Loading...

Lecture 12 | Convex Optimization II (Stanford)

7,763 views

Loading...

Loading...

Transcript

The interactive transcript could not be loaded.

Loading...

Loading...

Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Uploaded on Jul 9, 2008

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd finishes his talk on Sequential Convex Programming and begins a lecture on Conjugate Gradient Methods.

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...

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

  • License

    • Standard YouTube License

Loading...

When autoplay is enabled, a suggested video will automatically play next.

Up next


to add this to Watch Later

Add to

Loading playlists...