COMPLEXITY OF RANDOM SMOOTH FUNCTIONS ON THE HIGH-DIMENSIONAL SPHERE

• 成果类型: Article
• 署名作者: Auffinger, Antonio; Ben Arous, Gerard
• 署名单位: University of Chicago; New York University
• 刊物名称: ANNALS OF PROBABILITY
• ISSN/ISSBN: 0091-1798
• DOI: 10.1214/13-AOP862
• 发表日期: 2013
• 页码: 4214-4247
• 关键词: random matrices

摘要

We analyze the landscape of general smooth Gaussian functions on the sphere in dimension N, when N is large. We give an explicit formula for the asymptotic complexity of the mean number of critical points of finite and diverging index at any level of energy and for the mean Euler characteristic of level sets. We then find two possible scenarios for the bottom landscape, one that has a layered structure of critical values and a strong correlation between indexes and critical values and another where even at levels below the limiting ground state energy the mean number of local minima is exponentially large. We end the paper by discussing how these results can be interpreted in the language of spin glasses models.