qpOASES – Online Active Set Strategy. qpOASES is an open-source C++ implementation of the recently proposed online active set strategy (see [Ferreau, 2006], [Ferreau et al., 2008]), which was inspired by important observations from the field of parametric quadratic programming. It has several theoretical features that make it particularly suited for model predictive control (MPC) applications. The software package qpOASES implements these ideas and has already been successfully used within industrial projects and, e.g., for closed-loop control of a real-world Diesel engine [Ferreau et al., 2007]. Recently, a couple of numerical modifications (as proposed in [Potschka et al., 2010]) have been implemented that greatly increase qpOASES’s reliability when solving semi-definite, ill-posed or degenerated convex QPs. (Source: http://plato.asu.edu)

References in zbMATH (referenced in 59 articles )

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  1. Bettiol, Enrico; Létocart, Lucas; Rinaldi, Francesco; Traversi, Emiliano: A conjugate direction based simplicial decomposition framework for solving a specific class of dense convex quadratic programs (2020)
  2. Folberth, James; Becker, Stephen: Safe feature elimination for non-negativity constrained convex optimization (2020)
  3. Gros, Sébastien; Zanon, Mario; Quirynen, Rien; Bemporad, Alberto; Diehl, Moritz: From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020)
  4. Liao-McPherson, Dominic; Kolmanovsky, Ilya: FBstab: a proximally stabilized semismooth algorithm for convex quadratic programming (2020)
  5. Takapoui, Reza; Moehle, Nicholas; Boyd, Stephen; Bemporad, Alberto: A simple effective heuristic for embedded mixed-integer quadratic programming (2020)
  6. van den Berg, Ewout: A hybrid quasi-Newton projected-gradient method with application to lasso and basis-pursuit denoising (2020)
  7. Zanelli, A.; Domahidi, A.; Jerez, J.; Morari, M.: FORCES NLP: an efficient implementation of interior-point methods for multistage nonlinear nonconvex programs (2020)
  8. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  9. Bian, Chentong; Zhu, Tong; Yin, Guodong; Xu, Liwei: Integrated speed planning and friction coefficient estimation algorithm for intelligent electric vehicles (2019)
  10. Deng, Haoyang; Ohtsuka, Toshiyuki: A parallel Newton-type method for nonlinear model predictive control (2019)
  11. 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)
  12. Hose, Dominik; Hanss, Michael: Fuzzy linear least squares for the identification of possibilistic regression models (2019)
  13. Kouzoupis, D.; Klintberg, E.; Frison, G.; Gros, S.; Diehl, M.: A dual Newton strategy for tree-sparse quadratic programs and its implementation in the open-source software treeQP (2019)
  14. Otta, Pavel; Burant, Jiří; Šantin, Ondřej; Havlena, Vladimír: Newton projection with proportioning using iterative linear algebra for model predictive control with long prediction horizon (2019)
  15. Perne, Matija; Gerkšič, Samo; Pregelj, Boštjan: Soft inequality constraints in gradient method and fast gradient method for quadratic programming (2019)
  16. Robin Verschueren, Gianluca Frison, Dimitris Kouzoupis, Niels van Duijkeren, Andrea Zanelli, Branimir Novoselnik, Jonathan Frey, Thivaharan Albin, Rien Quirynen, Moritz Diehl: acados: a modular open-source framework for fast embedded optimal control (2019) arXiv
  17. Wang, Guoqiang; Yu, Bo: PAL-Hom method for QP and an application to LP (2019)
  18. Weber, Tobias; Sager, Sebastian; Gleixner, Ambros: Solving quadratic programs to high precision using scaled iterative refinement (2019)
  19. Huber, Andreas; Gerdts, Matthias; Bertolazzi, Enrico: Structure exploitation in an interior-point method for fully discretized, state constrained optimal control problems (2018)
  20. Korda, Milan; Mezić, Igor: Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control (2018)

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