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 26 articles )

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  1. Friedlander, Michael P.; Goh, Gabriel: Efficient evaluation of scaled proximal operators (2017)
  2. Li, Jinchao; Andersen, Martin S.; Vandenberghe, Lieven: Inexact proximal Newton methods for self-concordant functions (2017)
  3. Diamond, Steven; Boyd, Stephen: CVXPY: a Python-embedded modeling language for convex optimization (2016)
  4. Doran, Gary; Ray, Soumya: Multiple-instance learning from distributions (2016)
  5. Shakeri, Heman; Poggi-Corradini, Pietro; Scoglio, Caterina; Albin, Nathan: Generalized network measures based on modulus of families of walks (2016)
  6. Sturm, Kevin: Shape optimization with nonsmooth cost functions: from theory to numerics (2016)
  7. Van Cleve, Jeremy: Cooperation, conformity, and the coevolutionary problem of trait associations (2016)
  8. Bogolubsky, L.I.; Raigorodskii, A.M.: On the measurable chromatic number of a space of dimension $n \leq 24$ (2015)
  9. Bonettini, S.; Chiuso, A.; Prato, M.: A scaled gradient projection method for Bayesian learning in dynamical systems (2015)
  10. David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosic, Stephen Boyd, Jure Leskovec: SnapVX: A Network-Based Convex Optimization Solver (2015) arXiv
  11. Li, Li: Selected applications of convex optimization (2015)
  12. Birch, Elsa W.; Udell, Madeleine; Covert, Markus W.: Incorporation of flexible objectives and time-linked simulation with flux balance analysis (2014)
  13. Doran, Gary; Ray, Soumya: A theoretical and empirical analysis of support vector machine methods for multiple-instance classification (2014)
  14. Müller, Andreas C.; Behnke, Sven: Pystruct-learning structured prediction in Python (2014)
  15. Xanthopoulos, Petros; Guarracino, Mario R.; Pardalos, Panos M.: Robust generalized eigenvalue classifier with ellipsoidal uncertainty (2014)
  16. Alwan, Aravind; Aluru, N.R.: Improved statistical models for limited datasets in uncertainty quantification using stochastic collocation (2013)
  17. Ermon, Stefano; Xue, Yexiang; Gomes, Carla; Selman, Bart: Learning policies for battery usage optimization in electric vehicles (2013) ioport
  18. Chiu, Edmond Kwan-Yu; Wang, Qiqi; Hu, Rui; Jameson, Antony: A conservative mesh-free scheme and generalized framework for conservation laws (2012)
  19. Mattingley, Jacob; Boyd, Stephen: CVXGEN: a code generator for embedded convex optimization (2012)
  20. Ruotsalainen, Lauri; Vuorinen, Matti: Numerical methods with Sage (2012)

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