Lecture 13 | Convex Optimization II (Stanford)

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd continues his lecture on Conjugate Gradient Methods and then starts lecturing on the Truncated Newton Method.

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
• function
• algorithms
• trust
• region
• nonlinear
• optimal
• control
• discretization
• SCP
• torque
• residuals
• convex-c