Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Covers optimization methods, focusing on gradient methods and line search techniques.
Covers unconstrained and constrained optimization, optimal control, neural networks, and global optimization methods.
Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
