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 Disciplined geometric programming section shows how to solve log-log convex programs.

• The Disciplined quasiconvex programming section has examples on quasiconvex programming.

• The Derivatives section shows how to compute sensitivity analyses and gradients of solutions.

There are also application-specific sections.

• The Basic applications section shows real-world applications of common optimization problems.

• The Machine learning section is a tutorial on convex optimization in machine learning.

• The Advanced and Advanced applications sections contains more complex examples for experts in convex optimization.

Most of these examples are implemented as Jupyter notebooks, while some are implemented as interactive marimo notebooks.

Basic examples¶

For geometric interpretations of linear and quadratic programs, see the marimo links.

• Least squares [.ipynb] [marimo]

• Linear program [.ipynb] [marimo]

• Quadratic program [.ipynb] [marimo]

• Semidefinite program [.ipynb] [marimo]

• Disciplined convex programming

• Mixed-integer quadratic program [.ipynb]

Basic applications¶

• Minimum-fuel optimal control

• Markowitz portfolio optimization

• More control

• Beyond Markowitz portoflio optimization

• Worst-case risk analysis

• Model fitting

• Optimal advertising

• Total variation in-painting [.ipynb]

Disciplined geometric programming¶

• DGP fundamentals [.ipynb]

• Maximizing the volume of a box [.ipynb]

• Power control [.ipynb]

• Perron-Frobenius matrix completion [.ipynb]

• Rank-one nonnegative matrix factorization [.ipynb]

Disciplined quasiconvex programming¶

• Concave fractional function [.ipynb]

• Minimum-length least squares [.ipynb]

• Hypersonic shape design [.ipynb]

Derivatives¶

• Fundamentals [.ipynb]

• Queuing design [.ipynb]

• Structured prediction [.ipynb]

Machine learning¶

• Ridge regression [.ipynb]

• Lasso regression [.ipynb]

• Logistic regression [.ipynb]

• SVM classifier [.ipynb]

• Huber regression

• Quantile regression

Finance¶

• Portfolio optimization

• Cryptocurrency trading

• Entropic Portfolio Optimization

• Portfolio Optimization using SOC constraints

• Gini Mean Difference Portfolio Optimization

• Kurtosis Portfolio Optimization

• Relativistic Value at Risk Portfolio Optimization

• Approximate Kurtosis Portfolio Optimization

Advanced¶

• Second-order cone program [.ipynb]

• Object-oriented convex optimization [.ipynb]

• Consensus optimization [.ipynb]

• Method of multipliers [.ipynb]

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]

• Multiple Traveling Salesman Problem

• Minimize Condition Number by Scaling [.ipynb]