Result for 06FD26C858BAD3A369EDD9C0CAF7B729C5714B52

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.2
FileSize224616
MD55F1FFD5E73D503DB908C85BAC84DBFEB
SHA-106FD26C858BAD3A369EDD9C0CAF7B729C5714B52
SHA-2564BFB9EB39A1B2CF77714B5E16EE942A05B96359383E0C342D9FC14AE2EF7E767
SSDEEP3072:AxdPDcZ9KC05vJCw4SpqSr9Nq887dgKPI9PPV7CPVLHMngNXH0vO6NXHpbwl4kAA:AxdYcr6Sr6TdgKPAPoPlMgFHSEl4kJC
TLSHT1DC242B06F1A358ACC199F530A6F7B567F2323048531DB8E613C6A77029AEE115EC7B1B
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
MD54631EF1EC0BD572F33AB8504CF118262
PackageArchx86_64
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.el6
PackageVersion2.5
SHA-11D41B99BDE01020AAF2FAA945789444EB1F3099E
SHA-256B697D9372AF0C04B8A8C73029A16204D093F2C56AC58482F12C775C60DF615B2