Result for 9A87BCB294B016F1D89C2B1ACC1D7212C0B8EEC6

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.6
FileSize127304
MD5900DB75E40258893F6350979347F1BFB
SHA-19A87BCB294B016F1D89C2B1ACC1D7212C0B8EEC6
SHA-256F73CB242AB614F1A4791466C0F7A482A4B8BB2C833953E42F6E38C98E998A59E
SSDEEP1536:lbD4lY42bzHIMd7VyeaOAv/7fzkfSa1mWL1x90MvoofQbXfmFm7g6vpAm01na5ly:lglB2bBXw/0R30Mkbom3aB1
TLSHT17CC34BC7B9628BA6D0B42F75C3AEABB5A31B262939D13D0EDB9DD73049035106F03752
hashlookup:parent-total1
hashlookup:trust55

Network graph view

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
MD52E8519C74E7BE47C42E8962F99C5D0B7
PackageArchs390x
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
PackageRelease3.epel8.playground
PackageVersion2.6
SHA-15FDD25A406BFE5CA4EE81EFA36FC553F7F7B016C
SHA-2564F3D08A8D0060985982642CD3C503FE685B8687E2938061450D38F153F099244