Parents (Total: 6)

The searched file hash is included in 6 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	2430BDE44BE45B43AC80B940248E3D01
PackageArch	i686
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	Fedora Project
PackageName	levmar
PackageRelease	2.fc32
PackageVersion	2.6
SHA-1	5F239633CD3D34671681A1408962F01B4B1826AE
SHA-256	67338F65AAF1888C621BC9ADAE059D75670879196D8E9221A7FADE10580C38C0

Key	Value
MD5	141D9D0C7F36FDB9F5DA5CDE7CD66C2F
PackageArch	armv7hl
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	Fedora Project
PackageName	levmar
PackageRelease	2.fc32
PackageVersion	2.6
SHA-1	D3C96CC786E3BFB862298D1CE7537359BC16CDA6
SHA-256	8860E43400B1F000B1F0D2EE2F46FBA4B391EFE2CFF9D7B5D90074976AD689AF

Key	Value
MD5	C26B0DAE8BC60D8E8AA106D69F3BD45F
PackageArch	i686
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	Fedora Project
PackageName	levmar
PackageRelease	6.fc33
PackageVersion	2.6
SHA-1	EF49112733544BB0309AB1B4A10735934B70F098
SHA-256	8E6ED717135570A22E2ABDF19CB8203713186188A254C6DB9C0666F72C053547

Key	Value
MD5	A4A2CE2BE3DD668575CE6AD5292E937B
PackageArch	i686
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	Fedora Project
PackageName	levmar
PackageRelease	7.fc34
PackageVersion	2.6
SHA-1	46A7A3CCCE2385169886F12F5379207BDF649B7D
SHA-256	564577DCA62BDF943A518ECF4E542AA29E3290FA109B217E8B72F64F007B0AEB

Key	Value
MD5	DBA9E1E2326C4BCF4E39D8559C572348
PackageArch	armv7hl
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	Fedora Project
PackageName	levmar
PackageRelease	7.fc34
PackageVersion	2.6
SHA-1	E9AE365C4EA8629F6DDBDA21C143C2D58FA9D877
SHA-256	8F7FDDA57FABAD5B73DFF0D73FA5774C309748C84C691B44554FE59AB71D785C

Key	Value
MD5	05BE1DE5F8692F11CE13E61F137EE5A2
PackageArch	armv7hl
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	Fedora Project
PackageName	levmar
PackageRelease	6.fc33
PackageVersion	2.6
SHA-1	03C32B473726C70220E95B5855336F6113292815
SHA-256	91264CB306509A3ECA0D1215702B07A4528131EA7A7949FC6A323CD3F79A6DFD