Key | Value |
---|---|
FileName | ./usr/lib/liblevmar.so.2.2 |
FileSize | 210424 |
MD5 | 50C8A947887E3F28779C7B1215E382C8 |
SHA-1 | 6DE1788AFE5A17E8339E57398568CEA57260DBB2 |
SHA-256 | 39C0F5AC191B088A2FC3224731F9C8F304808655D7B501DB4BD5394FEA99589D |
SSDEEP | 3072:+4xoezAM/WgLtwiBfr8C8hYoHHiBSSLlbec4rUUhiaMBYCtheHlJlZmG:7xoe8MeMHj8CQYoHHWLJqeaMBP2JlZ3 |
TLSH | T12F244C88AF0D0813FEB24EF4DDAF5FD5D31C8A4B78A5A005008AE7186975E718A677DC |
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 | 79DAF1306B09AD0C6E7A49181B8DDCE8 |
PackageArch | ppc |
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 | Koji |
PackageName | levmar |
PackageRelease | 5.fc15 |
PackageVersion | 2.5 |
SHA-1 | EAD225831E3A37A7B465F53612F6496E53824402 |
SHA-256 | 00E57DE10512094563872DF6C7AB9CDD3743A8F392BCC3DE61641111276BF17F |
Key | Value |
---|---|
MD5 | 89DE2AA32E8E4338B7E32569424FFC79 |
PackageArch | ppc |
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 | Koji |
PackageName | levmar |
PackageRelease | 5.fc15 |
PackageVersion | 2.5 |
SHA-1 | 2B0EE3AD33F23C00333B03AF007C7FD2CABB4BA9 |
SHA-256 | 95DF5038CB8D57EB2F31D7703ADEC979521DB6E85B76099E542892C1AE985F0B |