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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Veröffentlicht in:Software, practice & experience Jg. 47; H. 11; S. 1707 - 1727
Hauptverfasser: Ramadasan, Datta, Chevaldonné, Marc, Chateau, Thierry
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 01.11.2017
Wiley
Schlagworte:
Algorithms
C++
Computer Science
Computer Vision and Pattern Recognition
Design optimization
genericity
Levenberg–Marquardt
meta‐programming
nonlinear least squares
ISSN:0038-0644, 1097-024X
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:0038-0644
1097-024X
DOI:10.1002/spe.2497