It provides a wide variety of statistical and graphical techniques (linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, ...). R is designed as a true computer language with control-flow constructions for iteration and alternation, and it allows users to add additional functionality by defining new functions. For computationally intensive tasks, C, C++ and Fortran code can be linked and called at run time. |
PackageMaintainer | Fedora Project |
PackageName | R-core |
PackageRelease | 1.el8 |
PackageVersion | 4.1.1 |
SHA-1 | 08E03E073B8E8B10B9E85123DE9D76B5F9C29A78 |
SHA-256 | 253FBBD3E1B70A54CA8956A9E89200B6AAAF2A1900B790294978B3733465CC18 |
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 |
---|---|
FileSize | 3622804 |
MD5 | CF62D9DF8677777F0BCA5A64B4EC2870 |
PackageDescription | GNU R package of classes for dense and sparse matrices This CRAN package provides S4 classes and methods for numerical linear algebra using dense or sparse matrices. The sparse matrix implementation uses code from the LDL sparse matrix package and code from the Metis package of partitioning algorithms. |
PackageMaintainer | Dirk Eddelbuettel <edd@debian.org> |
PackageName | r-cran-matrix |
PackageSection | gnu-r |
PackageVersion | 1.3-4-2 |
SHA-1 | 0E240917A0FDBA58E4A42C04441D3F10C715C9D9 |
SHA-256 | 59C99303B11288186765FC0A9E78851A2E8057373DD538C5E61A29ED3002199E |
Key | Value |
---|---|
MD5 | 929C899A1884F233F6CA878523729123 |
PackageArch | x86_64 |
PackageDescription | This packages provides R-Matrix, one of the recommended packages. |
PackageName | R-Matrix |
PackageRelease | 45.1 |
PackageVersion | 1.3.4 |
SHA-1 | 103A5E857E753287E5609231CA04D4FEAD6AD459 |
SHA-256 | C1D49AE365420A7C8590C60FEA54734A349F3DA1C4BC44886D86005D3267A202 |
Key | Value |
---|---|
MD5 | 3CA9F8B52C81449C328759F6C890F6B7 |
PackageArch | armv7hl |
PackageDescription | `GNU S' - A language and environment for statistical computing and graphics. R is similar to the S system, which was developed at Bell Laboratories by John Chambers et al. It provides a wide variety of statistical and graphical techniques (linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, ...). R is designed as a true computer language with control-flow constructions for iteration and alternation, and it allows users to add additional functionality by defining new functions. For computationally intensive tasks, C, C++ and Fortran code can be linked and called at run time. |
PackageMaintainer | joequant <joequant> |
PackageName | R-base |
PackageRelease | 3.mga9 |
PackageVersion | 4.1.3 |
SHA-1 | 11C37D237DE880893A491B1743C39B4F26AD0060 |
SHA-256 | 7C4816B762595A25E43B183D45A6D760A35EF109039FC7063664902CD248185A |
Key | Value |
---|---|
FileSize | 3649004 |
MD5 | 8235D26193A63A1D72FEEB3CA61BFA14 |
PackageDescription | GNU R package of classes for dense and sparse matrices This CRAN package provides S4 classes and methods for numerical linear algebra using dense or sparse matrices. The sparse matrix implementation uses code from the LDL sparse matrix package and code from the Metis package of partitioning algorithms. |
PackageMaintainer | Dirk Eddelbuettel <edd@debian.org> |
PackageName | r-cran-matrix |
PackageSection | gnu-r |
PackageVersion | 1.3-4-2 |
SHA-1 | 1203B929BB186FEB68571B39776580892AE27A21 |
SHA-256 | B7F928EA8F7BEBC439BA87130C52D0F919E67C1D9130B04B62168F3DF70FD9E6 |
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 |