Result for A2F7A6E8362E50363F0F973555CA31F499BC1608

Query result

Key Value
FileName./usr/lib/liblevmar.so.2.2
FileSize220460
MD5204FE27E16C940B1BBD0A4B0279E6828
SHA-1A2F7A6E8362E50363F0F973555CA31F499BC1608
SHA-25625CE7A10C3A7AED84DCBFA35B1EB9F171672341AA0DB57E158911280C9BB0902
SSDEEP6144:+YZzkO5UIrWnHL7gzyVnnnnnnJOQDkELYP5v49rfSKujWO:5ZQLe+HL72yVnnnnnnJlJfSKujWO
TLSHT17F245B86E7C546A9D0935CB28077AB36FA345F435037F2F0EBCAA711A830B5B7D25258
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
MD55B11208273593FE44DCA885EF69C5735
PackageArchi586
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>
PackageNameliblevmar2
PackageRelease4.mga9
PackageVersion2.6
SHA-1D1B07BBA267344DCD687C85C0E883561AAD0626D
SHA-2568880FFFC75BB99C65D0A4A7CCBF451875127A55285B00F10F8757A1B0D1ED814