This lecture introduces Simulated Annealing (SA) and the physical background from thermodynamics, statistical mechanics, and information theory needed to understand its function. After describing a general outline of SA, we pivot to discussing entropy (in terms of microstates and macrostates) and why systems move toward macrostates (distributions of microstates) with higher entropy. We link this to bias in stochastic modeling, which sets us up to start discussing Maximum Entropy (MaxEnt) methods in the next lecture. We will eventually bring this discussion back to Simulated Annealing after briefly discussing both MaxEnt Methods and Markov Chain Monte Carlo (MCMC) methods first (starting in the next lecture).
Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/dabgveu9swzj6d4dcveza/IEE598-Lecture5A-2025-03-18-Introduction_to_Simulated_Annealing_and_Entropy-Notes.pdf?rlkey=3dhycmnzij3fdd3fejes1wlvk&dl=0
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