论文标题
黑森西亚估计进化策略的收敛分析
Convergence Analysis of the Hessian Estimation Evolution Strategy
论文作者
论文摘要
称为Hessian估计进化策略（HE-ESS）的算法类别通过直接估计目标函数的曲率来更新其采样分布的协方差矩阵。这种方法实际上是有效的，即使在功能相当不规则的功能上，在BBOB测试中的可观性能证明了这一方法。 在本文中，我们正式证明了（1+4）-HE的两种有力的保证，这是该家族的最小精英成员：协方差矩阵更新的稳定性，因此，在所有凸率二次问题上，以与问题相关的情况，所有凸二次问题的线性收敛。
The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function. The approach is practically efficient, as attested by respectable performance on the BBOB testbed, even on rather irregular functions. In this paper we formally prove two strong guarantees for the (1+4)-HE-ES, a minimal elitist member of the family: stability of the covariance matrix update, and as a consequence, linear convergence on all convex quadratic problems at a rate that is independent of the problem instance.
