Result for 2CCFD308C1A25A2C957089F3BE9E84051B95EC36

Query result

Key Value
FileName./usr/lib64/liblevmar.so.2.2
FileSize250056
MD5E0787E474B94177D2ECFDDAEC56AD9F1
SHA-12CCFD308C1A25A2C957089F3BE9E84051B95EC36
SHA-256C109E58054DD2796E3E1310342D8877321082CDD957A4B0C647E4D28A4C96720
SSDEEP6144:9XvWfgXIpIEzxPLMKvGppo3p+3UKblA99UcTfx4y2:9/WfgX43zxPLMKvepo3oBbl+9hfx4y2
TLSHT16D343C47704228FCD1D5B571A2FAB12B7232301A5B1E6DE213D24B702F29D152F96B6F
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
MD5261F11224439968E930D7FFC6488F787
PackageArchx86_64
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
PackageRelease3.mga8
PackageVersion2.6
SHA-1A497D602A5059374C4C1C9CA607DF9B34F7D0B6D
SHA-256408E2E30A30F3F9632903D6C05DA521F9C5C4535E70EA07E56308981CCDF3551