Key | Value |
---|---|
FileName | ./usr/lib64/liblevmar.so.2 |
FileSize | 124808 |
MD5 | 88D6D52D947F8D14B59C50BE5AF9B2FD |
SHA-1 | 8B4AE6CBC211971F1F96F5A6C57C7ADA8AA5F840 |
SHA-256 | 0E850EA52B3A13ED6B80AB076AA9D9660D83BC2F431C4C9B4FD6CFC86F99B1E1 |
SSDEEP | 3072:ErU7OmbAh1PoB9XmEWJ67Oei48IwMmeyCBBr:vimgCZd7a48IbnNBr |
TLSH | T171C32ACBB9A187A7C0B02D77C35E67F6531729393AC67D2EA7A9D7300C13510AB06B52 |
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 | 35772295A30AF340465E97DFA2BD1DA3 |
PackageArch | s390x |
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 | bp156.2.6 |
PackageVersion | 2.6 |
SHA-1 | 00C6AFE5A7AAB8D27BD62212A48F319D3C4EE7B4 |
SHA-256 | A85E5A45764761B0A10FCEE1A4BA052AC97E93BB1CFA00312E32ACCC447E5FC8 |