Events
Department of Mathematics and Statistics
Texas Tech University
 | Wednesday
Jan. 19
|
| Algebra and Number Theory
No Seminar
|
Two main challenges to design efficient iterative solvers for the frequency-domain Maxwell equations are the indefinite nature of the underlying system and the high resolution requirements. Scalable parallel frequency-domain Maxwell solvers are highly desired.
This talk will introduce the EM-WaveHoltz method which is an extension of the recently developed WaveHoltz method for the Helmholtz equation to the time-harmonic Maxwell equations. Three main advantages of the proposed method are as follows. (1) It always results in a positive definite linear system. (2) Based on the framework of EM-WaveHoltz, it is flexible and simple to build efficient frequency-domain solvers from current scalable time-domain solvers. (3) It is possible to obtain solutions for multiple frequencies in one solve. The formulation of the EM-WaveHoltz and analysis in the continuous setting for the energy conserving case will be discussed. The performance of the proposed method will be demonstrated through numerical experiments.
Please virtually attend the Applied Math seminar via this Zoom link Wednesday the 19th at 4 PM -- meeting ID: 937 2431 1192
Algorithmic trading requires the trading machines to consider the reasonably
expected near term order flows (i.e., RENTOF) in trading.
To manage the price impact and transaction cost associated with large orders
submitted to an order driven market,
trading machine must determine their trading styles (aggressive, neutral, or passive)
based on the market liquidity in response to RENTOF, particularly for trading a large size of stocks.
In this work we introduce an adaptive learning algorithm for considering different trading styles
to satisfy the predictive near-term market liquidity
(i.e., order flows) with respect to an optimal order submission strategy based on
different market situations.
We show some analytical properties and numerical performances of our algorithm
in search of optimal solutions. We evaluate the performances of our algorithm with simulations
run over a set of experiments in comparison with two alternative strategies.
Our results suggest that the proposed algorithmic trading strategy illustrates superiority in performance.