论文标题
离散能量最小化的双重级坐标上升方法的分类学
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization
论文作者
论文摘要
我们考虑了基于双重座位坐标规则的离散图形模型中的最大A-posteriori推理问题和研究求解器。我们在一个框架中绘制所有现有的求解器，从而更好地了解其设计原理。从理论上讲，我们表明某些大优化的更新是优化的，以及如何严格改进它们。在各种不同的图形连接性的问题实例中，我们研究了现有求解器的性能以及在框架中可以获得的新变体。通过此探索，我们构建了一个新的最先进的求解器，在整个测试实例中表现更好。
We consider the maximum-a-posteriori inference problem in discrete graphical models and study solvers based on the dual block-coordinate ascent rule. We map all existing solvers in a single framework, allowing for a better understanding of their design principles. We theoretically show that some block-optimizing updates are sub-optimal and how to strictly improve them. On a wide range of problem instances of varying graph connectivity, we study the performance of existing solvers as well as new variants that can be obtained within the framework. As a result of this exploration we build a new state-of-the art solver, performing uniformly better on the whole range of test instances.
