Result for AF2399D25398FEB24E5B192997774A222F782B4B

Query result

Key Value
FileName./usr/lib/liblevmar.so.2.2
FileSize203360
MD54E981EA9F651DA2EDDE429D0D6E8F2C1
SHA-1AF2399D25398FEB24E5B192997774A222F782B4B
SHA-256D67D977074C25C5071CE64BD38014D9A255F7F6B9777072D55663DF90E16848F
SSDEEP3072:Uq2uQHdsX53UT+lqZcRyFQDlM8zUGYsBXMEyJ4/R2izpJkLBLWZ:UqhX53GnR8hBcEyJ4/R2izpJkLBLWZ
TLSHT146147ECAB9C1C783D8B621B80B9BDF5B371EA2717A349CBE4B355362145A0D4330BE65
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
MD56474872018010BA971336D7FD29A0FE1
PackageArchs390
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
PackageRelease8.fc19
PackageVersion2.5
SHA-126B94A86D5C56204F3B13B0CB9C5EF56801E2077
SHA-256D2985B91A482D1F2E042330EC1043E0588B55D0DB5E3D671755DCC5F95F28E78