从放松、补偿、再恢复的角度看对偶分解
Dual Decomposition from the Perspective of Relax, Compensate and then Recover
摘要：
放松，补偿，然后恢复（RCR）是概率图模型中近似推理的一个范例，它以前提供了关于迭代置信传播及其一些推广的理论和实践见解。在本文中，我们的特点的对偶分解技术的RCR方面，将其视为一种特定的方式来补偿放松的等价约束。在从这个角度收集的其他见解中，我们提出了新的算法来恢复放松的等价约束，目标是逐步收紧对偶分解近似，一直到达到精确解。我们还表明，经验，恢复等价约束有时可以收紧相应的近似（并获得确切的结果），而不会增加太多的推理的复杂性。
英文摘要：
Relax, Compensate and then Recover (RCR) is a paradigm for approximate inference in probabilistic graphical models that has previously provided theoretical and practical insights on iterative belief propagation and some of its generalizations. In this paper, we characterize the technique of dual decomposition in the terms of RCR, viewing it as a specific way to compensate for relaxed equivalence constraints. Among other insights gathered from this perspective, we propose novel heuristics for recovering relaxed equivalence constraints with the goal of incrementally tightening dual decomposition approximations, all the way to reaching exact solutions. We also show empirically that recovering equivalence constraints can sometimes tighten the corresponding approximation (and obtaining exact results), without increasing much the complexity of inference.
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。
