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 )

Showing results 41 to 60 of 64.
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  1. Behling, Roger; Fischer, Andreas: A unified local convergence analysis of inexact constrained Levenberg-Marquardt methods (2012)
  2. Shen, Chungen; Chen, Xiongda; Liang, Yumei: A regularized Newton method for degenerate unconstrained optimization problems (2012)
  3. 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)
  4. Dellepiane, Matteo; Venturi, Andrea; Scopigno, Roberto: Image guided reconstruction of un-sampled data: A filling technique for cultural heritage models (2011) ioport
  5. Du, Shou-Qiang; Gao, Yan: The Levenberg-Marquardt-type methods for a kind of vertical complementarity problem (2011)
  6. 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)
  7. Du, Shou-Qiang; Gao, Yan: Convergence analysis of nonmonotone Levenberg-Marquardt algorithms for complementarity problem (2010)
  8. Fischer, A.; Shukla, P. K.; Wang, M.: On the inexactness level of robust Levenberg-Marquardt methods (2010)
  9. Ma, Fengming; Wang, Chuanwei: Modified projection method for solving a system of monotone equations with convex constraints (2010)
  10. Farrell, Ryan; Garcia, Roberto; Lucarelli, Dennis; Terzis, Andreas; Wang, I-Jeng: Target localization in camera wireless networks (2009) ioport
  11. Li, Yiming: A simulation-based evolutionary approach to LNA circuit design optimization (2009)
  12. Li, Ying-Jie; Li, Dong-Hui: Truncated regularized Newton method for convex minimizations (2009)
  13. Macconi, Maria; Morini, Benedetta; Porcelli, Margherita: Trust-region quadratic methods for nonlinear systems of mixed equalities and inequalities (2009)
  14. Macconi, Maria; Morini, Benedetta; Porcelli, Margherita: A Gauss-Newton method for solving bound-constrained underdetermined nonlinear systems (2009)
  15. Wang, Chuanwei; Wang, Yiju: A superlinearly convergent projection method for constrained systems of nonlinear equations (2009)
  16. Bai, F. S.; Mammadov, M.; Wu, Z. Y.; Yang, Y. J.: A filled function method for constrained nonlinear equations (2008)
  17. Du, Shou-Qiang; Gao, Yan: A modified Levenberg-Marquardt method for nonsmooth equations with finitely many maximum functions (2008)
  18. Fischer, Andreas; Shukla, Pradyumn K.: A Levenberg-Marquardt algorithm for unconstrained multicriteria optimization (2008)
  19. Zhu, Detong: Affine scaling interior Levenberg-Marquardt method for bound-constrained semismooth equations under local error bound conditions (2008)
  20. Kanzow, Christian; Petra, Stefania: Projected filter trust region methods for a semismooth least squares formulation of mixed complementarity problems (2007)