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.

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References in zbMATH (referenced in 302 articles , 3 standard articles )

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  1. Chen, X.; Toint, Ph. L.: High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity terms (2021)
  2. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  3. Birgin, E. G.; Martínez, J. M.: Complexity and performance of an augmented Lagrangian algorithm (2020)
  4. Galabova, I. L.; Hall, J. A. J.: The `Idiot’ crash quadratic penalty algorithm for linear programming and its application to linearizations of quadratic assignment problems (2020)
  5. Gratton, S.; Toint, Ph. L.: A note on solving nonlinear optimization problems in variable precision (2020)
  6. Leyffer, Sven; Vanaret, Charlie: An augmented Lagrangian filter method (2020)
  7. Liu, Changshuo; Boumal, Nicolas: Simple algorithms for optimization on Riemannian manifolds with constraints (2020)
  8. Pfeiffer, Laurent: Optimality conditions in variational form for non-linear constrained stochastic control problems (2020)
  9. Wauters, Jolan; Keane, Andy; Degroote, Joris: Development of an adaptive infill criterion for constrained multi-objective asynchronous surrogate-based optimization (2020)
  10. Armand, Paul; Tran, Ngoc Nguyen: An augmented Lagrangian method for equality constrained optimization with rapid infeasibility detection capabilities (2019)
  11. Chen, Xiaojun; Toint, Ph. L.; Wang, H.: Complexity of partially separable convexly constrained optimization with non-Lipschitzian singularities (2019)
  12. 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)
  13. Hante, Falk M.; Schmidt, Martin: Complementarity-based nonlinear programming techniques for optimal mixing in gas networks (2019)
  14. Paternain, Santiago; Mokhtari, Aryan; Ribeiro, Alejandro: A Newton-based method for nonconvex optimization with fast evasion of saddle points (2019)
  15. Ri, Jun-Hyok; Hong, Hyon-Sik: A basis reduction method using proper orthogonal decomposition for shakedown analysis of kinematic hardening material (2019)
  16. Wang, Guoqiang; Yu, Bo: PAL-Hom method for QP and an application to LP (2019)
  17. 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)
  18. Birgin, E. G.; Haeser, G.; Ramos, Alberto: Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points (2018)
  19. 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)
  20. Haeser, Gabriel: A second-order optimality condition with first- and second-order complementarity associated with global convergence of algorithms (2018)

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