Explore the optimization of the Sherrington-Kirkpatrick Hamiltonian in this 23-minute IEEE conference talk by Andrea Montanari. Delve into key concepts such as the Stochastic Block Model, Adjacency Matrix, and the Sherrington-Kirkpatrick Model and Theorem. Examine random graphs, formulas, and assumptions while gaining insights into the geometric interpretation of the problem. Learn about algorithm structures, including two crucial insights on orthogonality and optimization techniques, to enhance your understanding of this complex topic in statistical physics and machine learning.
Optimization of the Sherrington-Kirkpatrick Hamiltonian
Overview
Syllabus
Introduction
Stochastic Block Model
Adjacency Matrix
SherringtonKirkpatrick Model
SherringtonKirkpatrick
Theorem
Random Graphs
Formula
Assumption
Geometric Interpretation
Algorithm
Algorithms
Algorithm structure
Two insights
Orthogonality
Optimization
Taught by
IEEE FOCS: Foundations of Computer Science
