Iterative regularization for constrained minimization formulations of nonlinear inverse problems
研究了将反问题表述为约束最小化问题，并用梯度或牛顿型方法迭代求解，分析了收敛性，并应用于椭圆偏微分方程中空间变化扩散系数的识别，包括阻抗声学断层扫描的数值实验。
In this paper we study the formulation of inverse problems as constrained minimization problems and their iterative solution by gradient or Newton type methods. We carry out a convergence analysis in the sense of regularization methods and discuss applicability to the problem of identifying the spatially varying diffusivity in an elliptic PDE from different sets of observations. Among these is a novel hybrid imaging technology known as impedance acoustic tomography, for which we provide numerical experiments.