Lecture 16 | Convex Optimization II (Stanford)

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd introduces a new topic- Model Predictive Control.

This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=3940DD956CDF0622

EE364B Course Website:
http://www.stanford.edu/class/ee364b/

Stanford University:
http://www.stanford.edu

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

Category:

Education

Tags:

• Math
• Technology
• Algebra
• calculus
• geometry
• electrical
• engineering
• convex
• optimization
• subgradient
• derivatives
• basic
• inequality
• functions
• algorithms
• linear
• optimal
• control
• finite
• horizon
• approximation
• greedy
• model
• pre