Result for 5F32023D31CC8D865C2E3B9F1B00ADF5E7F91970

Query result

Key Value
FileName./usr/lib/liblevmar.so.2.2
FileSize220472
MD584F6CB397510C2778D692157D612ABD7
SHA-15F32023D31CC8D865C2E3B9F1B00ADF5E7F91970
SHA-256E75413411A5B8FC45C3C3B497AFD5F2E3C0937086DEE146E1B15FD6332619D50
SSDEEP3072:J7b8u48EpWb6S9gtrPPPPUQBdG4hsEC2np5QtmWe0drKWuie2Tq9:mLZc6ciPPPPUQBdRHC2npem70dr5ux
TLSHT1C0247D82E7C682D4D0D75DB150B3F637FA246F426123F6F0ABDAAB01A934B5B3D24254
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
MD591956BF3EECE6A35C587E3CFEA8B04D6
PackageArchi586
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.
PackageMaintainerumeabot <umeabot>
PackageNameliblevmar2
PackageRelease3.mga8
PackageVersion2.6
SHA-1010905BB070C2FD141F9A8A81DEE5E2CA1832946
SHA-2562499251F157218E033EB685EFF3723E768938C2C248869FE4A8CA395A139E3E4