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 52 articles )

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  1. Naldi, Simone; Sinn, Rainer: Conic programming: infeasibility certificates and projective geometry (2021)
  2. Feppon, Florian; Allaire, Grégoire; Dapogny, Charles: Null space gradient flows for constrained optimization with applications to shape optimization (2020)
  3. Otani, Naoya; Otsubo, Yosuke; Koike, Tetsuya; Sugiyama, Masashi: Binary classification with ambiguous training data (2020)
  4. Siglidis, Giannis; Nikolentzos, Giannis; Limnios, Stratis; Giatsidis, Christos; Skianis, Konstantinos; Vazirgiannis, Michalis: GraKeL: a graph kernel library in Python (2020)
  5. Akrami, Hannaneh; Mehlhorn, Kurt; Odland, Tommy: Ratio-balanced maximum flows (2019)
  6. Bűrmen, Árpád; Fajfar, Iztok: Mesh adaptive direct search with simplicial Hessian update (2019)
  7. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  8. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  9. Lorenzen, Stephan S.; Igel, Christian; Seldin, Yevgeny: On PAC-Bayesian bounds for random forests (2019)
  10. Ramezanali, Mohammad; Mitra, Partha P.; Sengupta, Anirvan M.: Critical behavior and universality classes for an algorithmic phase transition in sparse reconstruction (2019)
  11. Bonami, Pierre; Günlük, Oktay; Linderoth, Jeff: Globally solving nonconvex quadratic programming problems with box constraints via integer programming methods (2018)
  12. Charkhgard, Hadi; Savelsbergh, Martin; Talebian, Masoud: A linear programming based algorithm to solve a class of optimization problems with a multi-linear objective function and affine constraints (2018)
  13. Henning Seidler, Timo de Wolff: An Experimental Comparison of SONC and SOS Certificates for Unconstrained Optimization (2018) arXiv
  14. Jha, Susmit; Raman, Vasumathi; Sadigh, Dorsa; Seshia, Sanjit A.: Safe autonomy under perception uncertainty using chance-constrained temporal logic (2018)
  15. Loiseau, Jean-Christophe; Brunton, Steven L.: Constrained sparse Galerkin regression (2018)
  16. Copp, David A.; Hespanha, João P.: Simultaneous nonlinear model predictive control and state estimation (2017)
  17. Friedlander, Michael P.; Goh, Gabriel: Efficient evaluation of scaled proximal operators (2017)
  18. Hallac, David; Wong, Christopher; Diamond, Steven; Sharang, Abhijit; Sosič, Rok; Boyd, Stephen; Leskovec, Jure: SnapVX: a network-based convex optimization solver (2017)
  19. Li, Jinchao; Andersen, Martin S.; Vandenberghe, Lieven: Inexact proximal Newton methods for self-concordant functions (2017)
  20. Parmar, Jupinder; Rahman, Saarim; Thiara, Jaskaran: A formulation of a matrix sparsity approach for the quantum ordered search algorithm (2017)

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