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 32 articles )

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  1. Bellavia, Stefania; Morini, Benedetta; Riccietti, Elisa: On an adaptive regularization for ill-posed nonlinear systems and its trust-region implementation (2016)
  2. 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)
  3. 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)
  4. Guo, Lei; Lin, Gui-Hua; Ye, Jane J.: Solving mathematical programs with equilibrium constraints (2015)
  5. Jin, Ping; Ling, Chen; Shen, Huifei: A smoothing Levenberg-Marquardt algorithm for semi-infinite programming (2015)
  6. Tian, Boshi; Hu, Yaohua; Yang, Xiaoqi: A box-constrained differentiable penalty method for nonlinear complementarity problems (2015)
  7. Behling, R.; Fischer, A.; Herrich, M.; Iusem, A.; Ye, Y.: A Levenberg-Marquardt method with approximate projections (2014)
  8. Behling, Roger; Iusem, Alfredo: The effect of calmness on the solution set of systems of nonlinear equations (2013)
  9. Behling, Roger; Fischer, Andreas: A unified local convergence analysis of inexact constrained Levenberg-Marquardt methods (2012)
  10. Shen, Chungen; Chen, Xiongda; Liang, Yumei: A regularized Newton method for degenerate unconstrained optimization problems (2012)
  11. 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)
  12. Dellepiane, Matteo; Venturi, Andrea; Scopigno, Roberto: Image guided reconstruction of un-sampled data: A filling technique for cultural heritage models (2011)
  13. Du, Shou-Qiang; Gao, Yan: The Levenberg-Marquardt-type methods for a kind of vertical complementarity problem (2011)
  14. 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)
  15. Du, Shou-Qiang; Gao, Yan: Convergence analysis of nonmonotone Levenberg-Marquardt algorithms for complementarity problem (2010)
  16. Fischer, A.; Shukla, P.K.; Wang, M.: On the inexactness level of robust Levenberg-Marquardt methods (2010)
  17. Ma, Fengming; Wang, Chuanwei: Modified projection method for solving a system of monotone equations with convex constraints (2010)
  18. Farrell, Ryan; Garcia, Roberto; Lucarelli, Dennis; Terzis, Andreas; Wang, I-Jeng: Target localization in camera wireless networks (2009)
  19. Li, Yiming: A simulation-based evolutionary approach to LNA circuit design optimization (2009)
  20. Li, Ying-Jie; Li, Dong-Hui: Truncated regularized Newton method for convex minimizations (2009)

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