Key | Value |
---|---|
FileName | ./usr/lib64/liblevmar.so.2.2 |
FileSize | 245968 |
MD5 | E8BD162F7743BAE4D6710383F06665AE |
SHA-1 | 483AEDA2A62256B9F06FDEBAC714CB346636EFC8 |
SHA-256 | 52E750EEB35EC40F6169F5DD04B88E43CCCAB8DB553C77F53CF43940FFBFC7AA |
SSDEEP | 6144:LKy8vSobIdQaFzMX58v/zCo2I42+ujLehFTD:uy8auQMX58nzCo2I42LjChFT |
TLSH | T1CC344B47B08228ECD4E5757172FA712B72323009571DAAD213E29F602B2AE117ED776F |
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 | 2E743EE93FACC2C508487F233077BA47 |
PackageArch | x86_64 |
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 | 4.mga9 |
PackageVersion | 2.6 |
SHA-1 | 57AA08C62BB13ACAA3388371E8D3BB25ECBC6433 |
SHA-256 | 1F32AE07FD19974DEEE34508676492D718D618E8998005C237A81958B688DD37 |