Event(s) on March 2023

• Thursday, 16th March, 2023

  Title:	Parallel-in-time preconditioners for the Sinc-Nyström systems
  Speaker:	Dr Jun Liu, Department of Mathematics and Statistics, Southern Illinois University Edwardsville, USA
  Time/Place:	11:00  -  12:00 Zoom, (Meeting ID: 965 2761 3109)
  Abstract:	The sinc-Nyström method is a high-order numerical method in time for evolutionary differential equations. But it needs to solve all the time steps in one shot (i.e., all-at-once), which results in a large-scale structured nonsymmetric dense system. In this talk, we propose and analyze parallel-in-time preconditioners for such dense systems arising from both the parabolic and hyperbolic PDEs. The proposed preconditioner is a low-rank perturbation of the original matrix and it has two advantages. First, we show that the eigenvalues of the preconditioned system are highly clustered with some uniform bounds which are independent of the mesh parameters. Second, the preconditioner can be computed in parallel for all the sinc time points via a block diagonalization procedure. The effectiveness of our proposed parallel-in-time preconditioners is illustrated by several linear and nonlinear numerical examples.

  
  

• Tuesday, 21st March, 2023

  Title:	A Synthetic Regression Model for Large Portfolio Allocation
  Speaker:	Professor Wenyang Zhang, Department of Mathematics, University of York, UK
  Time/Place:	16:00  -  17:00 FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
  Abstract:	Portfolio allocation is an important topic in financial data analysis. In this talk, based on the mean-variance optimization principle, I will present a synthetic regression model for construction of portfolio allocation, and an easy to implement approach to generate the synthetic sample for the model. Compared with the regression approach in existing literature for portfolio allocation, the proposed method of generating the synthetic sample provides more accurate approximation for the synthetic response variable when the number of assets under consideration is large. Due to the embedded leave-one-out idea, the synthetic sample generated by the proposed method has weaker within sample correlation, which makes the resulting portfolio allocation more close to the optimal one. I will show this intuitive conclusion is theoretically confirmed to be true by the asymptotic properties established. I will also show intensive simulation studies to compare the proposed method with the existing ones, and illustrate the proposed method works better. Finally, I will apply the proposed method to real data sets, and show very encouraging yielded returns.

  
  

• Wednesday, 29th March, 2023

  Title:	Mathematics Applications in Geophysical Exploration
  Speaker:	Dr. Joseph Ma, Halliburton, Singapore
  Time/Place:	12:00  -  13:00 FSC1110, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
  Abstract:	The exploration of planet Earth for underground resources is reliant on geophysical methods. From advances in theoretical development, computational techniques, geophysical data processing, imaging, and inversion, mathematics has been serving as the fundamental driving force enabling the emergence of new techniques and technologies towards geophysical exploration. In this seminar, the speaker will present several industrial examples on how mathematics is applied to overcome engineering and environmental constraints. And eventually becoming the key components for generating more efficient, more robust, and less noisy images for better interpreting the subsurface characteristics.

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