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 36 articles )

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  1. Keskar, N.; Wächter, Andreas: A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization (2019)
  2. 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)
  3. Henning Seidler, Timo de Wolff: An Experimental Comparison of SONC and SOS Certificates for Unconstrained Optimization (2018) arXiv
  4. Jha, Susmit; Raman, Vasumathi; Sadigh, Dorsa; Seshia, Sanjit A.: Safe autonomy under perception uncertainty using chance-constrained temporal logic (2018)
  5. Copp, David A.; Hespanha, João P.: Simultaneous nonlinear model predictive control and state estimation (2017)
  6. Friedlander, Michael P.; Goh, Gabriel: Efficient evaluation of scaled proximal operators (2017)
  7. Hallac, David; Wong, Christopher; Diamond, Steven; Sharang, Abhijit; Sosič, Rok; Boyd, Stephen; Leskovec, Jure: SnapVX: a network-based convex optimization solver (2017)
  8. Li, Jinchao; Andersen, Martin S.; Vandenberghe, Lieven: Inexact proximal Newton methods for self-concordant functions (2017)
  9. Parmar, Jupinder; Rahman, Saarim; Thiara, Jaskaran: A formulation of a matrix sparsity approach for the quantum ordered search algorithm (2017)
  10. Albin, Nathan; Klarmann, Joshua: An algorithmic exploration of the existence of high-order summation by parts operators with diagonal norm (2016)
  11. Diamond, Steven; Boyd, Stephen: CVXPY: a Python-embedded modeling language for convex optimization (2016)
  12. Doran, Gary; Ray, Soumya: Multiple-instance learning from distributions (2016)
  13. Shakeri, Heman; Poggi-Corradini, Pietro; Scoglio, Caterina; Albin, Nathan: Generalized network measures based on modulus of families of walks (2016)
  14. Sturm, Kevin: Shape optimization with nonsmooth cost functions: from theory to numerics (2016)
  15. Van Cleve, Jeremy: Cooperation, conformity, and the coevolutionary problem of trait associations (2016)
  16. Bogolubsky, L. I.; Raigorodskii, A. M.: On the measurable chromatic number of a space of dimension $n \leq 24$ (2015)
  17. Bonettini, S.; Chiuso, A.; Prato, M.: A scaled gradient projection method for Bayesian learning in dynamical systems (2015)
  18. David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosic, Stephen Boyd, Jure Leskovec: SnapVX: A Network-Based Convex Optimization Solver (2015) arXiv
  19. Li, Li: Selected applications of convex optimization (2015)
  20. Birch, Elsa W.; Udell, Madeleine; Covert, Markus W.: Incorporation of flexible objectives and time-linked simulation with flux balance analysis (2014)

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