Key | Value |
---|---|
FileName | ./usr/lib/liblevmar.so.2.2 |
FileSize | 220460 |
MD5 | 204FE27E16C940B1BBD0A4B0279E6828 |
SHA-1 | A2F7A6E8362E50363F0F973555CA31F499BC1608 |
SHA-256 | 25CE7A10C3A7AED84DCBFA35B1EB9F171672341AA0DB57E158911280C9BB0902 |
SSDEEP | 6144:+YZzkO5UIrWnHL7gzyVnnnnnnJOQDkELYP5v49rfSKujWO:5ZQLe+HL72yVnnnnnnJlJfSKujWO |
TLSH | T17F245B86E7C546A9D0935CB28077AB36FA345F435037F2F0EBCAA711A830B5B7D25258 |
hashlookup:parent-total | 1 |
hashlookup:trust | 55 |
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 |
---|---|
MD5 | 5B11208273593FE44DCA885EF69C5735 |
PackageArch | i586 |
PackageDescription | levmar 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. |
PackageMaintainer | umeabot <umeabot> |
PackageName | liblevmar2 |
PackageRelease | 4.mga9 |
PackageVersion | 2.6 |
SHA-1 | D1B07BBA267344DCD687C85C0E883561AAD0626D |
SHA-256 | 8880FFFC75BB99C65D0A4A7CCBF451875127A55285B00F10F8757A1B0D1ED814 |