Examples¶
Tutorial examples¶
Short examples that illustrate basic features of CVXOPT.
Book examples¶
Examples from the book Convex Optimization by Boyd and Vandenberghe.
- Optimal trade-off curve for a regularized least-squares problem (fig. 4.11)
- Risk-return trade-off (fig. 4.12)
- Penalty function approximation (fig. 6.2)
- Robust regression (fig. 6.5)
- Input design (fig. 6.6)
- Sparse regressor selection (fig. 6.7)
- Quadratic smoothing (fig. 6.8-6.10)
- Total variation reconstruction (fig. 6.11-6.14)
- Stochastic and worst-case robust approximation (fig. 6.15-6.16)
- Polynomial and spline fitting (fig. 6.19-6.20)
- Basis pursuit (fig 6.21-6.23)
- Least-squares fit of a convex function (fig. 6.24)
- Consumer preference analysis (fig. 6.25-6.26)
- Logistic regression (fig. 7.1)
- Maximum entropy distribution (fig. 7.2-7.3)
- Chebyshev bounds (fig. 7.6-7.7)
- Chernoff lower bound (fig. 7.8)
- Experiment design (fig. 7.9-7.11)
- Ellipsoidal approximations (fig. 8.3-8.4)
- Centers of polyhedra (fig. 8.5-8.7)
- Approximate linear discrimination (fig. 8.10-8.12)
- Linear, quadratic, and fourth-order placement (fig. 8.15-8.17)
- Floor planning example (fig. 8.20)
Custom interior-point solvers¶
Examples from the book chapter Interior-point methods for large-scale cone programming (pdf) by M. S. Andersen, J. Dahl, Z. Liu, L. Vandenberghe; in: S. Sra, S. Nowozin, S. J. Wright (Editors) Optimization for Machine Learning, MIT Press, 2011.
The code for nuclear norm approximation can be found here.
Utility functions¶
Useful Python scripts that are not included in the distribution.
Generating random sparse matrices (
sprandmtrx.py)Reading and writing Matlab mat-files (
matfile.py; Python 2.7 only)