ALGENCAN

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: http://plato.asu.edu)


References in zbMATH (referenced in 117 articles , 2 standard articles )

Showing results 41 to 60 of 117.
Sorted by year (citations)
  1. Armand, Paul; Tran, Ngoc Nguyen: An augmented Lagrangian method for equality constrained optimization with rapid infeasibility detection capabilities (2019)
  2. Birgin, E. G.; Lobato, R. D.: A matheuristic approach with nonlinear subproblems for large-scale packing of ellipsoids (2019)
  3. Birgin, E. G.; Martínez, J. M.: A Newton-like method with mixed factorizations and cubic regularization for unconstrained minimization (2019)
  4. Börgens, Eike; Kanzow, Christian; Steck, Daniel: Local and global analysis of multiplier methods for constrained optimization in Banach spaces (2019)
  5. Bueno, Luís Felipe; Haeser, Gabriel; Rojas, Frank Navarro: Optimality conditions and constraint qualifications for generalized Nash equilibrium problems and their practical implications (2019)
  6. da Silva, Gustavo Assis; Beck, André Teófilo; Sigmund, Ole: Stress-constrained topology optimization considering uniform manufacturing uncertainties (2019)
  7. da Silva, Gustavo Assis; Beck, André Teófilo; Sigmund, Ole: Topology optimization of compliant mechanisms with stress constraints and manufacturing error robustness (2019)
  8. Emmendoerfer, Hélio jun.; Silva, Emílio Carlos Nelli; Fancello, Eduardo Alberto: Stress-constrained level set topology optimization for design-dependent pressure load problems (2019)
  9. Galvan, Giulio; Lapucci, Matteo: On the convergence of inexact augmented Lagrangian methods for problems with convex constraints (2019)
  10. Hajinezhad, Davood; Hong, Mingyi: Perturbed proximal primal-dual algorithm for nonconvex nonsmooth optimization (2019)
  11. Kanzow, Christian; Steck, Daniel: Improved local convergence results for augmented Lagrangian methods in (C^2)-cone reducible constrained optimization (2019)
  12. Kanzow, Christian; Steck, Daniel: Quasi-variational inequalities in Banach spaces: theory and augmented Lagrangian methods (2019)
  13. Kanzow, C.; Karl, Veronika; Steck, Daniel; Wachsmuth, Daniel: The multiplier-penalty method for generalized Nash equilibrium problems in Banach spaces (2019)
  14. Kaya, C. Yalçın: Markov-Dubins interpolating curves (2019)
  15. Nasri, Mostafa; Matioli, Luiz Carlos; Rodriguez Torrealba, Elvis Manuel: Making augmented Lagrangian methods computer amenable for equilibrium problems (2019)
  16. Qiu, Songqiang: Convergence of a stabilized SQP method for equality constrained optimization (2019)
  17. Sánchez, M. D.; Schuverdt, M. L.: A second-order convergence augmented Lagrangian method using non-quadratic penalty functions (2019)
  18. Sutton, Brian D.: Numerical construction of structured matrices with given eigenvalues (2019)
  19. Van Tuyen, Nguyen; Yao, Jen-Chih; Wen, Ching-Feng: A note on approximate Karush-Kuhn-Tucker conditions in locally Lipschitz multiobjective optimization (2019)
  20. Vyskocil, Tomas; Djidjev, Hristo: Embedding equality constraints of optimization problems into a quantum annealer (2019)

Further publications can be found at: http://www.ime.usp.br/~egbirgin/tango/publications.php