Result for 21C897A6904DC0E66D8DA6048F8340FF0794FD97

Query result

Key Value
FileName./usr/lib/liblevmar.so.2.6
FileSize101856
MD5729824048EC17AD0AF90D6C93F1C7538
SHA-121C897A6904DC0E66D8DA6048F8340FF0794FD97
SHA-2563AD110F6483078EEFA25721203411A344C2A9DFB864924DAF7EDEC625BC59284
SSDEEP1536:E9JQft01BD7yDl6GQscwa7AJ9Ar6qQr/+xL+sNDsHO8nXrLemnaDO:1t01BD7O0scwa7AJXqQr/+d/OXrLXZ
TLSHT15AA33A8ABC419FA1C8C092F5933D979833031BB1D3A772579416E7346B9A52E0F3BB49
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
MD505BE1DE5F8692F11CE13E61F137EE5A2
PackageArcharmv7hl
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
PackageRelease6.fc33
PackageVersion2.6
SHA-103C32B473726C70220E95B5855336F6113292815
SHA-25691264CB306509A3ECA0D1215702B07A4528131EA7A7949FC6A323CD3F79A6DFD