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 41 articles )

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  1. Galli, Leonardo; Kanzow, Christian; Sciandrone, Marco: A nonmonotone trust-region method for generalized Nash equilibrium and related problems with strong convergence properties (2018)
  2. Marini, Leopoldo; Morini, Benedetta; Porcelli, Margherita: Quasi-Newton methods for constrained nonlinear systems: complexity analysis and applications (2018)
  3. Mohammad, Mutaz; Lin, En-Bing: Gibbs effects using Daubechies and Coiflet tight framelet systems (2018)
  4. Morini, Benedetta; Porcelli, Margherita; Toint, Philippe L.: Approximate norm descent methods for constrained nonlinear systems (2018)
  5. Cui, Yiran; del Baño Rollin, Sebastian; Germano, Guido: Full and fast calibration of the Heston stochastic volatility model (2017)
  6. Haslinger, J.; Blaheta, R.; Hrtus, R.: Identification problems with given material interfaces (2017)
  7. Andreani, R.; Júdice, J. J.; Martínez, J. M.; Martini, T.: Feasibility problems with complementarity constraints (2016)
  8. Bellavia, Stefania; Morini, Benedetta; Riccietti, Elisa: On an adaptive regularization for ill-posed nonlinear systems and its trust-region implementation (2016)
  9. Gonçalves, Douglas S.; Santos, Sandra A.: Local analysis of a spectral correction for the Gauss-Newton model applied to quadratic residual problems (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. 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)
  13. Guo, Lei; Lin, Gui-Hua; Ye, Jane J.: Solving mathematical programs with equilibrium constraints (2015)
  14. Jin, Ping; Ling, Chen; Shen, Huifei: A smoothing Levenberg-Marquardt algorithm for semi-infinite programming (2015)
  15. Tian, Boshi; Hu, Yaohua; Yang, Xiaoqi: A box-constrained differentiable penalty method for nonlinear complementarity problems (2015)
  16. Behling, R.; Fischer, A.; Herrich, M.; Iusem, A.; Ye, Y.: A Levenberg-Marquardt method with approximate projections (2014)
  17. Behling, Roger; Iusem, Alfredo: The effect of calmness on the solution set of systems of nonlinear equations (2013)
  18. Behling, Roger; Fischer, Andreas: A unified local convergence analysis of inexact constrained Levenberg-Marquardt methods (2012)
  19. Shen, Chungen; Chen, Xiongda; Liang, Yumei: A regularized Newton method for degenerate unconstrained optimization problems (2012)
  20. Ueda, Kenji; Yamashita, Nobuo: Global complexity bound analysis of the Levenberg-Marquardt method for nonsmooth equations and its application to the nonlinear complementarity problem (2012)

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