levmar

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 37 articles )

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  1. Morini, Benedetta; Porcelli, Margherita; Toint, Philippe L.: Approximate norm descent methods for constrained nonlinear systems (2018)
  2. Cui, Yiran; del Baño Rollin, Sebastian; Germano, Guido: Full and fast calibration of the Heston stochastic volatility model (2017)
  3. Haslinger, J.; Blaheta, R.; Hrtus, R.: Identification problems with given material interfaces (2017)
  4. Andreani, R.; Júdice, J.J.; Martínez, J.M.; Martini, T.: Feasibility problems with complementarity constraints (2016)
  5. Bellavia, Stefania; Morini, Benedetta; Riccietti, Elisa: On an adaptive regularization for ill-posed nonlinear systems and its trust-region implementation (2016)
  6. Gonçalves, Douglas S.; Santos, Sandra A.: Local analysis of a spectral correction for the Gauss-Newton model applied to quadratic residual problems (2016)
  7. 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)
  8. 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)
  9. Guo, Lei; Lin, Gui-Hua; Ye, Jane J.: Solving mathematical programs with equilibrium constraints (2015)
  10. Jin, Ping; Ling, Chen; Shen, Huifei: A smoothing Levenberg-Marquardt algorithm for semi-infinite programming (2015)
  11. Tian, Boshi; Hu, Yaohua; Yang, Xiaoqi: A box-constrained differentiable penalty method for nonlinear complementarity problems (2015)
  12. Behling, R.; Fischer, A.; Herrich, M.; Iusem, A.; Ye, Y.: A Levenberg-Marquardt method with approximate projections (2014)
  13. Behling, Roger; Iusem, Alfredo: The effect of calmness on the solution set of systems of nonlinear equations (2013)
  14. Behling, Roger; Fischer, Andreas: A unified local convergence analysis of inexact constrained Levenberg-Marquardt methods (2012)
  15. Shen, Chungen; Chen, Xiongda; Liang, Yumei: A regularized Newton method for degenerate unconstrained optimization problems (2012)
  16. 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)
  17. Dellepiane, Matteo; Venturi, Andrea; Scopigno, Roberto: Image guided reconstruction of un-sampled data: A filling technique for cultural heritage models (2011) ioport
  18. Du, Shou-Qiang; Gao, Yan: The Levenberg-Marquardt-type methods for a kind of vertical complementarity problem (2011)
  19. Guo, Zhihao; Malakooti, Shahdi; Sheikh, Shaya; Al-Najjar, Camelia; Malakooti, Behnam: Multi-objective OLSR for proactive routing in MANET with delay, energy, and link lifetime predictions (2011)
  20. Du, Shou-Qiang; Gao, Yan: Convergence analysis of nonmonotone Levenberg-Marquardt algorithms for complementarity problem (2010)

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