Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Explores the convexity of Lovász extension and submodular function maximization, focusing on extending functions to convex sets and proving their convexity.
Explores optimization techniques like Metropolis-Hastings and Simulated Annealing through Markov chains and stationary distributions.
