Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
