Result for 8CC902BF74CB04D50118D7F97BE1EB03022CB4D1

Query result

Key Value
FileName./usr/lib/liblevmar.so.2.2
FileSize194080
MD52C8D16BDF7862209554D8B059C7F371A
SHA-18CC902BF74CB04D50118D7F97BE1EB03022CB4D1
SHA-256606468144728C740EE91699D944F38D7FCB83F8835348D88CB0FCC3FA0862A64
SSDEEP3072:50zVJdqqFQlUpsnhnnnwFrQeVTV9poVi49B685qrGFrvk84sFsjmamPVcvn/nnYj:azVJdqqulUpsnhnnnwFceJik4350ervv
TLSHT134146CD2BD520C50C9C1D3F3923FCB15B38746B5E37A7143461097A832A7A1AAF7B686
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
MD59858C1342DFD50547A85B1EEEE868CD3
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
PackageRelease2.mga7
PackageVersion2.6
SHA-1544B8C9F54ACFCC9E34770DC90C74B5B48F7A1F1
SHA-256964F99F69FBC9E5FB3659E768AEB5F56F1D34ADD7A7CAF4BD79D0535E54DBF24