Result for C57F145ADF029E1CE7120DE2426954F51B08B69F

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.6
FileSize135928
MD53195461876A68995B5657D515AC44B57
SHA-1C57F145ADF029E1CE7120DE2426954F51B08B69F
SHA-256A1BEBAEE07759841F51104BFB6961F91D19493867DC463A8B0C6B764881ACF72
SSDEEP1536:OeVieR/X0McbpzOWOJ4DwBjbzv3poM4TFkbUzP8wlTyE79+/zLKKW6rtT:HVpRlcbkWY4Dwl/WMiEUzflWb
TLSHT125D36D0DBE0E6812C1C1B778A34D4A5C733E2254F75670F32046D3EC5A0AEA69BB7A65
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

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
MD523C7CEA8AFFFD7CF8F0F6F8987E7C30F
PackageArchaarch64
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease6.fc33
PackageVersion2.6
SHA-1FF1EB3AFBF29BA7F7174167B07D8889F7FEFECC1
SHA-256CADF0CAA21A9C7FC78E20709A46FB707EBEF6BB0B4FBABE3EA0399FF2F2BC14C