Covers the Metropolis-Hastings and Glauber algorithms for sampling from the Boltzmann distribution in the Curie-Weiss model.
Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.
Covers the theory and practical aspects of Monte Carlo simulations in molecular dynamics, including ensemble averages and Metropolis algorithm.
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 dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.
Explores sampling the canonical ensemble, temperature fluctuations, extended Lagrangian, and Maxwell-Boltzmann distribution in molecular dynamics simulations.
Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.
