levmar : Levenberg-Marquardt nonlinear least squares algorithms in C/C++ This site provides GPL native ANSI C implementations of the Levenberg-Marquardt optimization algorithm, usable also from C++, Matlab, Perl, Python, Haskell and Tcl and explains their use. Both unconstrained and constrained (under linear equations, inequality and box constraints) Levenberg-Marquardt variants are included. The Levenberg-Marquardt (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 one, 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.

References in zbMATH (referenced in 58 articles )

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  1. Gonçalves, Douglas S.; Gonçalves, Max L. N.; Oliveira, Fabrícia R.: An inexact projected LM type algorithm for solving convex constrained nonlinear equations (2021)
  2. Abubakar, Auwal Bala; Kumam, Poom; Mohammad, Hassan: A note on the spectral gradient projection method for nonlinear monotone equations with applications (2020)
  3. Shifrin, E. I.; Lebedev, I. M.: Identification of multiple cracks in a beam by natural frequencies (2020)
  4. Abubakar, Auwal Bala; Kumam, Poom; Awwal, Aliyu Muhammed: Global convergence via descent modified three-term conjugate gradient projection algorithm with applications to signal recovery (2019)
  5. Ahookhosh, Masoud; Aragón Artacho, Francisco J.; Fleming, Ronan M. T.; Vuong, Phan T.: Local convergence of the Levenberg-Marquardt method under Hölder metric subregularity (2019)
  6. Fischer, Andreas; Izmailov, Alexey F.; Solodov, Mikhail V.: Local attractors of Newton-type methods for constrained equations and complementarity problems with nonisolated solutions (2019)
  7. Guo, Jie; Wan, Zhong: A modified spectral PRP conjugate gradient projection method for solving large-scale monotone equations and its application in compressed sensing (2019)
  8. Ma, Xiang; Bi, Linfeng: A robust adaptive iterative ensemble smoother scheme for practical history matching applications (2019)
  9. Wang, Zhu-Jun; Cai, Li; Su, Yi-Fan; Peng, Zhen: An inexact affine scaling Levenberg-Marquardt method under local error bound conditions (2019)
  10. Galli, Leonardo; Kanzow, Christian; Sciandrone, Marco: A nonmonotone trust-region method for generalized Nash equilibrium and related problems with strong convergence properties (2018)
  11. Marini, Leopoldo; Morini, Benedetta; Porcelli, Margherita: Quasi-Newton methods for constrained nonlinear systems: complexity analysis and applications (2018)
  12. Mohammad, Mutaz; Lin, En-Bing: Gibbs effects using Daubechies and Coiflet tight framelet systems (2018)
  13. Morini, Benedetta; Porcelli, Margherita; Toint, Philippe L.: Approximate norm descent methods for constrained nonlinear systems (2018)
  14. Sánta, Zsolt; Kato, Zoltan: Elastic alignment of triangular surface meshes (2018)
  15. Serra, Diana; Ruggiero, Fabio; Satici, Aykut C.; Lippiello, Vincenzo; Siciliano, Bruno: Time-optimal paths for a robotic batting task (2018)
  16. Cui, Yiran; del Baño Rollin, Sebastian; Germano, Guido: Full and fast calibration of the Heston stochastic volatility model (2017)
  17. Dang, Yazheng; Liu, Wenwen: A nonmonotone projection method for constrained system of nonlinear equations (2017)
  18. Ficcadenti, Valerio; Cerqueti, Roy: Earthquakes economic costs through rank-size laws (2017)
  19. Haslinger, J.; Blaheta, R.; Hrtus, R.: Identification problems with given material interfaces (2017)
  20. Andreani, R.; Júdice, J. J.; Martínez, J. M.; Martini, T.: Feasibility problems with complementarity constraints (2016)

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