Averaging algorithms and distributed optimization
published: Jan. 13, 2011, recorded: December 2010, views: 1123
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In distributed averaging and consensus algorithms, processors exchange and update certain values (or "estimates", or "opinions") by forming a local average with the values of their neighbors. Under suitable conditions, such algorithms converge to consensus (every processor ends up holding the same value) or even average-consensus (consensus is achieved on the average of the initial values held by the processors). Algorithms of this type have been proposed as a subroutine of distributed optimization methods, used to combine the results of different processors while a master algorithm is running. We overview a few applications of averaging algorithms, with a focus on gradient-like optimization methods. We then proceed to highlight some results, old and new, with a focus on convergence rates. We finally discuss some open problems.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !