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
http://graphics.stanford.edu/ba-colloquium/
Abstract
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
Contact: bac-coordinators@cs.stanford.edu
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