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 95 articles , 2 standard articles )

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  1. Andreani, Roberto; Haeser, Gabriel; Viana, Daiana S.: Optimality conditions and global convergence for nonlinear semidefinite programming (2020)
  2. Barbeiro, Sílvia; Lobo, Diogo: Learning stable nonlinear cross-diffusion models for image restoration (2020)
  3. Bentbib, A. H.; El Guide, M.; Jbilou, K.: A generalized matrix Krylov subspace method for TV regularization (2020)
  4. 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)
  5. 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)
  6. Bueno, L. F.; Haeser, G.; Lara, F.; Rojas, F. N.: An augmented Lagrangian method for quasi-equilibrium problems (2020)
  7. Bueno, Luís Felipe; Haeser, Gabriel; Santos, Luiz-Rafael: Towards an efficient augmented Lagrangian method for convex quadratic programming (2020)
  8. Colombo, Tommaso; Sagratella, Simone: Distributed algorithms for convex problems with linear coupling constraints (2020)
  9. da Silva, Gustavo Assis; Beck, André Teófilo; Sigmund, Ole: Topology optimization of compliant mechanisms considering stress constraints, manufacturing uncertainty and geometric nonlinearity (2020)
  10. Emmendoerfer, Hélio jun.; Fancello, Eduardo Alberto; Silva, Emílio Carlos Nelli: Stress-constrained level set topology optimization for compliant mechanisms (2020)
  11. Fernández, Damián; Solodov, Mikhail: On the cost of solving augmented Lagrangian subproblems (2020)
  12. 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)
  13. Francisco, Juliano B.; Gonçalves, Douglas S.; Bazán, Fermín S. V.; Paredes, Lila L. T.: Non-monotone inexact restoration method for nonlinear programming (2020)
  14. Galvan, G.; Lapucci, M.; Levato, T.; Sciandrone, M.: An alternating augmented Lagrangian method for constrained nonconvex optimization (2020)
  15. Gonçalves, M. L. N.; Prudente, L. F.: On the extension of the Hager-Zhang conjugate gradient method for vector optimization (2020)
  16. 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)
  17. 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)
  18. 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)
  19. Armand, Paul; Tran, Ngoc Nguyen: An augmented Lagrangian method for equality constrained optimization with rapid infeasibility detection capabilities (2019)
  20. Birgin, E. G.; Lobato, R. D.: A matheuristic approach with nonlinear subproblems for large-scale packing of ellipsoids (2019)

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