CVXPY

CVXPY: A Python-Embedded Modeling Language for Convex Optimization. CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high-level features of Python such as parallelism and object-oriented design. CVXPY is available at http://www.cvxpy.org/ under the GPL license, along with documentation and examples


References in zbMATH (referenced in 71 articles , 1 standard article )

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  1. Dubois, Pierre; Gomez, Thomas; Planckaert, Laurent; Perret, Laurent: Machine learning for fluid flow reconstruction from limited measurements (2022)
  2. Kroer, Christian; Peysakhovich, Alexander; Sodomka, Eric; Stier-Moses, Nicolas E.: Computing large market equilibria using abstractions (2022)
  3. Li, Xiaoyue; Uysal, A. Sinem; Mulvey, John M.: Multi-period portfolio optimization using model predictive control with mean-variance and risk parity frameworks (2022)
  4. Akrour, Riad; Atamna, Asma; Peters, Jan: Convex optimization with an interpolation-based projection and its application to deep learning (2021)
  5. Aktaş, Fatih S.; Ekmekcioglu, Ömer; Pinar, Mustafa Ç.: Provably optimal sparse solutions to overdetermined linear systems with non-negativity constraints in a least-squares sense by implicit enumeration (2021)
  6. Askari, Armin; Rebjock, Quentin; d’Aspremont, Alexandre; El Ghaoui, Laurent: FANOK: knockoffs in linear time (2021)
  7. Barratt, Shane; Angeris, Guillermo; Boyd, Stephen: Automatic repair of convex optimization problems (2021)
  8. Barratt, Shane; Angeris, Guillermo; Boyd, Stephen: Optimal representative sample weighting (2021)
  9. Bertsimas, Dimitris; Stellato, Bartolomeo: The voice of optimization (2021)
  10. Bouza, Gemayqzel; Quintana, Ernest; Tammer, Christiane: A steepest descent method for set optimization problems with set-valued mappings of finite cardinality (2021)
  11. Hančová, Martina; Gajdoš, Andrej; Hanč, Jozef; Vozáriková, Gabriela: Estimating variances in time series kriging using convex optimization and empirical BLUPs (2021)
  12. Johnstone, Patrick R.; Eckstein, Jonathan: Single-forward-step projective splitting: exploiting cocoercivity (2021)
  13. Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez: MRCpy: A Library for Minimax Risk Classifiers (2021) arXiv
  14. Lin, Yun Hui; Tian, Qingyun: Exact approaches for competitive facility location with discrete attractiveness (2021)
  15. Mendez-Civieta, Alvaro; Aguilera-Morillo, M. Carmen; Lillo, Rosa E.: Adaptive sparse group LASSO in quantile regression (2021)
  16. Moehle, Nicholas; Kochenderfer, Mykel J.; Boyd, Stephen; Ang, Andrew: Tax-aware portfolio construction via convex optimization (2021)
  17. Murray, Riley; Chandrasekaran, Venkat; Wierman, Adam: Newton polytopes and relative entropy optimization (2021)
  18. O’Donoghue, Brendan: Operator splitting for a homogeneous embedding of the linear complementarity problem (2021)
  19. Tanneau, Mathieu; Anjos, Miguel F.; Lodi, Andrea: Design and implementation of a modular interior-point solver for linear optimization (2021)
  20. Tuck, Jonathan; Barratt, Shane; Boyd, Stephen: A distributed method for fitting Laplacian regularized stratified models (2021)

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