Combinatorial Optimization: Simulated Annealing

This lecture covers the concept of combinatorial optimization, focusing on simulated annealing as a solution method. It explains the process of finding ground states in frustrated systems and the challenges of satisfying all interactions simultaneously. The instructor discusses the application of Monte Carlo algorithms and the importance of equilibrium in the system. The lecture delves into the partition function, free energy, and the self-averaging property in spin glasses models. It also explores the role of frustration and degeneracy in determining the system's behavior, emphasizing the difficulty in identifying the ground state due to identical energy configurations. Various algorithms and models are presented to address these optimization problems.