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 29 articles , 2 standard articles )

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  1. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  2. Andreani, R.; Martínez, J.M.; Santos, L.T.: Newton’s method may fail to recognize proximity to optimal points in constrained optimization (2016)
  3. Andreani, Roberto; Martínez, José Mário; Ramos, Alberto; Silva, Paulo J.S.: A cone-continuity constraint qualification and algorithmic consequences (2016)
  4. 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)
  5. Birgin, E.G.; Bueno, L.F.; Martínez, J.M.: Sequential equality-constrained optimization for nonlinear programming (2016)
  6. 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)
  7. Birgin, E.G.; Lobato, R.D.; Martínez, J.M.: Packing ellipsoids by nonlinear optimization (2016)
  8. Birgin, E.G.; Martínez, J.M.: On the application of an augmented Lagrangian algorithm to some portfolio problems (2016)
  9. Kanzow, Christian: On the multiplier-penalty-approach for quasi-variational inequalities (2016)
  10. Kanzow, Christian; Steck, Daniel: Augmented Lagrangian methods for the solution of generalized Nash equilibrium problems (2016)
  11. Rao, Vishwas; Sandu, Adrian: A time-parallel approach to strong-constraint four-dimensional variational data assimilation (2016)
  12. Conejo, P.D.; Karas, E.W.; Pedroso, L.G.: A trust-region derivative-free algorithm for constrained optimization (2015)
  13. Izmailov, A.F.; Solodov, M.V.: Critical Lagrange multipliers: what we currently know about them, how they spoil our lives, and what we can do about it (2015)
  14. Izmailov, A.F.; Solodov, M.V.: Rejoinder on: Critical Lagrange multipliers: what we currently know about them, how they spoil our lives, and what we can do about it (2015)
  15. Izmailov, A.F.; Solodov, M.V.; Uskov, E.I.: Combining stabilized SQP with the augmented Lagrangian algorithm (2015)
  16. Birgin, Ernesto G.; Martínez, José Mario: Practical augmented Lagrangian methods for constrained optimization (2014)
  17. Fernández, D.; Pilotta, E.A.; Torres, G.A.: An inexact restoration strategy for the globalization of the sSQP method (2013)
  18. Birgin, Ernesto G.; Fernández, Damián; Martínez, J.M.: The boundedness of penalty parameters in an augmented Lagrangian method with constrained subproblems (2012)
  19. Izmailov, A.F.; Solodov, M.V.; Uskov, E.I.: Global convergence of augmented Lagrangian methods applied to optimization problems with degenerate constraints, including problems with complementarity constraints (2012)
  20. Apostolopoulou, M.S.; Sotiropoulos, D.G.; Botsaris, C.A.; Pintelas, P.: A practical method for solving large-scale TRS (2011)

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