Key | Value |
---|---|
FileName | ./usr/lib/liblevmar.so.2.2 |
FileSize | 220472 |
MD5 | 84F6CB397510C2778D692157D612ABD7 |
SHA-1 | 5F32023D31CC8D865C2E3B9F1B00ADF5E7F91970 |
SHA-256 | E75413411A5B8FC45C3C3B497AFD5F2E3C0937086DEE146E1B15FD6332619D50 |
SSDEEP | 3072:J7b8u48EpWb6S9gtrPPPPUQBdG4hsEC2np5QtmWe0drKWuie2Tq9:mLZc6ciPPPPUQBdRHC2npem70dr5ux |
TLSH | T1C0247D82E7C682D4D0D75DB150B3F637FA246F426123F6F0ABDAAB01A934B5B3D24254 |
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 | 91956BF3EECE6A35C587E3CFEA8B04D6 |
PackageArch | i586 |
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 | umeabot <umeabot> |
PackageName | liblevmar2 |
PackageRelease | 3.mga8 |
PackageVersion | 2.6 |
SHA-1 | 010905BB070C2FD141F9A8A81DEE5E2CA1832946 |
SHA-256 | 2499251F157218E033EB685EFF3723E768938C2C248869FE4A8CA395A139E3E4 |