StanfordOnline: Convex Optimization
This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.
Convex Optimization
At a glance
- Institution: StanfordOnline
- Subject: Engineering
- Level: Advanced
- Prerequisites:
You should have good knowledge of linear algebra and exposure to probability. Exposure to numerical computing, optimization, and application fields is helpful but not required; the applications will be kept basic and simple. You will use matlab and CVX to write simple scripts, so some basic familiarity with matlab is helpful. We will provide some basic Matlab tutorials.
- Language: English
- Video Transcript: English
- Associated skills:Operations Research, Computational Mathematics, Computer Science, Scientific Computing, Mathematical Optimization, Electrical Engineering, Enterprise Desktop Administrator (Microsoft Certified IT Professional), Civil Engineering, Structural Analysis, Political Sciences, Convex Optimization, Signal Processing, Biology, Computational Geometry, Analog Electronics, Mechanical Engineering, Reinforcement Learning, Machine Learning, Stochastic Optimization, Algorithms, Image Processing, Statistics, Finance