Result for D54B4D3750A75EA7A450B9F95A9839CD5945CBCF

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.6
FileSize135984
MD5EB8F7D50A70DDFBE63BCE1D6B3B31709
SHA-1D54B4D3750A75EA7A450B9F95A9839CD5945CBCF
SHA-2560341EDE2F97140A05E2AA093565A9805E2564CF33BD16AE76FA64439EF70A118
SSDEEP1536:P5ACIHusqE0bqUv2KvB5WnqPlNJweonX9LkioEh4LwI0F/xn4xLKKW6J6eZf4:XIZqhbqUVvB5WqNunX63EMwHhhz
TLSHT133D36D1DBE1F7802C1C1B37963594A5C733F2294F35660F32046C3EC6B06EAA9BA7655
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
MD5D97323DAD70DFB690F19E52C0A3A3A3D
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
PackageRelease7.fc34
PackageVersion2.6
SHA-138C2A4DD63C49EDE6BB3DC61F2684652F81AF616
SHA-256CBCAAD4AD0B774E5278055BA9D50F0617F9E4CEB426008129C52B6337F42EDA0