Result for 1C963E2339EE2E0B015B441A0BAD632B8FC9255B

Query result

Key Value
FileName./usr/lib/liblevmar.so.2.6
FileSize130772
MD539895484E38108C60F00B0A33E2668CD
SHA-11C963E2339EE2E0B015B441A0BAD632B8FC9255B
SHA-256220136DB9A503D41671FFE5326A1B76C3D9CDEE3BDE0E4FF5FD11369FC8C982B
SSDEEP3072:kd4n+253lQ31HM2C002fhdPmIrmSdt+xtmU4WFuM6RUHIeRonuXIeSCNc+Jc:kd4+W2C002fhZTmSdqtmU4iuM6RUHxRw
TLSHT1C9D32B45F78295B0E1D300F1065F76AB222016056177F5B3FBC6AB94B87E6923E8B339
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
MD5A4A2CE2BE3DD668575CE6AD5292E937B
PackageArchi686
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-146A7A3CCCE2385169886F12F5379207BDF649B7D
SHA-256564577DCA62BDF943A518ECF4E542AA29E3290FA109B217E8B72F64F007B0AEB