?ュ??瀛﹁??锛?Michiel E. Hochstenbach

?ュ??????浣?锛?Department of Mathematics and Computer Science, Eindhoven University of Technology

?ュ???堕?达?2024骞?1??21?ワ??ㄥ??锛?10:00-11:30

?ュ???扮?癸?7215浼?璁??

?ュ????瑕?锛?We propose two new algebraic reconstruction techniques based on Kaczmarz's method that produce a regularized solution to noisy tomography problems. Tomography problems exhibit semiconvergence when iterative methods are employed, and the aim is therefore to stop near the semiconvergence point. Our approach is based on an error gauge that is constructed by pairing standard down-sweep Kaczmarz's method with its up-sweep version; we stop the iterations when this error gauge is minimal. The reconstructions of the new methods differ from standard Kaczmarz iterates in that our final result is the average of the stopped up- and down-sweeps. Even when Kaczmarz's method is supplied with an oracle that provides the exact error---and is therefore able to stop at the best possible iterate---our methods have a lower two-norm error in the vast majority of our test cases. In terms of computational cost, our methods are a little cheaper than standard Kaczmarz equipped with a statistical stopping rule.

?ュ??瀛﹁??绠?浠?锛?Michiel E. Hochstenbach锛??峰?板??????娓╃??宸ュぇ瀛﹀??????锛???澹???瀵煎????ㄨ?峰?颁?寰?????ぇ瀛﹀?????峰?纭?澹?????澹??浣?锛?2003骞村?ㄥ痉?芥??濉?灏?澶?澶?ぇ瀛︿?浜???澹?????绌讹??句换缇??藉????タ?ㄥぇ瀛︾???╃????????姣??╂?跺?椴?濉?灏?澶у???浼???瀹㈠骇?╃????????Michiel E. Hochstenbach?????????介???????板?涓?瀹讹??ㄣ??SIAM Journal on Scientific Computing??????Journal of Scientific Computing??????SIAM Journal on Matrix Analysis and Applications??绛??介???板?涓?搴??ㄦ?板??ュ??????涓???琛ㄥ??????60浣?绡???