Parents (Total: 47)

The searched file hash is included in 47 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	35772295A30AF340465E97DFA2BD1DA3
PackageArch	s390x
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	https://bugs.opensuse.org
PackageName	liblevmar2
PackageRelease	bp156.2.6
PackageVersion	2.6
SHA-1	00C6AFE5A7AAB8D27BD62212A48F319D3C4EE7B4
SHA-256	A85E5A45764761B0A10FCEE1A4BA052AC97E93BB1CFA00312E32ACCC447E5FC8

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

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

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	5347F1E6512760C9B9C7C20F5648B01D
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	Fedora Project
PackageName	levmar
PackageRelease	3.epel8.playground
PackageVersion	2.6
SHA-1	0DA574132C6B879C3CE78C2697970A5B4A6CD034
SHA-256	81287E07BB1A1212D99516701F5CFA026DF7FA5855A60D10567A7789131192FA

Key	Value
MD5	3A383E422AE85D8B8506FFE5C0E2069D
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	https://bugs.opensuse.org
PackageName	liblevmar2
PackageRelease	1.7
PackageVersion	2.6
SHA-1	1090DF3997BFEF4735C4D5D831045B72A08FE43B
SHA-256	4E883602424EECB0BC9F8C80712D0265D11867FF8CB0AB19BDD710D228FA8F0A

Key	Value
MD5	4FFA1212DEAD06EFDC4A6A695526277C
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	Fedora Project
PackageName	levmar
PackageRelease	7.fc34
PackageVersion	2.6
SHA-1	13F89F5D085B41A28ABBF6F1A96A08E4DFC041B3
SHA-256	1A0016DA47BD6B468FA5BEBB62FC40EB9C80F59CF0746D865A45AD4B06BA059F

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	81CE1EFF58757A9AF5CF7E3C37318C84
PackageArch	s390x
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	3.el8
PackageVersion	2.6
SHA-1	33FBCA39106B6199A7E20D215B7EEC01670B1E98
SHA-256	EBD94F0AE293B2E626FFE55DB804071CD0B6FD9534E07CE019FCCF7937F437E4

Key	Value
MD5	D97323DAD70DFB690F19E52C0A3A3A3D
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	Fedora Project
PackageName	levmar
PackageRelease	7.fc34
PackageVersion	2.6
SHA-1	38C2A4DD63C49EDE6BB3DC61F2684652F81AF616
SHA-256	CBCAAD4AD0B774E5278055BA9D50F0617F9E4CEB426008129C52B6337F42EDA0