论文标题
DWIFOB：单调包含物的动态加权惯性前向算法
DWIFOB: A Dynamically Weighted Inertial Forward-Backward Algorithm for Monotone Inclusions
论文作者
论文摘要
我们提出了一种动态加权的惯性前向算法（DWIFOB），用于解决结构化单调包含问题。该方案利用[26]中的偏差利用了全球收敛的前向算法，并将其与安德森加速度用于改善局部收敛的外推技术相结合。我们还提出了DWIFOB的全球收敛性原始偶对偶变体，并将其性能与Chambolle-pock的原始偶对偶进行了比较，以及应用于同一问题的Anderson加速度的Tikhonov正则版本。在我们所有的数值评估中，DWIFOB的原始二变体都优于Chambolle-Pock算法。此外，我们的数值实验表明，我们所提出的方法比正规化的安德森加速度更强大，后者可能无法收敛并且对算法参数敏感。这些数值实验强调了我们的方法的性能很好，同时仍然坚固且可靠。
We propose a novel dynamically weighted inertial forward-backward algorithm (DWIFOB) for solving structured monotone inclusion problems. The scheme exploits the globally convergent forward-backward algorithm with deviations in [26] as the basis and combines it with the extrapolation technique used in Anderson acceleration to improve local convergence. We also present a globally convergent primal-dual variant of DWIFOB and numerically compare its performance to the primal-dual method of Chambolle-Pock and a Tikhonov regularized version of Anderson acceleration applied to the same problem. In all our numerical evaluations, the primal-dual variant of DWIFOB outperforms the Chambolle-Pock algorithm. Moreover, our numerical experiments suggest that our proposed method is much more robust than the regularized Anderson acceleration, which can fail to converge and be sensitive to algorithm parameters. These numerical experiments highlight that our method performs very well while still being robust and reliable.
