文献类型：会议论文

英文题名：A GRADIENT METHOD FOR REGULARIZING RETRIEVAL OF AEROSOL PARTICLE SIZE DISTRIBUTION FUNCTION

作者：Wang, Yanfei[1];Ma, Qinghua[2]

第一作者：Wang, Yanfei

通讯作者：Wang, YF[1]

机构：[1]Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China;[2]Beijing Union Univ, Coll Art & Sci, Beijing 100083, Peoples R China

第一机构：Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Inst Geol & Geophys, POB 9825, Beijing 100029, Peoples R China.

会议论文集：7th Triannual International Conference on Optimization - Techniques and Applications (ICOTA 7)

会议日期：DEC 12-15, 2007

会议地点：Kobe, JAPAN

语种：英文

外文关键词：Ill-posed aerosol inverse problems; Optimization; Regularization

摘要：The determination of the aerosol particle size distribution function using the particle spectrum extinction equation is an ill-posed integral equation of the first kind ([15, 19]). Even for finite moment case, the problem is still discrete ill-posed, since as is known, in remote sensing the observations are often limited /insufficient or contaminated. To overcome the ill-posedness, various standard or non-standard regularization techniques were developed (see [18] and references therein). However, most of the literature focuses on the application of the Phillips-Twomey's regularization or its variants which is unstable in several cases. Recently in [17], the authors considered Tikhonov's smooth regularization method in W-1,W-2 space for ill-posed inversion. But the method still relies on the choice of the regularization parameter and the a priori estimation of the noise level. In addition, these methods do not consider the nonnegative constraints of the model problem. As is known, the particle size distribution is always nonnegative and we are often faced with incomplete data. Therefore, creation of data to establish well-posedness and development of suitable method are urgently needed. We first present a regularization model which incorporates smoothness constraint to the solution, and then propose an efficient gradient method for solving the regularizing problem. Numerical tests are performed to show the efficiency and feasibility of the proposed algorithms.