懒惰的翻转器：高阶图模型中深度受限穷举搜索的MAP推理
The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search
摘要：
本文提出了一种新的搜索算法，用于优化根据图模型分解的二元变量函数的NP-难问题。它可以应用于任何顺序和结构的模型。主要的新颖性是一种技术，以限制搜索空间的基础上的拓扑结构的模型。当追求到全搜索深度时，该算法保证收敛到全局最优值，通过一系列单调改进的局部最优值，这些局部最优值保证在给定且增加的汉明距离内是最优的。如果搜索深度为1，则它专用于迭代条件模式。在这些极端情况之间，建立了近似质量和运行时间之间的有用权衡。来自说明性和真实问题的模型上的实验表明，用有限的搜索深度找到的近似匹配或改进了基于消息传递和线性规划的最先进方法所获得的近似。
英文摘要：
This article presents a new search algorithm for the NP-hard problem of optimizing functions of binary variables that decompose according to a graphical model. It can be applied to models of any order and structure. The main novelty is a technique to constrain the search space based on the topology of the model. When pursued to the full search depth, the algorithm is guaranteed to converge to a global optimum, passing through a series of monotonously improving local optima that are guaranteed to be optimal within a given and increasing Hamming distance. For a search depth of 1, it specializes to Iterated Conditional Modes. Between these extremes, a useful tradeoff between approximation quality and runtime is established. Experiments on models derived from both illustrative and real problems show that approximations found with limited search depth match or improve those obtained by state-of-the-art methods based on message passing and linear programming.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
