TANGO (Trustable Algorithms for Nonlinear General Optimization) is a set of Fortran routines for Optimization developed at the Department of Applied Mathematics of the State University of Campinas and at the Department of Computer Science of the University of São Paulo, under the coordination of Professor J. M. Martínez. Only well-established methods are included. The codes are easy to use and require minimum previous knowledge. On-line support is provided. TANGO is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation. Non-free versions of TANGO are available under terms different from those of the General Public License. Professors J. M. Martínez (martinez@ime.unicamp.br, martinezimecc@gmail.com) or E. G. Birgin (egbirgin@ime.usp.br, egbirgin@gmail.com) should be contacted for more information related to such a license, future developments and/or technical support.

References in zbMATH (referenced in 22 articles )

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  1. Kirches, Christian; Larson, Jeffrey; Leyffer, Sven; Manns, Paul: Sequential linearization method for bound-constrained mathematical programs with complementarity constraints (2022)
  2. Birgin, E. G.; Martínez, J. M.: Complexity and performance of an augmented Lagrangian algorithm (2020)
  3. Doubova, Anna; Fernández-Cara, Enrique: Some geometric inverse problems for the Lamé system with applications in elastography (2020)
  4. Fernández, Damián; Solodov, Mikhail: On the cost of solving augmented Lagrangian subproblems (2020)
  5. Leyffer, Sven; Vanaret, Charlie: An augmented Lagrangian filter method (2020)
  6. Andreani, Roberto; Secchin, Leonardo D.; Silva, Paulo J. S.: Convergence properties of a second order augmented Lagrangian method for mathematical programs with complementarity constraints (2018)
  7. Fernández-Cara, Enrique; Maestre, Faustino: An inverse problem in elastography involving Lamé systems (2018)
  8. Imani, Mahdi; Braga-Neto, Ulisses M.: Particle filters for partially-observed Boolean dynamical systems (2018)
  9. Waltoft, Berit Lindum; Hobolth, Asger: Non-parametric estimation of population size changes from the site frequency spectrum (2018)
  10. Birgin, E. G.; Bueno, L. F.; Martínez, J. M.: Sequential equality-constrained optimization for nonlinear programming (2016)
  11. Rao, Vishwas; Sandu, Adrian: A time-parallel approach to strong-constraint four-dimensional variational data assimilation (2016)
  12. Birgin, E. G.; Martínez, J. M.; Prudente, L. F.: Optimality properties of an augmented Lagrangian method on infeasible problems (2015)
  13. Doubova, Anna; Fernández-Cara, Enrique: Some geometric inverse problems for the linear wave equation (2015)
  14. 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)
  15. Birgin, E. G.; Martínez, J. M.; Prudente, L. F.: Augmented Lagrangians with possible infeasibility and finite termination for global nonlinear programming (2014)
  16. Ernesto Birgin; Jose Martínez; Marcos Raydan: Spectral Projected Gradient Methods: Review and Perspectives (2014) not zbMATH
  17. Lara, Pedro C. S.; Portugal, Renato; Lavor, Carlile: A new hybrid classical-quantum algorithm for continuous global optimization problems (2014)
  18. Martínez, Jose Mario; da Fonseca Prudente, Leandro: Handling infeasibility in a large-scale nonlinear optimization algorithm (2012)
  19. Arouxét, Ma. Belén; Echebest, Nélida; Pilotta, Elvio A.: Active-set strategy in Powell’s method for optimization without derivatives (2011)
  20. Birgin, Ernesto G.; Castelani, Emerson V.; Martinez, André L. M.; Martínez, J. M.: Outer trust-region method for constrained optimization (2011)

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