Key | Value |
---|---|
FileName | ./usr/lib64/liblevmar.so.2.2 |
FileSize | 208992 |
MD5 | E9FE71C9B4A6B5D4C7345CBD4E843223 |
SHA-1 | AC0701724CA07287EF5C9521AA0C7AF7AE3EAC3B |
SHA-256 | 7EC8CCF4F34C7B54327BC30EF25EF57B5D0D93B82C3BB5A9D457136AF0EC9822 |
SSDEEP | 3072:33MJGKV2Z+z6l34x/adD4U0ix6YOE1cslJjpLqU3osZ+7IgSnjDQoRNZwyNgLf3q:33mxm+M34x/04Kx/NVpxQ7xSHQoREyR |
TLSH | T14814BE58FA0D6A2AE5C5B2BC4C0A4E54F370215CF31770EAE81497FB298283557F2E79 |
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 | 1CAA8B93D06593ABD3AA18182E077640 |
PackageArch | aarch64 |
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 | CBB655B2E8293B78EF16001D990DE83A29355D71 |
SHA-256 | 65AB30DB4209AC1F59ABB9D01C622D638BC2E5F5CBF9D9B0E9D728A5379704FE |