Motivated by industrial computed tomography, we propose a memory efficient strategy to estimate the regularization hyperparameter of a non-smooth variational model. The approach is based on a combination of FISTA and Condat-Vu algorithms exploiting the convergence rate of the former and the low per-iteration complexity of the latter. The estimation is cast as a bilevel learning problem where a first-order method is obtained via reduced-memory automatic differentiation to compute the derivatives. The method is validated with experimental industrial tomographic data with the numerical implementation available.

翻译：受工业计算机断层扫描的启发，本文提出一种内存高效的策略，用于估计非光滑变分模型的正则化超参数。该方法结合了FISTA与Condat-Vu算法，充分利用前者收敛速度快与后者单次迭代复杂度低的优势。超参数估计被构建为双层学习问题，其中通过内存优化的自动微分技术计算导数，从而获得一阶优化方法。该方法在工业断层扫描实验数据上得到验证，相关数值实现已公开。