Result for D1D95340E027B28859481F5C104AEC4D3ED98ADF

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.2
FileSize268208
MD5BFC56E1D18E16B0D25864D571703298C
SHA-1D1D95340E027B28859481F5C104AEC4D3ED98ADF
SHA-25640AC1E0E9C4B948C872F8715F3DBAD6EA1BDE86A27E365BC808E32703841734B
SSDEEP3072:kW/UKdC0R/OCc2134XiweVuQUpc+rocrLZoPeVW+KaO9UTvGkY82MyIlXfpr0Amr:r8aXR/OCc2Y9RIcGPeVM91zIlhfm5X1
TLSHT17B446CC6BB058863FCA48DF0D96D2FF8F36C790668B49013178A67561CB25F5A88739C
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5EC4F7C8013E18A93625777B7594A5052
PackageArchppc64
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
PackageRelease7.fc18
PackageVersion2.5
SHA-111272D8A6F4E09BE98077D128BF43C507B1D8BE9
SHA-25628C21E430ED024143C601A0A09AD09FF2065B0F6340FFD3A2A360808E3BA1C4B