Result for C5E1A1EBB808F92416663E96E914F327773D074C

Query result

Key Value
FileName./usr/lib/liblevmar.so.2.2
FileSize191688
MD533C8B9040D9D030A23343E5D3F126282
SHA-1C5E1A1EBB808F92416663E96E914F327773D074C
SHA-2568BE386942611A016F5C10BB337EC60F38764D16B66BF8EBC713C8EC7A7D89647
SSDEEP3072:hR6QRxs+LvUnLnnnnJW4bKHFEj0eqmILCvPjBTzMGJuHaSf6Vsv9YZ:36kxsqvUnLnnnneqIWvPNTzf8esv9YZ
TLSHT175147D91BD521C51C8C1D3F3E23FCB55B38646F9E37A3452460093A835ABA26EF7B246
hashlookup:parent-total1
hashlookup:trust55

Network graph view

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
MD54DF1137B2DE810F131FBE45C732A958F
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
PackageRelease3.mga8
PackageVersion2.6
SHA-12C516AD80F00064180E1FB0280404DCCC1873D00
SHA-25626AAB990F14537FB4CA2F2D0AEDAD10F4517FBF002960D2B29D7EF8BA3172ADB