To refer to this page use:
|Abstract:||We consider large-scale linear inverse problems with a simulation-based algorithm that approximates the solution within a low-dimensional subspace. The algorithm uses Tikhonov regularization, regression, and low-dimensional linear algebra calculations and storage. For sampling efficiency, we implement importance sampling schemes, specially tailored to the structure of inverse problems. We emphasize various alternative methods for approximating the optimal sampling distribution and we demonstrate their impact on the reduction of simulation noise. The performance of our algorithm is tested on a practical inverse problem arising from Fredholm integral equations of the first kind.|
|Citation:||Polydorides, Nick, Mengdi Wang, and Dimitri P. Bertsekas. "A Quasi Monte Carlo Method for Large-Scale Inverse Problems." In Monte Carlo and Quasi-Monte Carlo Methods 2010. Springer Proceedings in Mathematics & Statistics, vol 23 (2012): 623-637. doi:10.1007/978-3-642-27440-4_36|
|Pages:||623 - 637|
|Type of Material:||Book Chapter|
|Journal/Proceeding Title:||Springer Proceedings in Mathematics and Statistics|
|Notes:||In Monte Carlo and Quasi-Monte Carlo Methods 2010. Springer Proceedings in Mathematics & Statistics, vol 23 (2012)|
Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.