Title: | Dimensional Analysis and Its Applications in Statistics |
Speaker: | Prof. Dennis K.J. LIN, Department of Statistics, The Pennsylvania State University, USA |
Time/Place: | 11:00 - 12:00 FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University |
Abstract: | Dimensional Analysis (DA) is a fundamental method in the engineering and physical sciences for analytically reducing the number of experimental variables prior to the experimentation. The principle use of dimensional analysis is to reduce from a study of the dimensions of the variables on the form of any possible relationship between those variables. The method is of great generality. In this talk, an overview/introduction of DA will be first given. A basic guideline for applying DA will be proposed, using examples for illustration. Some initial ideas on using DA for Data Analysis and Data Collection will be discussed. Future research issues will be proposed. |
Title: | Speeding Up ALS for Canonical Tensor Decomposition Using Nonlinear Krylov Methods and Multigrid |
Speaker: | Prof. Hans De Sterck, University of Waterloo, Canada |
Time/Place: | 14:30 - 15:30 FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University |
Abstract: | The Alternating Least Squares (ALS) method is the standard workhorse algorithm for computing the best rank-K approximation of a data tensor. This canonical rank-K tensor decomposition is widely used in a variety of application areas that include chemometrics, signal processing, neuroscience, and social network analysis. ALS can be interpreted as a block nonlinear Gauss-Seidel (GS) method for the canonical tensor optimization problem. Just like GS for linear systems, ALS can be fast for certain tensor problems, but it may converge prohibitively slowly for other problems. In the case of linear systems, it is well-known that GS convergence can be accelerated using Krylov methods like Conjugate Gradients (CG) or GMRES, or using multigrid. Inspired by the linear case, we develop nonlinear optimization algorithms for canonical tensor decomposition that can dramatically accelerate ALS convergence using nonlinear extensions of CG, GMRES and multigrid. |
We organize conferences and workshops every year. Hope we can see you in future.
Learn MoreProf. M. Cheng, Dr. Y. S. Hon, Dr. K. F. Lam, Prof. L. Ling, Dr. T. Tong and Prof. L. Zhu have been awarded research grants by Hong Kong Research Grant Council (RGC) — congratulations!
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