CVXOPT; Python Software for Convex Optimization. CVXOPT is a free software package for convex optimization based on the Python programming language. It can be used with the interactive Python interpreter, on the command line by executing Python scripts, or integrated in other software via Python extension modules. Its main purpose is to make the development of software for convex optimization applications straightforward by building on Python’s extensive standard library and on the strengths of Python as a high-level programming language.

References in zbMATH (referenced in 62 articles )

Showing results 1 to 20 of 62.
Sorted by year (citations)

1 2 3 4 next

  1. Faist, Philippe; Berta, Mario; Brandao, Fernando G. S. L.: Thermodynamic implementations of quantum processes (2021)
  2. Hokanson, Jeffrey M.; Constantine, Paul G.: A Lipschitz matrix for parameter reduction in computational science (2021)
  3. Liberti, Leo; Poirion, Pierre-Louis; Vu, Ky: Random projections for conic programs (2021)
  4. Messud, Jérémie; Poncet, Raphaël; Lambaré, Gilles: Optimal transport in full-waveform inversion: analysis and practice of the multidimensional Kantorovich-Rubinstein norm (2021)
  5. Naldi, Simone; Sinn, Rainer: Conic programming: infeasibility certificates and projective geometry (2021)
  6. Robert Andrew Martin: PyPortfolioOpt: portfolio optimization in Python (2021) not zbMATH
  7. Stöckel, Andreas; Eliasmith, Chris: Passive nonlinear dendritic interactions as a computational resource in spiking neural networks (2021)
  8. Zhang, Richard Y.; Lavaei, Javad: Sparse semidefinite programs with guaranteed near-linear time complexity via dualized clique tree conversion (2021)
  9. Arizmendi, Octavio; Tarrago, Pierre; Vargas, Carlos: Subordination methods for free deconvolution (2020)
  10. Cohen, Samuel N.; Reisinger, Christoph; Wang, Sheng: Detecting and repairing arbitrage in traded option prices (2020)
  11. Feppon, Florian; Allaire, Grégoire; Dapogny, Charles: Null space gradient flows for constrained optimization with applications to shape optimization (2020)
  12. Otani, Naoya; Otsubo, Yosuke; Koike, Tetsuya; Sugiyama, Masashi: Binary classification with ambiguous training data (2020)
  13. Siglidis, Giannis; Nikolentzos, Giannis; Limnios, Stratis; Giatsidis, Christos; Skianis, Konstantinos; Vazirgiannis, Michalis: GraKeL: a graph kernel library in Python (2020)
  14. Akrami, Hannaneh; Mehlhorn, Kurt; Odland, Tommy: Ratio-balanced maximum flows (2019)
  15. Bűrmen, Árpád; Fajfar, Iztok: Mesh adaptive direct search with simplicial Hessian update (2019)
  16. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  17. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  18. Lorenzen, Stephan S.; Igel, Christian; Seldin, Yevgeny: On PAC-Bayesian bounds for random forests (2019)
  19. Ramezanali, Mohammad; Mitra, Partha P.; Sengupta, Anirvan M.: Critical behavior and universality classes for an algorithmic phase transition in sparse reconstruction (2019)
  20. Bar-On, Achiya; Dinur, Itai; Dunkelman, Orr; Hod, Rani; Keller, Nathan; Ronen, Eyal; Shamir, Adi: Tight bounds on online checkpointing algorithms (2018)

1 2 3 4 next