Key | Value |
---|---|
FileName | ./usr/lib64/liblevmar.so.2.2 |
FileSize | 268200 |
MD5 | B0BE2F3FE5BCDB4E69D9A7B9BC06774E |
SHA-1 | C5FAD9028E40011CC0C4A284EFA4F5069D6693AE |
SHA-256 | 8F57324A2ADD9847FE6471DFE65E970A78BD15E658E68BD7332A2E6C8F47404A |
SSDEEP | 6144:NBHlyo88F2xDLMX4u3i/qJoVuJ42/Zdr:LUmNawKuJ42/Z |
TLSH | T195447BC27F048C53FDA48DF4D9AE2FB9F36CB90664749027178A662318B25B568873DC |
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 | 756DCAF2C978C7A5EE6D3C7598C55012 |
PackageArch | ppc64 |
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 | 8.fc19 |
PackageVersion | 2.5 |
SHA-1 | 0C7F47A1024F457B31C09C3D8326B755D3D51490 |
SHA-256 | 723C3F11356EB119F0E7AD21D49ED76778F8E94E3FEEF84755653EEF58927460 |