You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
You must be logged in to perform this action.
- Author:
-
Stephen Boyd
- Subject:
- Mathematics and Statistics, Science and Technology
- Institution Name:
- Stanford University
- Collection:
-
Stanford University - School of Engineering
- Grade Level:
- Post-secondary
- Abstract:
Continuation of Convex Optimization I. 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. Course requirements include a substantial project.
- Languages:
- English
- Material Type:
- Audio Lectures, Full Course, Lecture Notes, Video Lectures
- Media Format:
- Audio, Text/HTML, Downloadable docs, Video
- Conditions of Use:
-
Creative Commons Attribution 3.0
- Copyright Holder:
- Stanford University
No restrictions on your remixing, redistributing, or making derivative works.
Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some
restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make
derivative works.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based
educators, or other custom arrangements. Go to the resource provider to see
their individual restrictions.
Comments