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 39 articles , 1 standard article )

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  1. Abrishami, Tara; Guillen, Nestor; Rule, Parker; Schutzman, Zachary; Solomon, Justin; Weighill, Thomas; Wu, Si: Geometry of graph partitions via optimal transport (2020)
  2. Ceccon, Francesco; Siirola, John D.; Misener, Ruth: SUSPECT: MINLP special structure detector for Pyomo (2020)
  3. Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
  4. Ghate, Archis: Inverse optimization in semi-infinite linear programs (2020)
  5. Hu, Yifan; Chen, Xin; He, Niao: Sample complexity of sample average approximation for conditional stochastic optimization (2020)
  6. Lesage-Landry, Antoine; Shames, Iman; Taylor, Joshua A.: Predictive online convex optimization (2020)
  7. Lesage-Landry, Antoine; Taylor, Joshua A.: A second-order cone model of transmission planning with alternating and direct current lines (2020)
  8. McKinnon, Karen A.; Poppick, Andrew: Estimating changes in the observed relationship between humidity and temperature using noncrossing quantile smoothing splines (2020)
  9. Nystrup, Peter; Lindström, Erik; Pinson, Pierre; Madsen, Henrik: Temporal hierarchies with autocorrelation for load forecasting (2020)
  10. Pereira, Carlos A.; Vermeire, Brian C.: Fully-discrete analysis of high-order spatial discretizations with optimal explicit Runge-Kutta methods (2020)
  11. Yin, Ping; Diamond, Steven; Lin, Bill; Boyd, Stephen: Network optimization for unified packet and circuit switched networks (2020)
  12. Agrawal, Akshay; Diamond, Steven; Boyd, Stephen: Disciplined geometric programming (2019)
  13. Albin, Nathan; Fernando, Nethali; Poggi-Corradini, Pietro: Modulus metrics on networks (2019)
  14. Busseti, Enzo; Moursi, Walaa M.; Boyd, Stephen: Solution refinement at regular points of conic problems (2019)
  15. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  16. Fu, Anqi; Ungun, Barıṣ; Xing, Lei; Boyd, Stephen: A convex optimization approach to radiation treatment planning with dose constraints (2019)
  17. Koep, Niklas; Behboodi, Arash; Mathar, Rudolf: An introduction to compressed sensing (2019)
  18. Masood, Muhammad A.; Doshi-Velez, Finale: A particle-based variational approach to Bayesian non-negative matrix factorization (2019)
  19. Moehle, Nicholas; Busseti, Enzo; Boyd, Stephen; Wytock, Matt: Dynamic energy management (2019)
  20. Moehle, Nicholas; Shen, Xinyue; Luo, Zhi-Quan; Boyd, Stephen: A distributed method for optimal capacity reservation (2019)

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