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 )

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  1. Carvalho, Rui; Buzna, Lubos; Gibbens, Richard; Kelly, Frank: Critical behaviour in charging of electric vehicles (2015)
  2. David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosic, Stephen Boyd, Jure Leskovec: SnapVX: A Network-Based Convex Optimization Solver (2015) arXiv
  3. Li, Li: Selected applications of convex optimization (2015)
  4. Birch, Elsa W.; Udell, Madeleine; Covert, Markus W.: Incorporation of flexible objectives and time-linked simulation with flux balance analysis (2014)
  5. Doran, Gary; Ray, Soumya: A theoretical and empirical analysis of support vector machine methods for multiple-instance classification (2014)
  6. Müller, Andreas C.; Behnke, Sven: Pystruct-learning structured prediction in Python (2014)
  7. Xanthopoulos, Petros; Guarracino, Mario R.; Pardalos, Panos M.: Robust generalized eigenvalue classifier with ellipsoidal uncertainty (2014)
  8. Alwan, Aravind; Aluru, N. R.: Improved statistical models for limited datasets in uncertainty quantification using stochastic collocation (2013)
  9. Andersen, Martin S.; Dahl, Joachim; Vandenberghe, Lieven: Logarithmic barriers for sparse matrix cones (2013)
  10. Auslender, Alfred: A very simple SQCQP method for a class of smooth convex constrained minimization problems with nice convergence results (2013)
  11. Ermon, Stefano; Xue, Yexiang; Gomes, Carla; Selman, Bart: Learning policies for battery usage optimization in electric vehicles (2013) ioport
  12. Monajemi, Hatef; Jafarpour, Sina; Gavish, Matan; Stat 330/CME 362 Collaboration; Donoho, David L.: Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices (2013)
  13. Chiu, Edmond Kwan-Yu; Wang, Qiqi; Hu, Rui; Jameson, Antony: A conservative mesh-free scheme and generalized framework for conservation laws (2012)
  14. Mattingley, Jacob; Boyd, Stephen: CVXGEN: a code generator for embedded convex optimization (2012)
  15. Perez, Ruben E.; Jansen, Peter W.; Martins, Joaquim R. R. A.: PyOpt: a python-based object-oriented framework for nonlinear constrained optimization (2012)
  16. Ruotsalainen, Lauri; Vuorinen, Matti: Numerical methods with Sage (2012)
  17. Andersen, Martin S.; Dahl, Joachim; Vandenberghe, Lieven: Implementation of nonsymmetric interior-point methods for linear optimization over sparse matrix cones (2010)
  18. Auslender, Alfred; Shefi, Ron; Teboulle, Marc: A moving balls approximation method for a class of smooth constrained minimization problems (2010)
  19. Buriol, Luciana S.; Hirsch, Michael J.; Pardalos, Panos M.; Querido, Tania; Resende, Mauricio G. C.; Ritt, Marcus: A biased random-key genetic algorithm for road congestion minimization (2010)
  20. Liu, Zhang; Vandenberghe, Lieven: Interior-point method for nuclear norm approximation with application to system identification (2010)