In this lecture, we continue our march toward the basic algorithm for simulated annealing (a popular optimization metaheuristic). We start with the Metropolis algorithm, which was one of the first Markov Chain Monte Carlo approaches for numerical integration. We then generalize the Metropolis algorithm to the Metropolis–Hastings algorithm, which replaces the Boltzmann distribution with any desired probability distribution to sample from. That gives us an opportunity to talk about Markov Chain Monte Carlo (MCMC) in general and discuss its pros and cons. We then start to introduce the simulated annealing algorithm, which will make use of the Metropolis algorithm. We will finish off simulated annealing (SA) in the next lecture.
Whiteboard notes for this lecture can be found at: https://www.dropbox.com/s/84upgxenjkmxgld/IEE598-Lecture5C-2022-03-01-From_MCMC_Sampling_to_Opt_by_Simulated_Annealing.pdf?dl=0
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