# Examples¶

These examples show many different ways to use CVXPY.

- The Basic Examples section shows how to solve some common optimization problems in CVXPY.
- The Machine Learning section is a tutorial covering convex methods in machine learning.
- The Advanced and Advanced Applications sections contains more complex examples aimed at experts in convex optimization.
- The Disciplined Geometric Programming section contains an interactive tutorial on disciplined geometric programming and various examples of DGP problems.

## Basic Examples¶

- Least squares [.ipynb]
- Linear program [.ipynb]
- Quadratic program [.ipynb]
- Second-order cone program [.ipynb]
- Semidefinite program [.ipynb]
- Mixed-integer quadratic program [.ipynb]
- Control
- Portfolio optimization
- Worst-case risk analysis
- Model fitting
- Optimal advertising
- Total variation in-painting [.ipynb]

## Machine Learning¶

## Advanced¶

## Advanced Applications¶

- Allocating interdiction effort to catch a smuggler [.ipynb]
- Antenna array design [.ipynb]
- Channel capacity [.ipynb]
- Computing a sparse solution of a set of linear inequalities [.ipynb]
- Entropy maximization [.ipynb]
- Fault detection [.ipynb]
- Filter design [.ipynb]
- Fitting censored data [.ipynb]
- L1 trend filtering [.ipynb]
- Nonnegative matrix factorization [.ipynb]
- Optimal parade route [.ipynb]
- Optimal power and bandwidth allocation in a Gaussian broadcast channel [.ipynb]
- Power assignment in a wireless communication system [.ipynb]
- Predicting NBA game wins [.ipynb]
- Robust Kalman filtering for vehicle tracking [.ipynb]
- Sizing of clock meshes [.ipynb]
- Sparse covariance estimation for Gaussian variables [.ipynb]
- Water filling [.ipynb]