# Stanford EE364A: Convex Optimization

## Descriptions

- Offered by: Stanford
- Prerequisites: Python, Calculus, Linear Algebra, Probability Theory, Numerical Analysis
- Programming Languages: Python
- Difficulty: 🌟🌟🌟🌟🌟
- Class Hour: 150 hours

Professor [Stephen Boyd](http://web.stanford.edu/~boyd) is a great expert in the field of convex optimization and his textbook **Convex Optimization** has been adopted by many prestigious universities. His team has also developed a programming framework for solving common convex optimization problems in Python, Julia, and other popular programming languages, and its homework assignments also use this programming framework to solve real-life convex optimization problems.

In practice, you will deeply understand that for the same problem, a small change in the modeling process can make a world of difference in the difficulty of solving the equation. It is an art to make the equations you formulate "convex".

## Course Resources

- Course Website: <http://stanford.edu/class/ee364a/index.html>
- Recordings: <https://www.youtube.com/watch?v=VNON98dKjno&list=PLoCMsyE1cvdXeoqd1hGaMBsCAQQ6otUtO>
- Textbook: [Convex Optimization](https://stanford.edu/~boyd/cvxbook/)
- Assignments: refer to the course website

## Personal Resources

All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPic/Standford_CVX101 - GitHub](https://github.com/PKUFlyingPig/Standford_CVX101)