Key | Value |
---|---|
FileName | ./usr/lib64/liblevmar.so.2.6 |
FileSize | 123560 |
MD5 | 7AA0B5945847C7542F4C95C7A8DCECD4 |
SHA-1 | CDA8E78A0B8885917F5C0D858E529A2EBEA24C92 |
SHA-256 | C48B3112A5E499D7A3D4D02AA50CC11019473249E79EBDFC64EB8E04D8BDC165 |
SSDEEP | 3072:62uM0AEbw5ofiHEdqW06XQJCUpDKg/sfliDUsli:PuvAmw5oqHELMD3sfliQsli |
TLSH | T157C34A87F2B344A8C0D1D474A269B117B6317409653DBB3B5B80D2702E7FB192EAFB64 |
hashlookup:parent-total | 1 |
hashlookup:trust | 55 |
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 |
---|---|
MD5 | 5347F1E6512760C9B9C7C20F5648B01D |
PackageArch | x86_64 |
PackageDescription | levmar 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. |
PackageMaintainer | Fedora Project |
PackageName | levmar |
PackageRelease | 3.epel8.playground |
PackageVersion | 2.6 |
SHA-1 | 0DA574132C6B879C3CE78C2697970A5B4A6CD034 |
SHA-256 | 81287E07BB1A1212D99516701F5CFA026DF7FA5855A60D10567A7789131192FA |