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 21 to 40 of 117.
Sorted by year (citations)
  1. Börgens, Eike; Kanzow, Christian; Mehlitz, Patrick; Wachsmuth, Gerd: New constraint qualifications for optimization problems in Banach spaces based on asymptotic KKT conditions (2020)
  2. Bueno, L. F.; Haeser, G.; Lara, F.; Rojas, F. N.: An augmented Lagrangian method for quasi-equilibrium problems (2020)
  3. Bueno, Luís Felipe; Haeser, Gabriel; Santos, Luiz-Rafael: Towards an efficient augmented Lagrangian method for convex quadratic programming (2020)
  4. Cocchi, G.; Lapucci, M.: An augmented Lagrangian algorithm for multi-objective optimization (2020)
  5. Colombo, Tommaso; Sagratella, Simone: Distributed algorithms for convex problems with linear coupling constraints (2020)
  6. Costa, Carina Moreira; Grapiglia, Geovani Nunes: A subspace version of the Wang-Yuan augmented Lagrangian-trust region method for equality constrained optimization (2020)
  7. da Silva, Gustavo Assis; Beck, André Teófilo; Sigmund, Ole: Topology optimization of compliant mechanisms considering stress constraints, manufacturing uncertainty and geometric nonlinearity (2020)
  8. Emmendoerfer, Hélio jun.; Fancello, Eduardo Alberto; Silva, Emílio Carlos Nelli: Stress-constrained level set topology optimization for compliant mechanisms (2020)
  9. Fernández, Damián; Solodov, Mikhail: On the cost of solving augmented Lagrangian subproblems (2020)
  10. Ferreira, Orizon P.; Louzeiro, Mauricio S.; Prudente, Leandro F.: Iteration-complexity and asymptotic analysis of steepest descent method for multiobjective optimization on Riemannian manifolds (2020)
  11. Francisco, Juliano B.; Gonçalves, Douglas S.; Bazán, Fermín S. V.; Paredes, Lila L. T.: Non-monotone inexact restoration method for nonlinear programming (2020)
  12. Galvan, G.; Lapucci, M.; Levato, T.; Sciandrone, M.: An alternating augmented Lagrangian method for constrained nonconvex optimization (2020)
  13. Gonçalves, M. L. N.; Prudente, L. F.: On the extension of the Hager-Zhang conjugate gradient method for vector optimization (2020)
  14. Helou, Elias S.; Santos, Sandra A.; Simões, Lucas E. A.: Analysis of a new sequential optimality condition applied to mathematical programs with equilibrium constraints (2020)
  15. Karl, Veronika; Neitzel, Ira; Wachsmuth, Daniel: A Lagrange multiplier method for semilinear elliptic state constrained optimal control problems (2020)
  16. Leyffer, Sven; Vanaret, Charlie: An augmented Lagrangian filter method (2020)
  17. Liu, Changshuo; Boumal, Nicolas: Simple algorithms for optimization on Riemannian manifolds with constraints (2020)
  18. Tifroute, Mohamed; Lahmdani, Anouar; Bouzahir, Hassane: Solving the nonsmooth bi-objective environmental and economic dispatch problem using smoothing functions (2020)
  19. Andreani, R.; Haeser, G.; Secchin, Leonardo D.; Silva, P. J. S.: New sequential optimality conditions for mathematical programs with complementarity constraints and algorithmic consequences (2019)
  20. 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)

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