Key | Value |
---|---|
FileName | ./usr/lib64/liblevmar.so.2.6 |
FileSize | 135712 |
MD5 | 2234E7E022F00853D238250B23DCBAA7 |
SHA-1 | 6EB8453A3BC5AD808AFD8C38BFF1E8CAE449CAC8 |
SHA-256 | B30057FD38F019834294C811DD350A959D400081DB2F7BBAEF97C42C3577FD3A |
SSDEEP | 1536:qhCFqcQIhJoqX7FLETMAKwhVcvy/Yj9m3PZZ9f2jFwzT5Hz4pWWLqB1LqKW67RVh:M1qX7FwMARVcvyG9m3Pwo4pY |
TLSH | T1C9D37D997E1DAC03C0C1B374A78D4E6873372251A76264F33006C3EC5E47AE6DEA7666 |
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 | 1917971E3D05AE04362A9EA3336CBF66 |
PackageArch | aarch64 |
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.el8 |
PackageVersion | 2.6 |
SHA-1 | 3F82688B1B08CC2C31421812DA58666A9871566C |
SHA-256 | 4068553AF9A761BF912049408298CD2BA845292D9A3A1A3F727A22F40C16138C |