论文标题
基于共识的鞍点问题优化
Consensus-Based Optimization for Saddle Point Problems
论文作者
论文摘要
在本文中，我们提出了基于共识的鞍点问题（CBO-SP）的优化，这是一种新型的多粒子元映射无衍生物的优化方法，能够证明可以发现全局nash均衡。遵循群体智能的想法，该方法采用了一组相互作用的粒子，这些粒子对一个变量进行最小化，而另一个变量对另一个变量进行最大化。该范式允许通过平均场限制进行通过，这使得该方法可符合理论分析，并允许在对初始化和目标函数的合理假设下获得严格的融合保证，最著名的是Nonconvex-Nonconcave目标。
In this paper, we propose consensus-based optimization for saddle point problems (CBO-SP), a novel multi-particle metaheuristic derivative-free optimization method capable of provably finding global Nash equilibria. Following the idea of swarm intelligence, the method employs a group of interacting particles, which perform a minimization over one variable and a maximization over the other. This paradigm permits a passage to the mean-field limit, which makes the method amenable to theoretical analysis and allows to obtain rigorous convergence guarantees under reasonable assumptions about the initialization and the objective function, which most notably include nonconvex-nonconcave objectives.
