Covers optimization and simulation techniques for drawing from multivariate distributions and dealing with correlations.
Explores optimization techniques like Metropolis-Hastings and Simulated Annealing through Markov chains and stationary distributions.
Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.
Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.
Explores Markov chains, their properties, and algorithmic applications, emphasizing information quantification and state monotonicity.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
