| Key | Value |
|---|---|
| FileName | ./usr/lib/liblevmar.so.2.2 |
| FileSize | 209624 |
| MD5 | 43DD3D4B8935D397C4C3D470ADADBA27 |
| SHA-1 | E4F9CC386E9451EC3B406720C88346E88D19F3B0 |
| SHA-256 | F6E4FFCCDFA3D49CF7B0BE3C542C8E8FA9A3AE895672A46228B28B811C0A34FB |
| SSDEEP | 3072:eNnUf38zO8mkavGW8RYttt/rFLbRcb1OuJqoqdFGJHP17Xb3fk:NfszW8Yttt/nYqdItt3f |
| TLSH | T199241996B842B871CAD417FB863F8598330307B9D3E278068F118F256AD3E1E1D77A95 |
| hashlookup:parent-total | 2 |
| hashlookup:trust | 60 |
The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:
| Key | Value |
|---|---|
| MD5 | 6CCE01D98B0D936431A1AA42A0865D06 |
| PackageArch | armv5tel |
| 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 | 4.fc13 |
| PackageVersion | 2.5 |
| SHA-1 | 0A44124FA01A5609C01F3AC009003C84489F69BC |
| SHA-256 | 990D3FD54BEEC055A25ECD08D584BC12D1B0FE4737A37D1C1A591345D3763A63 |
| Key | Value |
|---|---|
| MD5 | 768A9DD93DB158100922D8769234F5E0 |
| PackageArch | armv5tel |
| 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 | 4.fc13 |
| PackageVersion | 2.5 |
| SHA-1 | 37CD4D1296180E8C0AD0B6CAC1913406855631A7 |
| SHA-256 | 3A9836384767C48D04BD76B83E3A7622F4E7E80E4BF1E7EE276E5192CDD3851B |