贝叶斯与克罗内克：用于欠定系统预处理的CGLS方法
Bayes meets Krylov: preconditioning CGLS for underdetermined systems
AI总结：
本文研究了在贝叶斯框架下如何利用先验信息通过右预处理改进CGLS方法，从而更紧密地连接贝叶斯推断与克罗内克子空间方法，解决欠定线性逆问题。
摘要：
本文研究了在贝叶斯框架下如何利用先验信息通过右预处理改进CGLS方法，从而更紧密地连接贝叶斯推断与克罗内克子空间方法，解决欠定线性逆问题。
英文摘要：
The solution of linear inverse problems when the unknown parameters outnumber data requires addressing the problem of a nontrivial null space. After restating the problem within the Bayesian framework, a priori information about the unknown can be utilized for determining the null space contribution to the solution. More specifically, if the solution of the associated linear system is computed by the Conjugate Gradient for Least Squares (CGLS) method, the additional information can be encoded in the form of a right preconditioner. In this paper we study how the right preconditioned changes the Krylov subspaces where the CGLS iterates live, and draw a tighter connection between Bayesian inference and Krylov subspace methods. The advantages of a Krylov-meet-Bayes approach to the solution of underdetermined linear inverse problems is illustrated with computed examples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
