Result for 7336AC9022869227D40388C5FB6E8C87E206CC40

Query result

Key Value
FileName./usr/lib/.build-id/11/a58f4961d7391f9dfada19b784d176376f1969
FileSize38
MD543AEF9EA6F80C32411633F801740C06D
SHA-17336AC9022869227D40388C5FB6E8C87E206CC40
SHA-25615BB9FEA52A1084437C9F4E218577E6122BF75600AD4117EDC7A25A9030436D9
SSDEEP3:gCD/C:X/C
TLSH
hashlookup:parent-total12
hashlookup:trust100

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Parents (Total: 12)

The searched file hash is included in 12 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD523C7CEA8AFFFD7CF8F0F6F8987E7C30F
PackageArchaarch64
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease6.fc33
PackageVersion2.6
SHA-1FF1EB3AFBF29BA7F7174167B07D8889F7FEFECC1
SHA-256CADF0CAA21A9C7FC78E20709A46FB707EBEF6BB0B4FBABE3EA0399FF2F2BC14C
Key Value
MD55FCED4467A4061B22D44CC679BE72614
PackageArchx86_64
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease6.fc33
PackageVersion2.6
SHA-1DFFF4322CBD0243471C0776527BFA3A94BBE6586
SHA-2564CF6267B785F15FD4D858F857FF08BC0F097B615E77207A349A1C23AA66DFA46
Key Value
MD52E8519C74E7BE47C42E8962F99C5D0B7
PackageArchs390x
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease3.epel8.playground
PackageVersion2.6
SHA-15FDD25A406BFE5CA4EE81EFA36FC553F7F7B016C
SHA-2564F3D08A8D0060985982642CD3C503FE685B8687E2938061450D38F153F099244
Key Value
MD54FFA1212DEAD06EFDC4A6A695526277C
PackageArchx86_64
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease7.fc34
PackageVersion2.6
SHA-113F89F5D085B41A28ABBF6F1A96A08E4DFC041B3
SHA-2561A0016DA47BD6B468FA5BEBB62FC40EB9C80F59CF0746D865A45AD4B06BA059F
Key Value
MD51917971E3D05AE04362A9EA3336CBF66
PackageArchaarch64
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease3.el8
PackageVersion2.6
SHA-13F82688B1B08CC2C31421812DA58666A9871566C
SHA-2564068553AF9A761BF912049408298CD2BA845292D9A3A1A3F727A22F40C16138C
Key Value
MD55347F1E6512760C9B9C7C20F5648B01D
PackageArchx86_64
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease3.epel8.playground
PackageVersion2.6
SHA-10DA574132C6B879C3CE78C2697970A5B4A6CD034
SHA-25681287E07BB1A1212D99516701F5CFA026DF7FA5855A60D10567A7789131192FA
Key Value
MD5536B647C8AD64C48B8D1FC0CDC989EB4
PackageArchaarch64
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease2.fc32
PackageVersion2.6
SHA-1FA12421AE62573AC2931C1697C09C6AA20DDC2CE
SHA-256352284E32FFA62C8D0181E175ACFDF8481772276725451FE28C505EB9CAA9A38
Key Value
MD52D15257FBC356052C44599062442F852
PackageArchppc64le
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease3.el8
PackageVersion2.6
SHA-17F674ED65E63F7584F1BDA64C9386F6322B93BD7
SHA-25655F29B81C321387A2F8855A438F2EFF84D39EFF75430E6CAFEF0E8F834775D9E
Key Value
MD56A0B2A54CCF5EDD4FC4D32CE1E300AF1
PackageArchx86_64
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease3.el8
PackageVersion2.6
SHA-1B03515D6735EDE3545CE811BBCE412E2BFF87C9C
SHA-256E6C2D0BE9A7730FD16611FB15F4F4EF7C3354AE35D4A50B44BD4003F988E9E08
Key Value
MD51AC7399E6D3B1C54425D9FA3427C1E07
PackageArchx86_64
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease2.fc32
PackageVersion2.6
SHA-148F0DA86586705020138224CED7F532EF6A3F644
SHA-25695FFB8DCE30D0086A7178058CC7A2BD0E1DDC75FED7D6D3871264D3F6FE23807
Key Value
MD581CE1EFF58757A9AF5CF7E3C37318C84
PackageArchs390x
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease3.el8
PackageVersion2.6
SHA-133FBCA39106B6199A7E20D215B7EEC01670B1E98
SHA-256EBD94F0AE293B2E626FFE55DB804071CD0B6FD9534E07CE019FCCF7937F437E4
Key Value
MD5D97323DAD70DFB690F19E52C0A3A3A3D
PackageArchaarch64
PackageDescriptionlevmar 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.
PackageMaintainerFedora Project
PackageNamelevmar
PackageRelease7.fc34
PackageVersion2.6
SHA-138C2A4DD63C49EDE6BB3DC61F2684652F81AF616
SHA-256CBCAAD4AD0B774E5278055BA9D50F0617F9E4CEB426008129C52B6337F42EDA0