Result for D1C7AF8AB1AB006E6D35C1B2793E2F05C2D44F70

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.2
FileSize200992
MD57B6933FAE97A108C8B12DEDA9ED24A34
SHA-1D1C7AF8AB1AB006E6D35C1B2793E2F05C2D44F70
SHA-256BC17D56B507D7672C8A70FCF0F2546A2A0D77FC4B9CFA8B9A6E65673B7EB717C
SSDEEP3072:3pLfGBt8Nsz2CSVBzldB4fEATrBvpzIxQNEo232jFjQZiP4olJnAis:3JGvDzMVBzld2swlvpyVMKroO
TLSHT193147D096B19FE66C1C4FB35EE8E4E9C7705289E9333619FE100C1F635829F5937AA09
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
MD52E67622A910035A39787A1F31942C3FA
PackageArchaarch64
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
PackageRelease13.fc24
PackageVersion2.5
SHA-1265900CBC9A993AF36E92DFF94EFB66A428402E9
SHA-256CF2AC326903D6EC29B0B2BB8803F8AA7A6F1A82BB0ED192B1F61484126E18B4A