Result for CDA8E78A0B8885917F5C0D858E529A2EBEA24C92

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.6
FileSize123560
MD57AA0B5945847C7542F4C95C7A8DCECD4
SHA-1CDA8E78A0B8885917F5C0D858E529A2EBEA24C92
SHA-256C48B3112A5E499D7A3D4D02AA50CC11019473249E79EBDFC64EB8E04D8BDC165
SSDEEP3072:62uM0AEbw5ofiHEdqW06XQJCUpDKg/sfliDUsli:PuvAmw5oqHELMD3sfliQsli
TLSHT157C34A87F2B344A8C0D1D474A269B117B6317409653DBB3B5B80D2702E7FB192EAFB64
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
MD55347F1E6512760C9B9C7C20F5648B01D
PackageArchx86_64
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
PackageRelease3.epel8.playground
PackageVersion2.6
SHA-10DA574132C6B879C3CE78C2697970A5B4A6CD034
SHA-25681287E07BB1A1212D99516701F5CFA026DF7FA5855A60D10567A7789131192FA