LMA: A generic and efficient implementation of the Levenberg–Marquardt Algorithm
Summary This paper presents an open‐source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg–Marquardt algorithm (LMA). This minimization algorithm is well known and hundreds of implementations have already been released. However, none of them offer...
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| Published in | Software, practice & experience Vol. 47; no. 11; pp. 1707 - 1727 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Bognor Regis
Wiley Subscription Services, Inc
01.11.2017
Wiley |
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| ISSN | 0038-0644 1097-024X |
| DOI | 10.1002/spe.2497 |
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| Abstract | Summary
This paper presents an open‐source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg–Marquardt algorithm (LMA). This minimization algorithm is well known and hundreds of implementations have already been released. However, none of them offer at the same time a high level of genericity, a friendly syntax and a high computational performance. In this paper, we propose a solution to gather all those advantages in one library named LMA. The main challenge is to implement an efficient solver for every encounter problem. To overcome this difficulty, LMA uses compile time algorithms to design a code specific to the given optimization problem. The features of LMA are presented and the performances are compared with the state‐of‐the‐art best alternatives through extensive benchmarks on different kind of problems. Copyright © 2017 John Wiley & Sons, Ltd. |
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| AbstractList | Summary
This paper presents an open‐source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg–Marquardt algorithm (LMA). This minimization algorithm is well known and hundreds of implementations have already been released. However, none of them offer at the same time a high level of genericity, a friendly syntax and a high computational performance. In this paper, we propose a solution to gather all those advantages in one library named LMA. The main challenge is to implement an efficient solver for every encounter problem. To overcome this difficulty, LMA uses compile time algorithms to design a code specific to the given optimization problem. The features of LMA are presented and the performances are compared with the state‐of‐the‐art best alternatives through extensive benchmarks on different kind of problems. Copyright © 2017 John Wiley & Sons, Ltd. This paper presents an open‐source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg–Marquardt algorithm (LMA). This minimization algorithm is well known and hundreds of implementations have already been released. However, none of them offer at the same time a high level of genericity, a friendly syntax and a high computational performance. In this paper, we propose a solution to gather all those advantages in one library named LMA. The main challenge is to implement an efficient solver for every encounter problem. To overcome this difficulty, LMA uses compile time algorithms to design a code specific to the given optimization problem. The features of LMA are presented and the performances are compared with the state‐of‐the‐art best alternatives through extensive benchmarks on different kind of problems. Copyright © 2017 John Wiley & Sons, Ltd. Summary This paper presents an open-source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg-Marquardt algorithm (LMA). This minimization algorithm is well known and hundreds of implementations have already been released. However, none of them offer at the same time a high level of genericity, a friendly syntax and a high computational performance. In this paper, we propose a solution to gather all those advantages in one library named LMA. The main challenge is to implement an efficient solver for every encounter problem. To overcome this difficulty, LMA uses compile time algorithms to design a code specific to the given optimization problem. The features of LMA are presented and the performances are compared with the state-of-the-art best alternatives through extensive benchmarks on different kind of problems. Copyright © 2017 John Wiley & Sons, Ltd. |
| Author | Chateau, Thierry Ramadasan, Datta Chevaldonné, Marc |
| Author_xml | – sequence: 1 givenname: Datta orcidid: 0000-0002-2396-9995 surname: Ramadasan fullname: Ramadasan, Datta email: datta.ramadasan@gmail.com organization: Institut Pascal UMR 6602 CNRS/UBP/IFMA Campus des Cézeaux – sequence: 2 givenname: Marc surname: Chevaldonné fullname: Chevaldonné, Marc organization: Institut Pascal UMR 6602 CNRS/UBP/IFMA Campus des Cézeaux – sequence: 3 givenname: Thierry surname: Chateau fullname: Chateau, Thierry organization: Institut Pascal UMR 6602 CNRS/UBP/IFMA Campus des Cézeaux |
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| Cites_doi | 10.1145/242224.242477 10.1109/CVPR.2011.5995552 10.1017/S0334270000004604 10.1007/978-3-642-15552-9_4 10.1109/ICCV.2005.128 10.1137/0111030 10.1016/S0164-1212(00)00089-3 10.1080/10867651.2004.10504901 10.15607/RSS.2013.IX.042 10.1007/3-540-46020-9_38 10.1090/qam/10666 10.1109/FMPC.1995.380454 10.1007/3-540-44480-7_21 10.1137/0724076 |
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This paper presents an open‐source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg–Marquardt... This paper presents an open‐source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg–Marquardt algorithm... Summary This paper presents an open-source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg-Marquardt... |
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| SubjectTerms | Algorithms C++ Computer Science Computer Vision and Pattern Recognition Design optimization genericity Levenberg–Marquardt meta‐programming nonlinear least squares |
| Title | LMA: A generic and efficient implementation of the Levenberg–Marquardt Algorithm |
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