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 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
- 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