Key | Value |
---|---|
FileName | ./usr/lib/liblevmar.so.2.2 |
FileSize | 218752 |
MD5 | 04AA62D73F50D9E3A9F611BAD2499EB7 |
SHA-1 | 85F8770A759E800CB4CB3626A32FFFF0CF7EDE42 |
SHA-256 | 93736ABAC713527F5D3A66D8573B74E8A5666291EC77D0BEC0BD77D8E779C6F8 |
SSDEEP | 6144:woQxvUPToPPPPup2jcqsOM1B2klYA4fj3uu:nQtUPToPPPPSSY2kqfj3uu |
TLSH | T161246C86E7C64198D0D31CB195A7E73BF6249F452123F7F4ABDEAB01A934B1B3D28244 |
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 | AA46846E9C7C7C20BB5758E34346AB95 |
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 | 2.mga7 |
PackageVersion | 2.6 |
SHA-1 | 7E2F2C667B9FA5EB68DDE492DCB98379B3C38130 |
SHA-256 | F06120634A1754160E14C804819A5C73C91FC1FE50D4ED2DD325FE478794A0C7 |