Learning Reduced Order Dynamics via Geometric Representations
Imran Nasim, Melanie Weber
SCML 2024
We study a class of methods for accelerating the convergence of iterative methods for solving linear systems. The methods proceed by replacing the given linear system with a derived one of smaller size, the aggregated system. The solution of the latter is used to accelerate the original iterative process. The construction of the aggregated system as well as the passage of information between it and the original system depends on one or more approximations of the solution of the latter. A number of variants are introduced, estimates of the acceleration are obtained, and numerical experiments are performed. The theory and computations show the methods to be effective. © 1980.
Imran Nasim, Melanie Weber
SCML 2024
Hang-Yip Liu, Steffen Schulze, et al.
Proceedings of SPIE - The International Society for Optical Engineering
Michael Ray, Yves C. Martin
Proceedings of SPIE - The International Society for Optical Engineering
Robert Manson Sawko, Malgorzata Zimon
SIAM/ASA JUQ