论文标题
有效的ADMM和分裂方法，用于连续的最低点和最大流量问题
Efficient ADMM and Splitting Methods for Continuous Min-cut and Max-flow Problems
论文作者
论文摘要
Potts模型有许多应用程序。它等同于某些最小和最大流量模型。原始二重式算法已用于解决这些问题。由于模型的特殊结构，融合证明仍然是一个困难的问题。在这项工作中，我们开发了两种新颖的，预处理的和过度删除的乘数交替方向方法（ADMM），并为这些模型提供了收敛保证。使用所提出的预处理或块预处理，我们获得了使用预处理AMDM的过度删除变体的加速度。 Potts模型还考虑了预处理和过度删除的Douglas-Rachford分裂方法。我们的框架可以根据Eckstein-Bertsekas和Fortin-Glowinski分裂技术来处理适当的块预处理的两标签或多标签问题。
The Potts model has many applications. It is equivalent to some min-cut and max-flow models. Primal-dual algorithms have been used to solve these problems. Due to the special structure of the models, convergence proof is still a difficult problem. In this work, we developed two novel, preconditioned, and over-relaxed alternating direction methods of multipliers (ADMM) with convergence guarantee for these models. Using the proposed preconditioners or block preconditioners, we get accelerations with the over-relaxation variants of preconditioned ADMM. The preconditioned and over-relaxed Douglas-Rachford splitting methods are also considered for the Potts model. Our framework can handle both the two-labeling or multi-labeling problems with appropriate block preconditioners based on Eckstein-Bertsekas and Fortin-Glowinski splitting techniques.
