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Event(s) on May 2014


  • Monday, 12th May, 2014

    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.


  • Friday, 30th May, 2014

    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.