Stanford / Engineering / Electrical
Lecture : Example: Piecewise Linear Minimization
By Stephen Boyd | Convex Optimization II
Lecture 7 of 18
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Course Description
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.

Prerequisites: Convex Optimization I
Courses Index
1 : Advanced Topics in Circuit Design   (Elad Alon / Berkeley)
2 : Introduction to Digital Integrated Circuits   (Jan RABAEY / Berkeley)
3 : Advanced Analog Integrated Circuits   (Elad Alon / Berkeley)
4 : Introduction to Microelectronic Circuits   (Bernhard BOSER / Berkeley)
5 : The Fourier Transform and its Applications   (Brad Osgood / Stanford)
6 : Introduction to Linear Dynamical Systems   (Stephen Boyd / Stanford)
7 : Convex Optimization I   (Stephen Boyd / Stanford)
8 : Circuits and Electronics   (Anant Agarwal / MIT)
9 : Computer System Engineering   (Samuel Madden / MIT)
10 : Introduction to Algorithms   (Erik Demaine / MIT)
11 : Principles of Digital Communications I   (Lizhong Zheng / MIT)
12 : Principles of Digital Communication II   (David Forney / MIT)
13 : Understanding Lasers and Fiberoptics   (Shaoul Ezekiel / MIT)
14 : Electromagnetics and Applications   (Multiple Instructors / MIT)
15 : Information and Entropy   (Paul Penfield / MIT)
16 : Fundamentals of Laser   (Sabieh Anwar / LUMS)
17 : Synchrotron Radiation for Materials Science   (David Attwood / Berkeley)
18 : Linear Integrated Circuits   (Clark Nguyen / Berkeley)
19 : Digital Circuit Design   (Ken Boyd / University of New South Wales)
20 : Speech and Audio Processing   (Multiple Multiple / University of New South Wales)