Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
(CS 528)

Optimization over Linear Matrix Inequalities

Stephen Boyd
Stanford Electrical Engineering
November 1, 2004, 4:15PM
TCSeq 200


The recent development of efficient interior-point algorithms for convex optimization problems involving linear matrix inequalities (LMIs) has spurred research in a wide variety of application fields, including control system analysis and synthesis, combinatorial optimization, circuit design, structural optimization, experiment design, and geometrical problems involving ellipsoidal bounding and approximation.

In the first part of the talk, I will describe the basic problems, semidefinite programming (SDP) and determinant maximization, discuss their basic properties, and give a brief description of interior-point methods for their solution. In the second half of the talk I will survey applications from several areas.

Joint work with Lieven Vandenberghe.

About the Speaker


Back to the Colloquium Page