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 64 articles )

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  1. Serra, Diana; Ruggiero, Fabio; Satici, Aykut C.; Lippiello, Vincenzo; Siciliano, Bruno: Time-optimal paths for a robotic batting task (2018)
  2. Cui, Yiran; del Baño Rollin, Sebastian; Germano, Guido: Full and fast calibration of the Heston stochastic volatility model (2017)
  3. Dang, Yazheng; Liu, Wenwen: A nonmonotone projection method for constrained system of nonlinear equations (2017)
  4. Ficcadenti, Valerio; Cerqueti, Roy: Earthquakes economic costs through rank-size laws (2017)
  5. Haslinger, J.; Blaheta, R.; Hrtus, R.: Identification problems with given material interfaces (2017)
  6. Andreani, R.; Júdice, J. J.; Martínez, J. M.; Martini, T.: Feasibility problems with complementarity constraints (2016)
  7. Bellavia, Stefania; Morini, Benedetta; Riccietti, Elisa: On an adaptive regularization for ill-posed nonlinear systems and its trust-region implementation (2016)
  8. Gonçalves, Douglas S.; Santos, Sandra A.: Local analysis of a spectral correction for the Gauss-Newton model applied to quadratic residual problems (2016)
  9. Wang, Peng; Zhu, Detong: An inexact derivative-free Levenberg-Marquardt method for linear inequality constrained nonlinear systems under local error bound conditions (2016)
  10. Wang, X. Y.; Li, S. J.; Kou, Xi Peng: A self-adaptive three-term conjugate gradient method for monotone nonlinear equations with convex constraints (2016)
  11. Ziggah, Yao Yevenyo; Youjian, Hu; Yu, Xianyu; Basommi, Laari Prosper: Capability of artificial neural network for forward conversion of geodetic coordinates ((\phi,\lambda,h)) to Cartesian coordinates ((X,Y,Z)) (2016)
  12. Croft, Wayne; Elliott, Charles M.; Ladds, Graham; Stinner, Björn; Venkataraman, Chandrasekhar; Weston, Cathryn: Parameter identification problems in the modelling of cell motility (2015)
  13. Fischer, Andreas: Comments on: Critical Lagrange multipliers: what we currently know about them, how they spoil our lives, and what we can do about it (2015)
  14. Guo, Lei; Lin, Gui-Hua; Ye, Jane J.: Solving mathematical programs with equilibrium constraints (2015)
  15. Jin, Ping; Ling, Chen; Shen, Huifei: A smoothing Levenberg-Marquardt algorithm for semi-infinite programming (2015)
  16. Liu, J. K.; Li, S. J.: A projection method for convex constrained monotone nonlinear equations with applications (2015)
  17. Tian, Boshi; Hu, Yaohua; Yang, Xiaoqi: A box-constrained differentiable penalty method for nonlinear complementarity problems (2015)
  18. Behling, R.; Fischer, A.; Herrich, M.; Iusem, A.; Ye, Y.: A Levenberg-Marquardt method with approximate projections (2014)
  19. Behling, Roger; Iusem, Alfredo: The effect of calmness on the solution set of systems of nonlinear equations (2013)
  20. Civicioglu, Pinar: Backtracking search optimization algorithm for numerical optimization problems (2013)