日時: 2012年7月6日(金) 16:30〜17:30
会場: 東京大学 大学院数理科学研究科 002号室
S.R.Srinivasa Varadhan 氏 (Courant Institute of Mathematical Sciences, New York University)
Large Deviations of Random Graphs and Random Matrices
A random graph with $n$ vertices is a random symmetric matrix of $0$'s and $1$'s and they share some common aspects in their large deviation behavior. For random matrices it is the question of having large eigenvalues. For random graphs it is having too many or too few subgraph counts, like the number of triangles etc. The question that we will try to answer is what would a random matrix or a random graph conditioned to exhibit such a large deviation look like. Since the randomness is of size $n^2$ large deviation rates of order $n^2$ are possible.