Result for AC0701724CA07287EF5C9521AA0C7AF7AE3EAC3B

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.2
FileSize208992
MD5E9FE71C9B4A6B5D4C7345CBD4E843223
SHA-1AC0701724CA07287EF5C9521AA0C7AF7AE3EAC3B
SHA-2567EC8CCF4F34C7B54327BC30EF25EF57B5D0D93B82C3BB5A9D457136AF0EC9822
SSDEEP3072:33MJGKV2Z+z6l34x/adD4U0ix6YOE1cslJjpLqU3osZ+7IgSnjDQoRNZwyNgLf3q:33mxm+M34x/04Kx/NVpxQ7xSHQoREyR
TLSHT14814BE58FA0D6A2AE5C5B2BC4C0A4E54F370215CF31770EAE81497FB298283557F2E79
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
MD51CAA8B93D06593ABD3AA18182E077640
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.
PackageMaintainerumeabot <umeabot>
PackageNamelib64levmar2
PackageRelease4.mga9
PackageVersion2.6
SHA-1CBB655B2E8293B78EF16001D990DE83A29355D71
SHA-25665AB30DB4209AC1F59ABB9D01C622D638BC2E5F5CBF9D9B0E9D728A5379704FE