Parents (Total: 55)

The searched file hash is included in 55 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	6CCE01D98B0D936431A1AA42A0865D06
PackageArch	armv5tel
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	4.fc13
PackageVersion	2.5
SHA-1	0A44124FA01A5609C01F3AC009003C84489F69BC
SHA-256	990D3FD54BEEC055A25ECD08D584BC12D1B0FE4737A37D1C1A591345D3763A63

Key	Value
MD5	756DCAF2C978C7A5EE6D3C7598C55012
PackageArch	ppc64
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	8.fc19
PackageVersion	2.5
SHA-1	0C7F47A1024F457B31C09C3D8326B755D3D51490
SHA-256	723C3F11356EB119F0E7AD21D49ED76778F8E94E3FEEF84755653EEF58927460

Key	Value
MD5	EC4F7C8013E18A93625777B7594A5052
PackageArch	ppc64
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	Koji
PackageName	levmar
PackageRelease	7.fc18
PackageVersion	2.5
SHA-1	11272D8A6F4E09BE98077D128BF43C507B1D8BE9
SHA-256	28C21E430ED024143C601A0A09AD09FF2065B0F6340FFD3A2A360808E3BA1C4B

Key	Value
MD5	199CCF9038AB522C3A7F7D77763932AE
PackageArch	ppc64
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.el6
PackageVersion	2.5
SHA-1	117C2A887E82CC1D73AAE2AC51DA7058D2355F2E
SHA-256	8F6BB7F0D2BE51183C80F241816265A6570C8547F00581E8F58CD188DA21E801

Key	Value
MD5	4D99717FE273740D10651689BB3B63CD
PackageArch	ppc64le
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	12.fc23
PackageVersion	2.5
SHA-1	151B843F98A44D5FFEDFEA9BEEFFE0FD277C685D
SHA-256	AC8A47E46BE8E23D75B14C19E76A9D46F83BB65DE0C36D9F7CF40D9C5E47B1FB

Key	Value
MD5	4631EF1EC0BD572F33AB8504CF118262
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	6.el6
PackageVersion	2.5
SHA-1	1D41B99BDE01020AAF2FAA945789444EB1F3099E
SHA-256	B697D9372AF0C04B8A8C73029A16204D093F2C56AC58482F12C775C60DF615B2

Key	Value
MD5	6062C51FB9B6011511419A210E22514C
PackageArch	s390
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	12.fc23
PackageVersion	2.5
SHA-1	21ED7674874B89F6299B24696A36BF2BD755E8E5
SHA-256	49C69AFD5B4B90D4551106F85D93083D1A06432AE570656641DC540BD7F88D2F

Key	Value
MD5	2E67622A910035A39787A1F31942C3FA
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	13.fc24
PackageVersion	2.5
SHA-1	265900CBC9A993AF36E92DFF94EFB66A428402E9
SHA-256	CF2AC326903D6EC29B0B2BB8803F8AA7A6F1A82BB0ED192B1F61484126E18B4A

Key	Value
MD5	6474872018010BA971336D7FD29A0FE1
PackageArch	s390
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	8.fc19
PackageVersion	2.5
SHA-1	26B94A86D5C56204F3B13B0CB9C5EF56801E2077
SHA-256	D2985B91A482D1F2E042330EC1043E0588B55D0DB5E3D671755DCC5F95F28E78

Key	Value
MD5	89DE2AA32E8E4338B7E32569424FFC79
PackageArch	ppc
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	Koji
PackageName	levmar
PackageRelease	5.fc15
PackageVersion	2.5
SHA-1	2B0EE3AD33F23C00333B03AF007C7FD2CABB4BA9
SHA-256	95DF5038CB8D57EB2F31D7703ADEC979521DB6E85B76099E542892C1AE985F0B