Key | Value |
---|---|
FileName | ./usr/lib/liblevmar.so.2.6 |
FileSize | 132480 |
MD5 | 2648FE18FB5C8D3732B3F72FFE921D10 |
SHA-1 | 95CC6CBE49F314F55DB00BF1AAD2A351636BE153 |
SHA-256 | 2D70C7AEA52C8CFFD70DD853634547AB644AD477B172FEE49D1E3D4CFFF97DC1 |
SSDEEP | 3072:vB9aEp1J1WJUk1LU03eQTEQLGcu3EJBKfvoe28q:J4Ep1/WJUaUmeQgQLGcu3EJBKfvk |
TLSH | T11FD30A99E7C291F0E5D304F5446B633FA6300B05A037F6F1EFCAA351B97161A7E2A264 |
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 | 3A383E422AE85D8B8506FFE5C0E2069D |
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 | https://bugs.opensuse.org |
PackageName | liblevmar2 |
PackageRelease | 1.7 |
PackageVersion | 2.6 |
SHA-1 | 1090DF3997BFEF4735C4D5D831045B72A08FE43B |
SHA-256 | 4E883602424EECB0BC9F8C80712D0265D11867FF8CB0AB19BDD710D228FA8F0A |