CVXR

CVXR: An R Package for Disciplined Convex Optimization. CVXR is an R package that provides an object-oriented modeling language for convex optimization, similar to CVX, CVXPY, YALMIP, and Convex.jl. It allows the user to formulate convex optimization problems in a natural mathematical syntax rather than the restrictive standard form required by most solvers. The user specifies an objective and set of constraints by combining constants, variables, and parameters using a library of functions with known mathematical properties. CVXR then applies signed disciplined convex programming (DCP) to verify the problem’s convexity. Once verified, the problem is converted into standard conic form using graph implementations and passed to a cone solver such as ECOS or SCS. We demonstrate CVXR’s modeling framework with several applications.


References in zbMATH (referenced in 19 articles )

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  1. Severn, Katie E.; Dryden, Ian L.; Preston, Simon P.: Manifold valued data analysis of samples of networks, with applications in corpus linguistics (2022)
  2. Zhao, Jun; Yan, Guan’ao; Zhang, Yi: Robust estimation and shrinkage in ultrahigh dimensional expectile regression with heavy tails and variance heterogeneity (2022)
  3. Amato, Umberto; Antoniadis, Anestis; De Feis, Italia; Gijbels, Irene: Penalised robust estimators for sparse and high-dimensional linear models (2021)
  4. Banerjee, Trambak; Liu, Qiang; Mukherjee, Gourab; Sun, Wengunag: A general framework for empirical Bayes estimation in discrete linear exponential family (2021)
  5. Barratt, Shane; Angeris, Guillermo; Boyd, Stephen: Optimal representative sample weighting (2021)
  6. Chen, Xiaowei; Chong, Wing Fung; Feng, Runhuan; Zhang, Linfeng: Pandemic risk management: resources contingency planning and allocation (2021)
  7. Hančová, Martina; Gajdoš, Andrej; Hanč, Jozef; Vozáriková, Gabriela: Estimating variances in time series kriging using convex optimization and empirical BLUPs (2021)
  8. Hirshberg, David A.; Wager, Stefan: Augmented minimax linear estimation (2021)
  9. Lee, Sokbae; Liao, Yuan; Seo, Myung Hwan; Shin, Youngki: Sparse HP filter: finding kinks in the COVID-19 contact rate (2021)
  10. Moehle, Nicholas; Kochenderfer, Mykel J.; Boyd, Stephen; Ang, Andrew: Tax-aware portfolio construction via convex optimization (2021)
  11. Qin, Shanshan; Ding, Hao; Wu, Yuehua; Liu, Feng: High-dimensional sign-constrained feature selection and grouping (2021)
  12. Agrawal, Akshay; Boyd, Stephen: Disciplined quasiconvex programming (2020)
  13. Kisung You, Changhee Suh: Rdimtools: An R package for Dimension Reduction and Intrinsic Dimension Estimation (2020) arXiv
  14. Kumar, Sandeep; Ying, Jiaxi; Cardoso, José Vinícius de M.; Palomar, Daniel P.: A unified framework for structured graph learning via spectral constraints (2020)
  15. Agrawal, Akshay; Diamond, Steven; Boyd, Stephen: Disciplined geometric programming (2019)
  16. Busseti, Enzo; Moursi, Walaa M.; Boyd, Stephen: Solution refinement at regular points of conic problems (2019)
  17. Moehle, Nicholas; Busseti, Enzo; Boyd, Stephen; Wytock, Matt: Dynamic energy management (2019)
  18. Adam Rahman: sdpt3r: Semidefinite Quadratic Linear Programming in R (2018) not zbMATH
  19. Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd: CVXR: An R Package for Disciplined Convex Optimization (2017) arXiv