离散图形模型--一个optimization透视图
Discrete graphical models -- an optimization perspective
摘要：
这本专著是关于离散图模型的离散能量最小化。它认为图形模型，或更准确地说，最大后验推理的图形模型，纯粹作为一个组合最优化问题。建模、应用、概率解释和许多其他方面要么被忽略，要么只在示例和注释中找到它们的位置。它涵盖了整数线性规划制定的问题，以及它的线性规划，拉格朗日和拉格朗日分解为基础的松弛。特别是，它提供了一个详细的分析多项式可解的无环和次模问题，以及相应的精确最优化方法。对主要的近似方法，如消息传递和图切割技术进行了全面的描述和分析。专着可以是有用的本科生和研究生学习优化或图形模型，以及专家在优化谁想要看看图形模型。为了使专着适合这两类读者，我们明确分离的数学优化的背景章节，从那些特定的图形模型。
英文摘要：
This monograph is about discrete energy minimization for discrete graphical models. It considers graphical models, or, more precisely, maximum a posteriori inference for graphical models, purely as a combinatorial optimization problem. Modeling, applications, probabilistic interpretations and many other aspects are either ignored here or find their place in examples and remarks only. It covers the integer linear programming formulation of the problem as well as its linear programming, Lagrange and Lagrange decomposition-based relaxations. In particular, it provides a detailed analysis of the polynomially solvable acyclic and submodular problems, along with the corresponding exact optimization methods. Major approximate methods, such as message passing and graph cut techniques are also described and analyzed comprehensively. The monograph can be useful for undergraduate and graduate students studying optimization or graphical models, as well as for experts in optimization who want to have a look into graphical models. To make the monograph suitable for both categories of readers we explicitly separate the mathematical optimization background chapters from those specific to graphical models.
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。
