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.
Explores finding the initial Basic Feasible Solution (BFS) in a linear program.
Covers the proof of strong duality in optimization problems and provides examples of Rayleigh quotient optimization.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
