Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
