Result for 6EB8453A3BC5AD808AFD8C38BFF1E8CAE449CAC8

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.6
FileSize135712
MD52234E7E022F00853D238250B23DCBAA7
SHA-16EB8453A3BC5AD808AFD8C38BFF1E8CAE449CAC8
SHA-256B30057FD38F019834294C811DD350A959D400081DB2F7BBAEF97C42C3577FD3A
SSDEEP1536:qhCFqcQIhJoqX7FLETMAKwhVcvy/Yj9m3PZZ9f2jFwzT5Hz4pWWLqB1LqKW67RVh:M1qX7FwMARVcvyG9m3Pwo4pY
TLSHT1C9D37D997E1DAC03C0C1B374A78D4E6873372251A76264F33006C3EC5E47AE6DEA7666
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
MD51917971E3D05AE04362A9EA3336CBF66
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
PackageRelease3.el8
PackageVersion2.6
SHA-13F82688B1B08CC2C31421812DA58666A9871566C
SHA-2564068553AF9A761BF912049408298CD2BA845292D9A3A1A3F727A22F40C16138C