Well-posedness and Convergence Rates for Sparse Regularization with Sublinear l q Penalty Term
Well-posedness and Convergence Rates for Sparse Regularization with Sublinear l q Penalty Term
复制标题
具有次线性 l q 惩罚项的稀疏正则化的适定性和收敛率
DOI：
--
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
M. Grasmair
中科院分区：
文献类型：
--
作者：
M. Grasmair
本文讨论了非凸次线性惩罚项的应用
This paper deals with the application of non-convex, sublinear penalty terms
to the regularization of possibly non-linear inverse problems
the solutions of which are assumed to have a sparse expansion with respect
to some given basis or frame.
It is shown that this type of regularization
is well-posed and yields sparse results.
Moreover, linear convergence rates are derived under the additional
assumption of a certain range condition.