Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Explores optimization techniques like Metropolis-Hastings and Simulated Annealing through Markov chains and stationary distributions.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
