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 45 articles , 2 standard articles )

Showing results 1 to 20 of 45.
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  1. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)
  2. Birgin, E. G.; Haeser, G.; Ramos, Alberto: Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points (2018)
  3. Birgin, E. G.; Martínez, J. M.: On regularization and active-set methods with complexity for constrained optimization (2018)
  4. Dolgopolik, Maxim V.: Augmented Lagrangian functions for cone constrained optimization: the existence of global saddle points and exact penalty property (2018)
  5. Dolgopolik, M. V.: A unified approach to the global exactness of penalty and augmented Lagrangian functions. I: Parametric exactness (2018)
  6. Haeser, Gabriel: A second-order optimality condition with first- and second-order complementarity associated with global convergence of algorithms (2018)
  7. Kanzow, Christian; Steck, Daniel: On error bounds and multiplier methods for variational problems in Banach spaces (2018)
  8. Kanzow, Christian; Steck, Daniel: Augmented Lagrangian and exact penalty methods for quasi-variational inequalities (2018)
  9. Karl, Veronika; Wachsmuth, Daniel: An augmented Lagrange method for elliptic state constrained optimal control problems (2018)
  10. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  11. Birgin, E. G.; Krejić, N.; Martínez, J. M.: On the minimization of possibly discontinuous functions by means of pointwise approximations (2017)
  12. Birgin, E. G.; Lobato, R. D.; Martínez, J. M.: A nonlinear programming model with implicit variables for packing ellipsoids (2017)
  13. Eckstein, Jonathan; Yao, Wang: Approximate ADMM algorithms derived from Lagrangian splitting (2017)
  14. Kaya, C. Yalçın: Markov-Dubins path via optimal control theory (2017)
  15. Martínez, J. M.; Raydan, M.: Cubic-regularization counterpart of a variable-norm trust-region method for unconstrained minimization (2017)
  16. Andreani, R.; Martínez, J. M.; Santos, L. T.: Newton’s method may fail to recognize proximity to optimal points in constrained optimization (2016)
  17. Andreani, Roberto; Martínez, José Mário; Ramos, Alberto; Silva, Paulo J. S.: A cone-continuity constraint qualification and algorithmic consequences (2016)
  18. Arreckx, Sylvain; Lambe, Andrew; Martins, Joaquim R. R. A.; Orban, Dominique: A matrix-free augmented Lagrangian algorithm with application to large-scale structural design optimization (2016)
  19. Birgin, E. G.; Bueno, L. F.; Martínez, J. M.: Sequential equality-constrained optimization for nonlinear programming (2016)
  20. Birgin, E. G.; Gardenghi, J. L.; Martínez, J. M.; Santos, S. A.; Toint, Ph. L.: Evaluation complexity for nonlinear constrained optimization using unscaled KKT conditions and high-order models (2016)

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