Multiplicative Algorithms: Why Do They Converge and How to Make Them Faster
RAND Statistics Seminar Series
Multiplicative Algorithms: Why Do They Converge and How to Make Them Faster
Dr. Yaming Yu—UC Irvine
Thursday, October 13th, 2011
10:30 a.m. – 12:00 p.m. PT / 1:30pm – 3:00pm ET
Conference Room 3312
RAND Corporation, Santa Monica, CA
Please contact Fabiola Lopez if you would like to attend this seminar.
Abstract
Multiplicative algorithms are simple optimization schemes that iteratively adjust the coordinates of an input probability vector by multiplicative factors so as to increase a suitable objective function. Examples include the EM algorithm for maximum likelihood estimation of mixture proportions, multiplicative algorithms for computing approximate optimal designs, and the Arimoto-Blahut algorithm for calculating channel capacities in Shannon theory. By exploiting the connections among these seemingly separate problems we derive general conditions that ensure monotonic convergence for multiplicative algorithms in optimal designs, and construct hybrid algorithms that converge faster but maintain the simplicity and stability.
Speaker Bio
Dr. Yaming Yu is an associate professor in the Department of Statistics at the University of California, Irvine. He received his Ph.D. from the Department of Statistics at Harvard University and his B.S. in Mathematics from Beijing University. His research interests include statistical computing, information theory, Bayesian analysis and applied probability.
Attending a Seminar
Other Locations/Times:
Washington, D.C. Conf. Rm. 4132: 1:30 p.m. ET
Pittsburgh Conf. Rm. 6202: 1:30 p.m. ET
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