Key | Value |
---|---|
FileName | ./usr/lib64/liblevmar.so.2.2 |
FileSize | 266016 |
MD5 | C46EC9D7D9B3FC4C5A68AB0D274363CB |
SHA-1 | C13DC6F64A11241E38955E5B794AC18ACBC96CE1 |
SHA-256 | A769B42FA11723A5C0130334844A1926F1C6B4ADF00B867538D825389522B223 |
SSDEEP | 3072:Kgkol0DDED/DJWT9yp4KbDiQuaXNLNXPs0RqMGTxCe6Os/qDcedQpo8Ys3mA03cG:Kg1i9QpJuQT/XGX6PSAedQqAK971 |
TLSH | T18D448D5673089EC3F9815C3FC95E2D11B71D3D8D67209093AC907327BFA9A26870B79A |
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 | 4D99717FE273740D10651689BB3B63CD |
PackageArch | ppc64le |
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 | 12.fc23 |
PackageVersion | 2.5 |
SHA-1 | 151B843F98A44D5FFEDFEA9BEEFFE0FD277C685D |
SHA-256 | AC8A47E46BE8E23D75B14C19E76A9D46F83BB65DE0C36D9F7CF40D9C5E47B1FB |