Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores optimization techniques like Metropolis-Hastings and Simulated Annealing through Markov chains and stationary distributions.
Covers optimization and simulation techniques for drawing from multivariate distributions and dealing with correlations.
