Wei Organized & Chaired Session at 2017 INFORMS Annual Meeting

At the conference, analytics and operations research professionals and academics from around the world will gather to share research and ground breaking ideas.

Prof. Ermin Wei

Prof. Ermin Wei Organized and Chaired a Session on Distributed and Large scale Optimization at the 2017 INFORMS annual meeting, held in Houston, TX on October 22-25, 2017. She also presented a talk on "Asynchronous Distributed Newton Method."

As the largest city in both the south and southwest, Houston is proof that everything in Texas is bigger. Houston is considered the healthcare and energy center of the U.S., as well as home to the hub of human spaceflight, NASA’s Johnson Space Center, which has become synonymous with the now infamous message, “Houston, we have a problem.” Now more than four decades later, INFORMS will be sharing an entirely different message with the city, “Houston, we SOLVE problems,” when analytics and operations research professionals and academics from around the world will gather to share research and ground breaking ideas at the 2017 INFORMS Annual Meeting.

Prof. Wei's Talk Abstract: The problem of minimizing a sum of local convex objective functions over a networked system captures many important applications and has received much attention in the distributed optimization field. Most of existing work focuses on development of distributed algorithms under the presence of a central clock. The only known algorithms with convergence guarantees in asynchronous setup could achieve linear rate. In this work, we built upon existing literature to develop and analyze an asynchronous Newton method. We show that this algorithm converges almost surely and has superlinear rate in expectation. Numerical studies confirm superior performance against other existing asynchronous methods.