Result for C74BD1B4E3E08228C8DF1C3A86CDD705E518F30A

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.6
FileSize127296
MD53FEE2721F028F48845109577539C8EA0
SHA-1C74BD1B4E3E08228C8DF1C3A86CDD705E518F30A
SHA-25660881F00E7F767105097580B4508451F97847FBDFC4B62FFA899BA9B11D821E4
SSDEEP1536:bbD4lY42bzHIMd7VyeaOAv/7fzkfSa1mWL1x90MvoofQbXfmFm7g6vpAm01na5ly:bglB2bBXw/0R30Mkbom3aB1
TLSHT1A2C34BC7B9628BA6D0B42F75C3AEABB5A30B262939D13D0EDB9DD73049135106F03752
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
MD581CE1EFF58757A9AF5CF7E3C37318C84
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.el8
PackageVersion2.6
SHA-133FBCA39106B6199A7E20D215B7EEC01670B1E98
SHA-256EBD94F0AE293B2E626FFE55DB804071CD0B6FD9534E07CE019FCCF7937F437E4