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 108 articles , 2 standard articles )

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  1. Assunção, P. B.; Ferreira, O. P.; Prudente, L. F.: Conditional gradient method for multiobjective optimization (2021)
  2. Birgin, E. G.; Laurain, A.; Massambone, R.; Santana, A. G.: A shape optimization approach to the problem of covering a two-dimensional region with minimum-radius identical balls (2021)
  3. Birgin, E. G.; Martínez, J. M.; Ramos, A.: On constrained optimization with nonconvex regularization (2021)
  4. Dostál, Zdeněk; Vlach, Oldřich: An accelerated augmented Lagrangian algorithm with adaptive orthogonalization strategy for bound and equality constrained quadratic programming and its application to large-scale contact problems of elasticity (2021)
  5. Hermans, Ben; Pipeleers, Goele; Patrinos, Panagiotis (Panos): A penalty method for nonlinear programs with set exclusion constraints (2021)
  6. Ramos, Alberto: Mathematical programs with equilibrium constraints: a sequential optimality condition, new constraint qualifications and algorithmic consequences (2021)
  7. Andreani, Roberto; Haeser, Gabriel; Viana, Daiana S.: Optimality conditions and global convergence for nonlinear semidefinite programming (2020)
  8. Barbeiro, Sílvia; Lobo, Diogo: Learning stable nonlinear cross-diffusion models for image restoration (2020)
  9. Bentbib, A. H.; El Guide, M.; Jbilou, K.: A generalized matrix Krylov subspace method for TV regularization (2020)
  10. Birgin, E. G.; Gardenghi, J. L.; Martínez, J. M.; Santos, S. A.: On the use of third-order models with fourth-order regularization for unconstrained optimization (2020)
  11. Birgin, E. G.; Martínez, J. M.: Complexity and performance of an augmented Lagrangian algorithm (2020)
  12. Birgin, Ernesto G.; Gómez, Walter; Haeser, Gabriel; Mito, Leonardo M.; Santos, Daiana O.: An augmented Lagrangian algorithm for nonlinear semidefinite programming applied to the covering problem (2020)
  13. Börgens, Eike; Kanzow, Christian; Mehlitz, Patrick; Wachsmuth, Gerd: New constraint qualifications for optimization problems in Banach spaces based on asymptotic KKT conditions (2020)
  14. Bueno, L. F.; Haeser, G.; Lara, F.; Rojas, F. N.: An augmented Lagrangian method for quasi-equilibrium problems (2020)
  15. Bueno, Luís Felipe; Haeser, Gabriel; Santos, Luiz-Rafael: Towards an efficient augmented Lagrangian method for convex quadratic programming (2020)
  16. Cocchi, G.; Lapucci, M.: An augmented Lagrangian algorithm for multi-objective optimization (2020)
  17. Colombo, Tommaso; Sagratella, Simone: Distributed algorithms for convex problems with linear coupling constraints (2020)
  18. Costa, Carina Moreira; Grapiglia, Geovani Nunes: A subspace version of the Wang-Yuan augmented Lagrangian-trust region method for equality constrained optimization (2020)
  19. da Silva, Gustavo Assis; Beck, André Teófilo; Sigmund, Ole: Topology optimization of compliant mechanisms considering stress constraints, manufacturing uncertainty and geometric nonlinearity (2020)
  20. Emmendoerfer, Hélio jun.; Fancello, Eduardo Alberto; Silva, Emílio Carlos Nelli: Stress-constrained level set topology optimization for compliant mechanisms (2020)

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