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.
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Covers optimization methods, focusing on gradient methods and line search techniques.
Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
