Result for 974DE23783F97BB806C49B7B9C6A029FA097D43F

Query result

Key Value
FileName./usr/lib/liblevmar.so.2.2
FileSize187576
MD552CB5609B0A74BB3A523D3CCB624C919
SHA-1974DE23783F97BB806C49B7B9C6A029FA097D43F
SHA-2564AB9D5E6D0DD4FCACE4DD345E9D95A7DC0B59B4D111DB303B245CD8D873513B3
SSDEEP3072:HErDT3gFanna6Rbq6/7VfQRRwiiii+cACzxo2tFL3kiJwJFLVD9Gh9WQ2aU5NnTN:HsDgYnnHRbqaeRRwiiiioYo4J39+nxDD
TLSHT134046BC27D835E50C9C1E3B3E53ECB94734307F5E3667403861087A43A9BA5AAF7AA45
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
MD5CBBF780B66734536FA1F1ACF845D0328
PackageArcharmv7hl
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
PackageRelease4.mga9
PackageVersion2.6
SHA-172079B1FDF4F76DDC432233452346B38D7EFA714
SHA-2560219C67AF97C6D58DB11A51D08C3BBFCEC988108712C9F26EAAE4CC9931A083B