Result for 6DE1788AFE5A17E8339E57398568CEA57260DBB2

Query result

Key Value
FileName./usr/lib/liblevmar.so.2.2
FileSize210424
MD550C8A947887E3F28779C7B1215E382C8
SHA-16DE1788AFE5A17E8339E57398568CEA57260DBB2
SHA-25639C0F5AC191B088A2FC3224731F9C8F304808655D7B501DB4BD5394FEA99589D
SSDEEP3072:+4xoezAM/WgLtwiBfr8C8hYoHHiBSSLlbec4rUUhiaMBYCtheHlJlZmG:7xoe8MeMHj8CQYoHHWLJqeaMBP2JlZ3
TLSHT12F244C88AF0D0813FEB24EF4DDAF5FD5D31C8A4B78A5A005008AE7186975E718A677DC
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
MD579DAF1306B09AD0C6E7A49181B8DDCE8
PackageArchppc
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.
PackageMaintainerKoji
PackageNamelevmar
PackageRelease5.fc15
PackageVersion2.5
SHA-1EAD225831E3A37A7B465F53612F6496E53824402
SHA-25600E57DE10512094563872DF6C7AB9CDD3743A8F392BCC3DE61641111276BF17F
Key Value
MD589DE2AA32E8E4338B7E32569424FFC79
PackageArchppc
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.
PackageMaintainerKoji
PackageNamelevmar
PackageRelease5.fc15
PackageVersion2.5
SHA-12B0EE3AD33F23C00333B03AF007C7FD2CABB4BA9
SHA-25695DF5038CB8D57EB2F31D7703ADEC979521DB6E85B76099E542892C1AE985F0B