In this article we continue our investigation of the iterative regularizationmethod for optimization problems based on Bregman distances. The optimizationproblems are subject to pointwise inequality constraints in $L^2(\Omega)$. Weprovide an estimate for the noise error for perturbed data, which can be usedto construct an a priori stopping rule. Furthermore we show how to implementour method with a semi-smooth Newton method using finite elements and presentnumerical results for the stopping rule.
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机译：在本文中，我们继续研究基于Bregman距离的优化问题的迭代正则化方法。优化问题受制于$ L ^ 2（\ Omega）$中的逐点不等式约束。我们提供了对扰动数据的噪声误差的估计，该估计可用于构建先验停止规则。此外，我们还展示了如何使用半光滑牛顿法（使用有限元和停止规则的数值结果）来实现我们的方法。
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