Result for C13DC6F64A11241E38955E5B794AC18ACBC96CE1

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.2
FileSize266016
MD5C46EC9D7D9B3FC4C5A68AB0D274363CB
SHA-1C13DC6F64A11241E38955E5B794AC18ACBC96CE1
SHA-256A769B42FA11723A5C0130334844A1926F1C6B4ADF00B867538D825389522B223
SSDEEP3072:Kgkol0DDED/DJWT9yp4KbDiQuaXNLNXPs0RqMGTxCe6Os/qDcedQpo8Ys3mA03cG:Kg1i9QpJuQT/XGX6PSAedQqAK971
TLSHT18D448D5673089EC3F9815C3FC95E2D11B71D3D8D67209093AC907327BFA9A26870B79A
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
MD54D99717FE273740D10651689BB3B63CD
PackageArchppc64le
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
PackageRelease12.fc23
PackageVersion2.5
SHA-1151B843F98A44D5FFEDFEA9BEEFFE0FD277C685D
SHA-256AC8A47E46BE8E23D75B14C19E76A9D46F83BB65DE0C36D9F7CF40D9C5E47B1FB