Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.
Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Explores optimization techniques like Metropolis-Hastings and Simulated Annealing through Markov chains and stationary distributions.
Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Covers the Quantum Approximate Optimization Algorithm, physically inspired unitary coupled cluster ansatz, hardware-efficient ansatz, and variational quantum eigensolver.
Introduces the backpack problem, a discrete optimization problem with constraints and algorithms to solve it.
