GALAHAD

We describe the design of version 1.0 of GALAHAD, a library of Fortran 90 packages for largescale nonlinear optimization. The library particularly addresses quadratic programming problems, containing both interior point and active set algorithms, as well as tools for preprocessing problems prior to solution. It also contains an updated version of the venerable nonlinear programming package, LANCELOT.


References in zbMATH (referenced in 48 articles , 1 standard article )

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  1. Amaioua, Nadir; Audet, Charles; Conn, Andrew R.; Le Digabel, Sébastien: Efficient solution of quadratically constrained quadratic subproblems within the mesh adaptive direct search algorithm (2018)
  2. Lenders, Felix; Kirches, C.; Potschka, A.: trlib: a vector-free implementation of the GLTR method for iterative solution of the trust region problem (2018)
  3. Adachi, Satoru; Iwata, Satoru; Nakatsukasa, Yuji; Takeda, Akiko: Solving the trust-region subproblem by a generalized eigenvalue problem (2017)
  4. Cristofari, Andrea; De Santis, Marianna; Lucidi, Stefano; Rinaldi, Francesco: A two-stage active-set algorithm for bound-constrained optimization (2017)
  5. Curtis, Frank E.; Gould, Nicholas I. M.; Robinson, Daniel P.; Toint, Philippe L.: An interior-point trust-funnel algorithm for nonlinear optimization (2017)
  6. Francisco, J. B.; Viloche Bazán, F. S.; Weber Mendonça, M.: Non-monotone algorithm for minimization on arbitrary domains with applications to large-scale orthogonal Procrustes problem (2017)
  7. Gould, Nicholas I. M.; Robinson, Daniel P.: A dual gradient-projection method for large-scale strictly convex quadratic problems (2017)
  8. Gould, Nicholas; Scott, Jennifer: The state-of-the-art of preconditioners for sparse linear least-squares problems (2017)
  9. Zhang, Lei-Hong; Shen, Chungen; Li, Ren-Cang: On the generalized Lanczos trust-region method (2017)
  10. Forsgren, Anders; Gill, Philip E.; Wong, Elizabeth: Primal and dual active-set methods for convex quadratic programming (2016)
  11. Hager, William W.; Zhang, Hongchao: Projection onto a polyhedron that exploits sparsity (2016)
  12. Houska, Boris; Frasch, Janick; Diehl, Moritz: An augmented Lagrangian based algorithm for distributed nonconvex optimization (2016)
  13. Pestana, Jennifer; Rees, Tyrone: Null-space preconditioners for saddle point systems (2016)
  14. Bianconcini, Tommaso; Liuzzi, Giampaolo; Morini, Benedetta; Sciandrone, Marco: On the use of iterative methods in cubic regularization for unconstrained optimization (2015)
  15. Chen, Yannan; Sun, Wenyu: A dwindling filter line search method for unconstrained optimization (2015)
  16. Gill, Philip E.; Wong, Elizabeth: Methods for convex and general quadratic programming (2015)
  17. Gould, Nicholas I. M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  18. Le Thi, Hoai An; Huynh Van Ngai; Dinh, Tao Pham; Vaz, A. Ismael F.; Vicente, L. N.: Globally convergent DC trust-region methods (2014)
  19. Audet, C.; Dang, C.-K.; Orban, D.: Efficient use of parallelism in algorithmic parameter optimization applications (2013)
  20. Gould, Nicholas I. M.; Orban, Dominique; Robinson, Daniel P.: Trajectory-following methods for large-scale degenerate convex quadratic programming (2013)

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