OOQP is an object-oriented C++ package, based on a primal-dual interior-point method, for solving convex quadratic programming problems (QPs). It contains code that can be used ”out of the box” to solve a variety of structured QPs, including general sparse QPs, QPs arising from support vector machines, Huber regression problems, and QPs with bound constraints. OOQP also can be used as a framework can be used to design efficient solvers for new classes of structured QPs. Its design allows for easy substitution of the linear algebra modules, allowing different standard linear algebra packages to be tried.

References in zbMATH (referenced in 24 articles , 1 standard article )

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  1. Niu, Lingfeng; Zhou, Ruizhi; Zhao, Xi; Shi, Yong: Two new decomposition algorithms for training bound-constrained support vector machines (2015)
  2. Ferreau, Hans Joachim; Kirches, Christian; Potschka, Andreas; Bock, Hans Georg; Diehl, Moritz: qpOASES: a parametric active-set algorithm for quadratic programming (2014)
  3. Büskens, Christof; Wassel, Dennis: The ESA NLP solver WORHP (2013)
  4. Friedlander, M.P.; Orban, D.: A primal-dual regularized interior-point method for convex quadratic programs (2012)
  5. Gondzio, Jacek: Interior point methods 25 years later (2012)
  6. Munsell, Brent C.; Temlyakov, Andrew; Styner, Martin; Wang, Song: Pre-organizing shape instances for landmark-based shape correspondence (2012) ioport
  7. Petra, Cosmin G.; Anitescu, Mihai: A preconditioning technique for Schur complement systems arising in stochastic optimization (2012)
  8. Kirches, Christian; Bock, Hans Georg; Schlöder, Johannes P.; Sager, Sebastian: A factorization with update procedures for a KKT matrix arising in direct optimal control (2011)
  9. Niu, Lingfeng; Yuan, Ya-Xiang: A parallel decomposition algorithm for training multiclass kernel-based vector machines (2011)
  10. Woodsend, Kristian; Gondzio, Jacek: Exploiting separability in large-scale linear support vector machine training (2011)
  11. D’apuzzo, Marco; De Simone, Valentina; Di Serafino, Daniela: Starting-point strategies for an infeasible potential reduction method (2010)
  12. D’Apuzzo, Marco; De Simone, Valentina; di Serafino, Daniela: On mutual impact of numerical linear algebra and large-scale optimization with focus on interior point methods (2010)
  13. Gertz, E.Michael; Griffin, Joshua D.: Using an iterative linear solver in an interior-point method for generating support vector machines (2010)
  14. Gondzio, Jacek; Grothey, Andreas: Exploiting structure in parallel implementation of interior point methods for optimization (2009)
  15. Goulart, Paul J.; Kerrigan, Eric C.; Ralph, Daniel: Efficient robust optimization for robust control with constraints (2008)
  16. Cafieri, S.; D’apuzzo, M.; De Simone, V.; di Serafino, D.: On the iterative solution of KKT systems in potential reduction software for large-scale quadratic problems (2007)
  17. Gondzio, Jacek; Grothey, Andreas: Parallel interior-point solver for structured quadratic programs: Application to financial planning problems (2007)
  18. Dominguez, Juan; González-Lima, María D.: A primal-dual interior-point algorithm for quadratic programming (2006)
  19. Beliakov, G.: Monotonicity preserving approximation of multivariate scattered data (2005)
  20. Bartlett, Roscoe A.; van Bloemen Waanders, Bart G.; Heroux, Michael A.: Vector reduction/transformation operators (2004)

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