Result for 57D0BB0D866910AF5BDDA78300BE2850B9AD117D

Query result

Key Value
FileName./usr/lib/liblevmar.so.2.6
FileSize130660
MD5E7411B311C2D2EA99FEC57824CF212CD
SHA-157D0BB0D866910AF5BDDA78300BE2850B9AD117D
SHA-2566EB5D4F9794FD9D7AD7F0F04384887A34A670AF6205BEE19726C9F2322CBB008
SSDEEP3072:yH8sfh43BCYpsv/q9KQCFtgTeV1xut4x9sl+3+5quZeSKvvri/z:yHYdps695CFtgTAfut4x9sli+5quUvrs
TLSHT1A1D33A45F782A5B0E1D310F0025F36AB23201605B177F5B3F7C67B95A87A6923A9B339
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
MD5C26B0DAE8BC60D8E8AA106D69F3BD45F
PackageArchi686
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.fc33
PackageVersion2.6
SHA-1EF49112733544BB0309AB1B4A10735934B70F098
SHA-2568E6ED717135570A22E2ABDF19CB8203713186188A254C6DB9C0666F72C053547