A robust alternating direction method of multipliers numerical scheme for solving geometric inverse problems in a shape optimization setting

Abstract

The alternating direction method of multipliers within a shape optimization framework is developed for solving geometric inverse problems, focusing on a cavity identification problem from the perspective of non-destructive testing and evaluation techniques. The rationale behind this method is to achieve more accurate detection of unknown inclusions with pronounced concavities, emphasizing the aspect of shape optimization. Several numerical results to illustrate the applicability and efficiency of the method are presented for various shape detection problems. These numerical experiments are conducted in both two- and three-dimensional settings, with a focus on cases involving noise-contaminated data. The main finding of the study is that the proposed method significantly outperforms conventional shape optimization methods based on first-order optimality conditions in reconstructing unknown cavity shapes. This superior performance is demonstrated through more numerically accurate constructions compared to classical methods.