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 117 articles , 2 standard articles )

Showing results 61 to 80 of 117.
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  1. Wang, Yu; Yin, Wotao; Zeng, Jinshan: Global convergence of ADMM in nonconvex nonsmooth optimization (2019)
  2. 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)
  3. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)
  4. Birgin, E. G.; Haeser, G.; Ramos, Alberto: Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points (2018)
  5. Birgin, E. G.; Martínez, J. M.: On regularization and active-set methods with complexity for constrained optimization (2018)
  6. Dolgopolik, Maxim V.: Augmented Lagrangian functions for cone constrained optimization: the existence of global saddle points and exact penalty property (2018)
  7. Dolgopolik, M. V.: A unified approach to the global exactness of penalty and augmented Lagrangian functions. I: Parametric exactness (2018)
  8. Feng, Min; Li, Shengjie: An approximate strong KKT condition for multiobjective optimization (2018)
  9. Fukuda, Ellen H.; Lourenço, Bruno F.: Exact augmented Lagrangian functions for nonlinear semidefinite programming (2018)
  10. Haeser, Gabriel: A second-order optimality condition with first- and second-order complementarity associated with global convergence of algorithms (2018)
  11. Kanzow, Christian; Steck, Daniel: Augmented Lagrangian and exact penalty methods for quasi-variational inequalities (2018)
  12. Kanzow, Christian; Steck, Daniel: On error bounds and multiplier methods for variational problems in Banach spaces (2018)
  13. Kanzow, Christian; Steck, Daniel; Wachsmuth, Daniel: An augmented Lagrangian method for optimization problems in Banach spaces (2018)
  14. Karl, Veronika; Wachsmuth, Daniel: An augmented Lagrange method for elliptic state constrained optimal control problems (2018)
  15. Lourenço, Bruno F.; Fukuda, Ellen H.; Fukushima, Masao: Optimality conditions for problems over symmetric cones and a simple augmented Lagrangian method (2018)
  16. Lucambio Pérez, L. R.; Prudente, L. F.: Nonlinear conjugate gradient methods for vector optimization (2018)
  17. Ribeiro, Ademir A.; Sachine, Mael; Santos, Sandra A.: On the approximate solutions of augmented subproblems within sequential methods for nonlinear programming (2018)
  18. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  19. Birgin, E. G.; Krejić, N.; Martínez, J. M.: On the minimization of possibly discontinuous functions by means of pointwise approximations (2017)
  20. Birgin, E. G.; Lobato, R. D.; Martínez, J. M.: A nonlinear programming model with implicit variables for packing ellipsoids (2017)

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