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	4DF1137B2DE810F131FBE45C732A958F
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	umeabot <umeabot>
PackageName	liblevmar2
PackageRelease	3.mga8
PackageVersion	2.6
SHA-1	2C516AD80F00064180E1FB0280404DCCC1873D00
SHA-256	26AAB990F14537FB4CA2F2D0AEDAD10F4517FBF002960D2B29D7EF8BA3172ADB

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

Key	Value
MD5	9858C1342DFD50547A85B1EEEE868CD3
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	umeabot <umeabot>
PackageName	liblevmar2
PackageRelease	2.mga7
PackageVersion	2.6
SHA-1	544B8C9F54ACFCC9E34770DC90C74B5B48F7A1F1
SHA-256	964F99F69FBC9E5FB3659E768AEB5F56F1D34ADD7A7CAF4BD79D0535E54DBF24

Key	Value
MD5	AA46846E9C7C7C20BB5758E34346AB95
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	2.mga7
PackageVersion	2.6
SHA-1	7E2F2C667B9FA5EB68DDE492DCB98379B3C38130
SHA-256	F06120634A1754160E14C804819A5C73C91FC1FE50D4ED2DD325FE478794A0C7

Key	Value
MD5	CBBF780B66734536FA1F1ACF845D0328
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	umeabot <umeabot>
PackageName	liblevmar2
PackageRelease	4.mga9
PackageVersion	2.6
SHA-1	72079B1FDF4F76DDC432233452346B38D7EFA714
SHA-256	0219C67AF97C6D58DB11A51D08C3BBFCEC988108712C9F26EAAE4CC9931A083B

Key	Value
MD5	91956BF3EECE6A35C587E3CFEA8B04D6
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	3.mga8
PackageVersion	2.6
SHA-1	010905BB070C2FD141F9A8A81DEE5E2CA1832946
SHA-256	2499251F157218E033EB685EFF3723E768938C2C248869FE4A8CA395A139E3E4