Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Covers the formulation and problem definition in optimization, focusing on defining the objective function, constraints, and feasible set.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores optimization techniques like Metropolis-Hastings and Simulated Annealing through Markov chains and stationary distributions.
Covers the general framework of optimization and simulation, including deterministic control and the optimization problem.
Covers the Branch & Bound algorithm for efficient exploration of feasible solutions and discusses LP relaxation, portfolio optimization, Nonlinear Programming, and various optimization problems.
Explores greedy heuristics in optimization, integrality constraints, and comparison of optimization methods.
