CVXGEN

CVXGEN: a code generator for embedded convex optimization. CVXGEN is a software tool that takes a high level description of a convex optimization problem family, and automatically generates custom C code that compiles into a reliable, high speed solver for the problem family. The current implementation targets problem families that can be transformed, using disciplined convex programming techniques, to convex quadratic programs of modest size. CVXGEN generates simple, flat, library-free code suitable for embedding in real-time applications. The generated code is almost branch free, and so has highly predictable run-time behavior. The combination of regularization (both static and dynamic) and iterative refinement in the search direction computation yields reliable performance, even with poor quality data. In this paper we describe how CVXGEN is implemented, and give some results on the speed and reliability of the automatically generated solvers.


References in zbMATH (referenced in 37 articles , 2 standard articles )

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  1. Kim, Jihan; Back, Juhoon; Park, Gyunghoon; Lee, Chanhwa; Shim, Hyungbo; Voulgaris, Petros G.: Neutralizing zero dynamics attack on sampled-data systems via generalized holds (2020)
  2. Liao-McPherson, Dominic; Kolmanovsky, Ilya: FBstab: a proximally stabilized semismooth algorithm for convex quadratic programming (2020)
  3. Takapoui, Reza; Moehle, Nicholas; Boyd, Stephen; Bemporad, Alberto: A simple effective heuristic for embedded mixed-integer quadratic programming (2020)
  4. Ahmadi, Amir Ali; Majumdar, Anirudha: DSOS and SDSOS optimization: more tractable alternatives to sum of squares and semidefinite optimization (2019)
  5. Hu, Yunyi; Andersen, Martin S.; Nagy, James G.: Spectral computed tomography with linearization and preconditioning (2019)
  6. Nystrup, Peter; Boyd, Stephen; Lindström, Erik; Madsen, Henrik: Multi-period portfolio selection with drawdown control (2019)
  7. Perne, Matija; Gerkšič, Samo; Pregelj, Boštjan: Soft inequality constraints in gradient method and fast gradient method for quadratic programming (2019)
  8. Kouzoupis, Dimitris; Frison, Gianluca; Zanelli, Andrea; Diehl, Moritz: Recent advances in quadratic programming algorithms for nonlinear model predictive control (2018)
  9. Nikolić, Milutin; Borovac, Branislav; Raković, Mirko: Dynamic balance preservation and prevention of sliding for humanoid robots in the presence of multiple spatial contacts (2018)
  10. Amir Ali Ahmadi, Anirudha Majumdar: DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization (2017) arXiv
  11. Blanchini, Franco; Fenu, Gianfranco; Giordano, Giulia; Pellegrino, Felice Andrea: A convex programming approach to the inverse kinematics problem for manipulators under constraints (2017)
  12. Kersting, Kristian; Mladenov, Martin; Tokmakov, Pavel: Relational linear programming (2017)
  13. Ahmadi, Amir Ali; Majumdar, Anirudha: Some applications of polynomial optimization in operations research and real-time decision making (2016)
  14. Diamond, Steven; Boyd, Stephen: Matrix-free convex optimization modeling (2016)
  15. Schwickart, Tim; Voos, Holger; Hadji-Minaglou, Jean-Régis; Darouach, Mohamed: A fast model-predictive speed controller for minimised charge consumption of electric vehicles (2016)
  16. Wang, Timothy; Jobredeaux, Romain; Pantel, Marc; Garoche, Pierre-Loic; Feron, Eric; Henrion, Didier: Credible autocoding of convex optimization algorithms (2016)
  17. Borwein, Jonathan M.; Luke, D. Russell: Duality and convex programming (2015)
  18. Frasch, Janick V.; Sager, Sebastian; Diehl, Moritz: A parallel quadratic programming method for dynamic optimization problems (2015)
  19. Giselsson, Pontus; Boyd, Stephen: Metric selection in fast dual forward-backward splitting (2015)
  20. Hartley, Edward N.; Maciejowski, Jan M.: Field programmable gate array based predictive control system for spacecraft rendezvous in elliptical orbits (2015)

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