Result for E4F9CC386E9451EC3B406720C88346E88D19F3B0

Query result

Key Value
FileName./usr/lib/liblevmar.so.2.2
FileSize209624
MD543DD3D4B8935D397C4C3D470ADADBA27
SHA-1E4F9CC386E9451EC3B406720C88346E88D19F3B0
SHA-256F6E4FFCCDFA3D49CF7B0BE3C542C8E8FA9A3AE895672A46228B28B811C0A34FB
SSDEEP3072:eNnUf38zO8mkavGW8RYttt/rFLbRcb1OuJqoqdFGJHP17Xb3fk:NfszW8Yttt/nYqdItt3f
TLSHT199241996B842B871CAD417FB863F8598330307B9D3E278068F118F256AD3E1E1D77A95
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD56CCE01D98B0D936431A1AA42A0865D06
PackageArcharmv5tel
PackageDescriptionlevmar is a native ANSI C implementation of the Levenberg-Marquardt optimization algorithm. Both unconstrained and constrained (under linear equations, inequality and box constraints) Levenberg-Marquardt variants are included. The LM algorithm is an iterative technique that finds a local minimum of a function that is expressed as the sum of squares of nonlinear functions. It has become a standard technique for nonlinear least-squares problems and can be thought of as a combination of steepest descent and the Gauss-Newton method. When the current solution is far from the correct on, the algorithm behaves like a steepest descent method: slow, but guaranteed to converge. When the current solution is close to the correct solution, it becomes a Gauss-Newton method.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease4.fc13
PackageVersion2.5
SHA-10A44124FA01A5609C01F3AC009003C84489F69BC
SHA-256990D3FD54BEEC055A25ECD08D584BC12D1B0FE4737A37D1C1A591345D3763A63
Key Value
MD5768A9DD93DB158100922D8769234F5E0
PackageArcharmv5tel
PackageDescriptionlevmar is a native ANSI C implementation of the Levenberg-Marquardt optimization algorithm. Both unconstrained and constrained (under linear equations, inequality and box constraints) Levenberg-Marquardt variants are included. The LM algorithm is an iterative technique that finds a local minimum of a function that is expressed as the sum of squares of nonlinear functions. It has become a standard technique for nonlinear least-squares problems and can be thought of as a combination of steepest descent and the Gauss-Newton method. When the current solution is far from the correct on, the algorithm behaves like a steepest descent method: slow, but guaranteed to converge. When the current solution is close to the correct solution, it becomes a Gauss-Newton method.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease4.fc13
PackageVersion2.5
SHA-137CD4D1296180E8C0AD0B6CAC1913406855631A7
SHA-2563A9836384767C48D04BD76B83E3A7622F4E7E80E4BF1E7EE276E5192CDD3851B