.. _examples:

Examples
========

These examples show many different ways to use CVXPY. The :ref:`basic` section shows how to solve some common optimization problems in CVXPY. The :ref:`advanced-python` and :ref:`applications` sections contains more complex examples aimed at experts in convex optimization.

.. _basic:

Basic Examples
--------------

- `Total variation in-painting <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/tv_inpainting.ipynb>`_

- `Control <http://nbviewer.ipython.org/github/cvxgrp/cvx_short_course/blob/master/intro/control.ipynb>`_

- `SVM classifier with regularization <http://nbviewer.ipython.org/github/cvxgrp/cvx_short_course/blob/master/intro/SVM.ipynb>`_

- `Portfolio optimization <http://nbviewer.ipython.org/github/cvxgrp/cvx_short_course/blob/master/applications/portfolio_optimization.ipynb>`_

- `Worst-case risk analysis <http://nbviewer.ipython.org/github/cvxgrp/cvx_short_course/blob/master/applications/worst_case_analysis.ipynb>`_

- `Optimal advertising <http://nbviewer.ipython.org/github/cvxgrp/cvx_short_course/blob/master/applications/optimal_advertising.ipynb>`_

- `Huber regression <http://nbviewer.ipython.org/github/cvxgrp/cvx_short_course/blob/master/applications/huber_regression.ipynb>`_

- `Quantile regression <http://nbviewer.ipython.org/github/cvxgrp/cvx_short_course/blob/master/applications/quantile_regression.ipynb>`_

- `Model fitting <http://nbviewer.ipython.org/github/cvxgrp/cvx_short_course/blob/master/applications/model_fitting.ipynb>`_

.. _advanced-python:

Advanced Examples
-----------------

- `Object-oriented convex optimization <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/OOCO.ipynb>`_

- `Consensus optimization <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/consensus_opt.ipynb>`_

- `Method of multipliers <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/MM.ipynb>`_

.. _applications:

Advanced Applications
---------------------

- `Allocating interdiction effort to catch a smuggler <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/interdiction.ipynb>`_
- `Antenna array design <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/ant_array_min_beamwidth.ipynb>`_
- `Channel capacity <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/Channel_capacity_BV4.57.ipynb>`_
- `Computing a sparse solution of a set of linear inequalities <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/sparse_solution.ipynb>`_
- `Entropy maximization <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/max_entropy.ipynb>`_
- `Fault detection <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/fault_detection.ipynb>`_
- `Filter design <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/fir_chebychev_design.ipynb>`_
- `Fitting censored data <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/censored_data.ipynb>`_
- `L1 trend filtering <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/l1_trend_filter.ipynb>`_
- `Nonnegative matrix factorization <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/nonneg_matrix_fact.ipynb>`_
- `Optimal parade route <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/parade_route.ipynb>`_
- `Optimal power and bandwidth allocation in a Gaussian broadcast channel <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/optimal_power_gaussian_channel_BV4.62.ipynb>`_
- `Power assignment in a wireless communication system <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/maximise_minimum_SINR_BV4.20.ipynb>`_
- `Predicting NBA game wins <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/nba_ranking.ipynb>`_
- `Robust Kalman filtering for vehicle tracking <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/robust_kalman.ipynb>`_
- `Sizing of clock meshes <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/clock_mesh.ipynb>`_
- `Sparse covariance estimation for Gaussian variables <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/sparse_covariance_est.ipynb>`_
- `Water filling <http://nbviewer.ipython.org/github/cvxgrp/cvxpy/blob/master/examples/notebooks/WWW/water_filling_BVex5.2.ipynb>`_