Key | Value |
---|---|
FileName | ./usr/lib64/liblevmar.so.2.2 |
FileSize | 207328 |
MD5 | F4E7ECF1566179C03AFFDFD3D8AB1FAE |
SHA-1 | 672B8ED1A747550399C12F0F7A3A7B3C8386D527 |
SHA-256 | 2C831E444A201C760265A8FF3E9149F33365B63DBEC9661443AD51D388CF584A |
SSDEEP | 6144:h4Db3N6liRZdbMGG+3a7I8S1Q3FLuGvhnMLqXUeUM:iDbd6YVjXb1YhI4UM |
TLSH | T138149E59EA1AEE27C5C4F736ED9A0E9D3704248A9333718B9000C6F67242BF166F5F19 |
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 | E940A63C7329BD0565578AF6DE5DDAAC |
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 | umeabot <umeabot> |
PackageName | lib64levmar2 |
PackageRelease | 2.mga7 |
PackageVersion | 2.6 |
SHA-1 | 086AC87C37AD753A705CDE221A87AF21C31C78DE |
SHA-256 | 785A451B9681AE2A46FF9DBDD1D9E5D492CA6403C18432589684A1D63C6B3491 |