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 42 articles , 1 standard article )

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  1. Adachi, Satoru; Iwata, Satoru; Nakatsukasa, Yuji; Takeda, Akiko: Solving the trust-region subproblem by a generalized eigenvalue problem (2017)
  2. Curtis, Frank E.; Gould, Nicholas I.M.; Robinson, Daniel P.; Toint, Philippe L.: An interior-point trust-funnel algorithm for nonlinear optimization (2017)
  3. 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)
  4. Forsgren, Anders; Gill, Philip E.; Wong, Elizabeth: Primal and dual active-set methods for convex quadratic programming (2016)
  5. Hager, William W.; Zhang, Hongchao: Projection onto a polyhedron that exploits sparsity (2016)
  6. Houska, Boris; Frasch, Janick; Diehl, Moritz: An augmented Lagrangian based algorithm for distributed nonconvex optimization (2016)
  7. Pestana, Jennifer; Rees, Tyrone: Null-space preconditioners for saddle point systems (2016)
  8. Bianconcini, Tommaso; Liuzzi, Giampaolo; Morini, Benedetta; Sciandrone, Marco: On the use of iterative methods in cubic regularization for unconstrained optimization (2015)
  9. Chen, Yannan; Sun, Wenyu: A dwindling filter line search method for unconstrained optimization (2015)
  10. Gill, Philip E.; Wong, Elizabeth: Methods for convex and general quadratic programming (2015)
  11. Gould, Nicholas I.M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  12. Le Thi, Hoai An; Huynh Van Ngai; Dinh, Tao Pham; Vaz, A.Ismael F.; Vicente, L.N.: Globally convergent DC trust-region methods (2014)
  13. Audet, C.; Dang, C.-K.; Orban, D.: Efficient use of parallelism in algorithmic parameter optimization applications (2013)
  14. Gould, Nicholas I.M.; Orban, Dominique; Robinson, Daniel P.: Trajectory-following methods for large-scale degenerate convex quadratic programming (2013)
  15. Oudet, Édouard: Shape optimization under width constraint (2013)
  16. Porcelli, Margherita: On the convergence of an inexact Gauss-Newton trust-region method for nonlinear least-squares problems with simple bounds (2013)
  17. Weiwei, Yang; Yueting, Yang; Chenhui, Zhang; Mingyuan, Cao: A Newton-like trust region method for large-scale unconstrained nonconvex minimization (2013)
  18. Birgin, Ernesto G.; Gentil, Jan M.: Evaluating bound-constrained minimization software (2012)
  19. De Santis, M.; Di Pillo, G.; Lucidi, S.: An active set feasible method for large-scale minimization problems with bound constraints (2012)
  20. Etman, L.F.P.; Groenwold, Albert A.; Rooda, J.E.: First-order sequential convex programming using approximate diagonal QP subproblems (2012)

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