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.
