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	E940A63C7329BD0565578AF6DE5DDAAC
PackageArch	aarch64
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	lib64levmar2
PackageRelease	2.mga7
PackageVersion	2.6
SHA-1	086AC87C37AD753A705CDE221A87AF21C31C78DE
SHA-256	785A451B9681AE2A46FF9DBDD1D9E5D492CA6403C18432589684A1D63C6B3491

Key	Value
MD5	261F11224439968E930D7FFC6488F787
PackageArch	x86_64
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	lib64levmar2
PackageRelease	3.mga8
PackageVersion	2.6
SHA-1	A497D602A5059374C4C1C9CA607DF9B34F7D0B6D
SHA-256	408E2E30A30F3F9632903D6C05DA521F9C5C4535E70EA07E56308981CCDF3551

Key	Value
MD5	2E743EE93FACC2C508487F233077BA47
PackageArch	x86_64
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	lib64levmar2
PackageRelease	4.mga9
PackageVersion	2.6
SHA-1	57AA08C62BB13ACAA3388371E8D3BB25ECBC6433
SHA-256	1F32AE07FD19974DEEE34508676492D718D618E8998005C237A81958B688DD37

Key	Value
MD5	C63A57F66D8012F6585F4D6D28F2E753
PackageArch	x86_64
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	lib64levmar2
PackageRelease	2.mga7
PackageVersion	2.6
SHA-1	ADE0C6F0F0D8CD050CA03BFDCBDBB4A4D2BF7F16
SHA-256	1246F8F2DAE7E216186A4C511EEB4B9D7D02B9F4D852F1BB9C4CF114662AF298

Key	Value
MD5	FF5FC1FC185619BA42D2380B830E0F04
PackageArch	aarch64
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	lib64levmar2
PackageRelease	3.mga8
PackageVersion	2.6
SHA-1	941BD2ECCBAED0ACE4FBC99B24E3919C00609CF2
SHA-256	7C38653C140A64DFF41E1DD38AB209E5CC891152456067375F9B76505A3B3113

Key	Value
MD5	1CAA8B93D06593ABD3AA18182E077640
PackageArch	aarch64
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	lib64levmar2
PackageRelease	4.mga9
PackageVersion	2.6
SHA-1	CBB655B2E8293B78EF16001D990DE83A29355D71
SHA-256	65AB30DB4209AC1F59ABB9D01C622D638BC2E5F5CBF9D9B0E9D728A5379704FE