arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 0802.2831cs.CCcs.GT
收藏

平衡、不动点和复杂性类

Equilibria, Fixed Points, and Complexity Classes

Mihalis Yannakakis

摘要:

来自不同领域的许多模型都涉及到某种平衡点或不动点的计算。例子包括游戏中的纳什均衡;市场均衡;计算最优策略和竞争游戏(随机和其他游戏)的值;神经网络的稳定配置;分析基本的随机模型,如分支过程和语言,如随机上下文无关文法;以及模型,包括概率和递归的基本原语,如递归马尔可夫链。这些问题是否能在多项式时间内解决还不知道。不同类型的均衡背后有一些共同的计算原则,这些原则被复杂性类PLS、PPAD和FIXP捕获。这些类的代表完整的问题分别是,纯纳什均衡的游戏,他们保证存在,(混合)纳什均衡2-玩家范式游戏,(混合)纳什均衡在范式游戏与3(或更多)的球员。本文回顾了基本的计算原理和相应的类。

英文摘要:

Many models from a variety of areas involve the computation of an equilibrium or fixed point of some kind. Examples include Nash equilibria in games; market equilibria; computing optimal strategies and the values of competitive games (stochastic and other games); stable configurations of neural networks; analysing basic stochastic models for evolution like branching processes and for language like stochastic context-free grammars; and models that incorporate the basic primitives of probability and recursion like recursive Markov chains. It is not known whether these problems can be solved in polynomial time. There are certain common computational principles underlying different types of equilibria, which are captured by the complexity classes PLS, PPAD, and FIXP. Representative complete problems for these classes are respectively, pure Nash equilibria in games where they are guaranteed to exist, (mixed) Nash equilibria in 2-player normal form games, and (mixed) Nash equilibria in normal form games with 3 (or more) players. This paper reviews the underlying computational principles and the corresponding classes.

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。

↑