Key | Value |
---|---|
FileName | ./usr/lib/.build-id/11/a58f4961d7391f9dfada19b784d176376f1969 |
FileSize | 38 |
MD5 | 43AEF9EA6F80C32411633F801740C06D |
SHA-1 | 7336AC9022869227D40388C5FB6E8C87E206CC40 |
SHA-256 | 15BB9FEA52A1084437C9F4E218577E6122BF75600AD4117EDC7A25A9030436D9 |
SSDEEP | 3:gCD/C:X/C |
TLSH | |
hashlookup:parent-total | 12 |
hashlookup:trust | 100 |
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 |
---|---|
MD5 | 23C7CEA8AFFFD7CF8F0F6F8987E7C30F |
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 | 6.fc33 |
PackageVersion | 2.6 |
SHA-1 | FF1EB3AFBF29BA7F7174167B07D8889F7FEFECC1 |
SHA-256 | CADF0CAA21A9C7FC78E20709A46FB707EBEF6BB0B4FBABE3EA0399FF2F2BC14C |
Key | Value |
---|---|
MD5 | 5FCED4467A4061B22D44CC679BE72614 |
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.fc33 |
PackageVersion | 2.6 |
SHA-1 | DFFF4322CBD0243471C0776527BFA3A94BBE6586 |
SHA-256 | 4CF6267B785F15FD4D858F857FF08BC0F097B615E77207A349A1C23AA66DFA46 |
Key | Value |
---|---|
MD5 | 2E8519C74E7BE47C42E8962F99C5D0B7 |
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.epel8.playground |
PackageVersion | 2.6 |
SHA-1 | 5FDD25A406BFE5CA4EE81EFA36FC553F7F7B016C |
SHA-256 | 4F3D08A8D0060985982642CD3C503FE685B8687E2938061450D38F153F099244 |
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 | 1917971E3D05AE04362A9EA3336CBF66 |
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 | 3.el8 |
PackageVersion | 2.6 |
SHA-1 | 3F82688B1B08CC2C31421812DA58666A9871566C |
SHA-256 | 4068553AF9A761BF912049408298CD2BA845292D9A3A1A3F727A22F40C16138C |
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 | 536B647C8AD64C48B8D1FC0CDC989EB4 |
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 | 2.fc32 |
PackageVersion | 2.6 |
SHA-1 | FA12421AE62573AC2931C1697C09C6AA20DDC2CE |
SHA-256 | 352284E32FFA62C8D0181E175ACFDF8481772276725451FE28C505EB9CAA9A38 |
Key | Value |
---|---|
MD5 | 2D15257FBC356052C44599062442F852 |
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 | 3.el8 |
PackageVersion | 2.6 |
SHA-1 | 7F674ED65E63F7584F1BDA64C9386F6322B93BD7 |
SHA-256 | 55F29B81C321387A2F8855A438F2EFF84D39EFF75430E6CAFEF0E8F834775D9E |
Key | Value |
---|---|
MD5 | 6A0B2A54CCF5EDD4FC4D32CE1E300AF1 |
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.el8 |
PackageVersion | 2.6 |
SHA-1 | B03515D6735EDE3545CE811BBCE412E2BFF87C9C |
SHA-256 | E6C2D0BE9A7730FD16611FB15F4F4EF7C3354AE35D4A50B44BD4003F988E9E08 |
Key | Value |
---|---|
MD5 | 1AC7399E6D3B1C54425D9FA3427C1E07 |
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 | 2.fc32 |
PackageVersion | 2.6 |
SHA-1 | 48F0DA86586705020138224CED7F532EF6A3F644 |
SHA-256 | 95FFB8DCE30D0086A7178058CC7A2BD0E1DDC75FED7D6D3871264D3F6FE23807 |
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 |