LANCELOT

LANCELOT. A Fortran package for large-scale nonlinear optimization (Release A). LANCELOT is a software package for solving large-scale nonlinear optimization problems. This book provides a coherent overview of the package and its use. In particular, it contains a proposal for a standard input for problems and the LANCELOT optimization package. Although the book is primarily concerned with a specific optimization package, the issues discussed have much wider implications for the design and implementation of large-scale optimization algorithms.


References in zbMATH (referenced in 269 articles , 3 standard articles )

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  1. Armand, Paul; Tran, Ngoc Nguyen: An augmented Lagrangian method for equality constrained optimization with rapid infeasibility detection capabilities (2019)
  2. Chen, Xiaojun; Toint, Ph. L.; Wang, H.: Complexity of partially separable convexly constrained optimization with non-Lipschitzian singularities (2019)
  3. Englert, Tobias; Völz, Andreas; Mesmer, Felix; Rhein, Sönke; Graichen, Knut: A software framework for embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC) (2019)
  4. Paternain, Santiago; Mokhtari, Aryan; Ribeiro, Alejandro: A Newton-based method for nonconvex optimization with fast evasion of saddle points (2019)
  5. Ri, Jun-Hyok; Hong, Hyon-Sik: A basis reduction method using proper orthogonal decomposition for shakedown analysis of kinematic hardening material (2019)
  6. Wang, Guoqiang; Yu, Bo: PAL-Hom method for QP and an application to LP (2019)
  7. 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)
  8. Birgin, E. G.; Haeser, G.; Ramos, Alberto: Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points (2018)
  9. Caliciotti, Andrea; Fasano, Giovanni; Nash, Stephen G.; Roma, Massimo: An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization (2018)
  10. Haeser, Gabriel: A second-order optimality condition with first- and second-order complementarity associated with global convergence of algorithms (2018)
  11. Kuhlmann, Renke; Büskens, Christof: A primal-dual augmented Lagrangian penalty-interior-point filter line search algorithm (2018)
  12. Ma, Ding; Judd, Kenneth L.; Orban, Dominique; Saunders, Michael A.: Stabilized optimization via an NCL algorithm (2018)
  13. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  14. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  15. Zhang, Hao; Ni, Qin: A new augmented Lagrangian method for equality constrained optimization with simple unconstrained subproblem (2017)
  16. 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)
  17. Birgin, E. G.; Martínez, J. M.: On the application of an augmented Lagrangian algorithm to some portfolio problems (2016)
  18. Cho, Won Sang; Gainer, James S.; Kim, Doojin; Lim, Sung Hak; Matchev, Konstantin T.; Moortgat, Filip; Pape, Luc; Park, Myeonghun: OPTIMASS: a package for the minimization of kinematic mass functions with constraints (2016)
  19. Chrapary, Hagen; Ren, Yue: The software portal swMATH: a state of the art report and next steps (2016)
  20. Curtis, Frank E.; Gould, Nicholas I. M.; Jiang, Hao; Robinson, Daniel P.: Adaptive augmented Lagrangian methods: algorithms and practical numerical experience (2016)

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