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 66 articles )

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  1. Ben Hermans, Andreas Themelis, Panagiotis Patrinos: QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs (2020) arXiv
  2. 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)
  3. Cristofari, Andrea; Rinaldi, Francesco; Tudisco, Francesco: Total variation based community detection using a nonlinear optimization approach (2020)
  4. Du, Wenqian; Benamar, Faïz: A compact form dynamics controller for a high-DOF tetrapod-on-wheel robot with one manipulator via null space based convex optimization and compatible impedance controllers (2020)
  5. Folberth, James; Becker, Stephen: Safe feature elimination for non-negativity constrained convex optimization (2020)
  6. Gros, Sébastien; Zanon, Mario; Quirynen, Rien; Bemporad, Alberto; Diehl, Moritz: From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020)
  7. Liao-McPherson, Dominic; Kolmanovsky, Ilya: FBstab: a proximally stabilized semismooth algorithm for convex quadratic programming (2020)
  8. Na, Sen; Anitescu, Mihai: Exponential decay in the sensitivity analysis of nonlinear dynamic programming (2020)
  9. Stellato, Bartolomeo; Banjac, Goran; Goulart, Paul; Bemporad, Alberto; Boyd, Stephen: OSQP: an operator splitting solver for quadratic programs (2020)
  10. Takapoui, Reza; Moehle, Nicholas; Boyd, Stephen; Bemporad, Alberto: A simple effective heuristic for embedded mixed-integer quadratic programming (2020)
  11. van den Berg, Ewout: A hybrid quasi-Newton projected-gradient method with application to lasso and basis-pursuit denoising (2020)
  12. Yue Jiang, Wolfgang Stuerzlinger, Matthias Zwicker, Christof Lutteroth: ORCSolver: An Efficient Solver for Adaptive GUI Layout with OR-Constraints (2020) arXiv
  13. Zanelli, A.; Domahidi, A.; Jerez, J.; Morari, M.: FORCES NLP: an efficient implementation of interior-point methods for multistage nonlinear nonconvex programs (2020)
  14. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  15. Bian, Chentong; Zhu, Tong; Yin, Guodong; Xu, Liwei: Integrated speed planning and friction coefficient estimation algorithm for intelligent electric vehicles (2019)
  16. Chen, Hao; Chen, Jian; Liu, Zhiyang; Lu, Huaxin: Real-time optimal energy management for a fuel cell/battery hybrid system (2019)
  17. Deng, Haoyang; Ohtsuka, Toshiyuki: A parallel Newton-type method for nonlinear model predictive control (2019)
  18. 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)
  19. Hose, Dominik; Hanss, Michael: Fuzzy linear least squares for the identification of possibilistic regression models (2019)
  20. 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)

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