ALGENCAN. Fortran code for general nonlinear programming that does not use matrix manipulations at all and, so, is able to solve extremely large problems with moderate computer time. The general algorithm is of Augmented Lagrangian type and the subproblems are solved using GENCAN. GENCAN (included in ALGENCAN) is a Fortran code for minimizing a smooth function with a potentially large number of variables and box-constraints. (Source:

References in zbMATH (referenced in 57 articles , 2 standard articles )

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  1. Andreani, Roberto; Fazzio, Nadia S.; Schuverdt, Maria L.; Secchin, Leonardo D.: A sequential optimality condition related to the quasi-normality constraint qualification and its algorithmic consequences (2019)
  2. Birgin, E. G.; Lobato, R. D.: A matheuristic approach with nonlinear subproblems for large-scale packing of ellipsoids (2019)
  3. Bueno, Luís Felipe; Haeser, Gabriel; Rojas, Frank Navarro: Optimality conditions and constraint qualifications for generalized Nash equilibrium problems and their practical implications (2019)
  4. Kanzow, C.; Karl, Veronika; Steck, Daniel; Wachsmuth, Daniel: The multiplier-penalty method for generalized Nash equilibrium problems in Banach spaces (2019)
  5. Van Tuyen, Nguyen; Yao, Jen-Chih; Wen, Ching-Feng: A note on approximate Karush-Kuhn-Tucker conditions in locally Lipschitz multiobjective optimization (2019)
  6. Wang, Yu; Yin, Wotao; Zeng, Jinshan: Global convergence of ADMM in nonconvex nonsmooth optimization (2019)
  7. Andreani, Roberto; Secchin, Leonardo D.; Silva, Paulo J. S.: Convergence properties of a second order augmented Lagrangian method for mathematical programs with complementarity constraints (2018)
  8. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)
  9. Birgin, E. G.; Haeser, G.; Ramos, Alberto: Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points (2018)
  10. Birgin, E. G.; Martínez, J. M.: On regularization and active-set methods with complexity for constrained optimization (2018)
  11. Dolgopolik, Maxim V.: Augmented Lagrangian functions for cone constrained optimization: the existence of global saddle points and exact penalty property (2018)
  12. Dolgopolik, M. V.: A unified approach to the global exactness of penalty and augmented Lagrangian functions. I: Parametric exactness (2018)
  13. Feng, Min; Li, Shengjie: An approximate strong KKT condition for multiobjective optimization (2018)
  14. Fukuda, Ellen H.; Lourenço, Bruno F.: Exact augmented Lagrangian functions for nonlinear semidefinite programming (2018)
  15. Haeser, Gabriel: A second-order optimality condition with first- and second-order complementarity associated with global convergence of algorithms (2018)
  16. Kanzow, Christian; Steck, Daniel: On error bounds and multiplier methods for variational problems in Banach spaces (2018)
  17. Kanzow, Christian; Steck, Daniel: Augmented Lagrangian and exact penalty methods for quasi-variational inequalities (2018)
  18. Karl, Veronika; Wachsmuth, Daniel: An augmented Lagrange method for elliptic state constrained optimal control problems (2018)
  19. Lucambio Pérez, L. R.; Prudente, L. F.: Nonlinear conjugate gradient methods for vector optimization (2018)
  20. Ribeiro, Ademir A.; Sachine, Mael; Santos, Sandra A.: On the approximate solutions of augmented subproblems within sequential methods for nonlinear programming (2018)

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