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 under the GPL license, along with documentation and examples

References in zbMATH (referenced in 54 articles , 1 standard article )

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  1. Johnstone, Patrick R.; Eckstein, Jonathan: Single-forward-step projective splitting: exploiting cocoercivity (2021)
  2. Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez: MRCpy: A Library for Minimax Risk Classifiers (2021) arXiv
  3. Lin, Yun Hui; Tian, Qingyun: Exact approaches for competitive facility location with discrete attractiveness (2021)
  4. Moehle, Nicholas; Kochenderfer, Mykel J.; Boyd, Stephen; Ang, Andrew: Tax-aware portfolio construction via convex optimization (2021)
  5. O’Donoghue, Brendan: Operator splitting for a homogeneous embedding of the linear complementarity problem (2021)
  6. Tuck, Jonathan; Barratt, Shane; Boyd, Stephen: A distributed method for fitting Laplacian regularized stratified models (2021)
  7. Zhang, Guodong; Bao, Xuchan; Lessard, Laurent; Grosse, Roger: A unified analysis of first-order methods for smooth games via integral quadratic constraints (2021)
  8. Abrishami, Tara; Guillen, Nestor; Rule, Parker; Schutzman, Zachary; Solomon, Justin; Weighill, Thomas; Wu, Si: Geometry of graph partitions via optimal transport (2020)
  9. Agrawal, Akshay; Boyd, Stephen: Disciplined quasiconvex programming (2020)
  10. Bienstock, Dan; Escobar, Mauro; Gentile, Claudio; Liberti, Leo: Mathematical programming formulations for the alternating current optimal power flow problem (2020)
  11. Ceccon, Francesco; Siirola, John D.; Misener, Ruth: SUSPECT: MINLP special structure detector for Pyomo (2020)
  12. Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
  13. Fu, Anqi; Zhang, Junzi; Boyd, Stephen: Anderson accelerated Douglas-Rachford splitting (2020)
  14. Ghate, Archis: Inverse optimization in semi-infinite linear programs (2020)
  15. Hu, Yifan; Chen, Xin; He, Niao: Sample complexity of sample average approximation for conditional stochastic optimization (2020)
  16. Lesage-Landry, Antoine; Shames, Iman; Taylor, Joshua A.: Predictive online convex optimization (2020)
  17. Lesage-Landry, Antoine; Taylor, Joshua A.: A second-order cone model of transmission planning with alternating and direct current lines (2020)
  18. Massias, Mathurin; Vaiter, Samuel; Gramfort, Alexandre; Salmon, Joseph: Dual extrapolation for sparse GLMs (2020)
  19. McKinnon, Karen A.; Poppick, Andrew: Estimating changes in the observed relationship between humidity and temperature using noncrossing quantile smoothing splines (2020)
  20. Nystrup, Peter; Lindström, Erik; Pinson, Pierre; Madsen, Henrik: Temporal hierarchies with autocorrelation for load forecasting (2020)

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