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 45 articles )

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  1. Bűrmen, Árpád; Fajfar, Iztok: Mesh adaptive direct search with simplicial Hessian update (2019)
  2. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  3. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  4. Lorenzen, Stephan S.; Igel, Christian; Seldin, Yevgeny: On PAC-Bayesian bounds for random forests (2019)
  5. Ramezanali, Mohammad; Mitra, Partha P.; Sengupta, Anirvan M.: Critical behavior and universality classes for an algorithmic phase transition in sparse reconstruction (2019)
  6. Bonami, Pierre; Günlük, Oktay; Linderoth, Jeff: Globally solving nonconvex quadratic programming problems with box constraints via integer programming methods (2018)
  7. 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)
  8. Henning Seidler, Timo de Wolff: An Experimental Comparison of SONC and SOS Certificates for Unconstrained Optimization (2018) arXiv
  9. Jha, Susmit; Raman, Vasumathi; Sadigh, Dorsa; Seshia, Sanjit A.: Safe autonomy under perception uncertainty using chance-constrained temporal logic (2018)
  10. Loiseau, Jean-Christophe; Brunton, Steven L.: Constrained sparse Galerkin regression (2018)
  11. Copp, David A.; Hespanha, João P.: Simultaneous nonlinear model predictive control and state estimation (2017)
  12. Friedlander, Michael P.; Goh, Gabriel: Efficient evaluation of scaled proximal operators (2017)
  13. Hallac, David; Wong, Christopher; Diamond, Steven; Sharang, Abhijit; Sosič, Rok; Boyd, Stephen; Leskovec, Jure: SnapVX: a network-based convex optimization solver (2017)
  14. Li, Jinchao; Andersen, Martin S.; Vandenberghe, Lieven: Inexact proximal Newton methods for self-concordant functions (2017)
  15. Parmar, Jupinder; Rahman, Saarim; Thiara, Jaskaran: A formulation of a matrix sparsity approach for the quantum ordered search algorithm (2017)
  16. Albin, Nathan; Klarmann, Joshua: An algorithmic exploration of the existence of high-order summation by parts operators with diagonal norm (2016)
  17. Diamond, Steven; Boyd, Stephen: Matrix-free convex optimization modeling (2016)
  18. Diamond, Steven; Boyd, Stephen: CVXPY: a Python-embedded modeling language for convex optimization (2016)
  19. Doran, Gary; Ray, Soumya: Multiple-instance learning from distributions (2016)
  20. Shakeri, Heman; Poggi-Corradini, Pietro; Scoglio, Caterina; Albin, Nathan: Generalized network measures based on modulus of families of walks (2016)

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