Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Covers optimization methods, focusing on gradient methods and line search techniques.
Explores optimization techniques like Metropolis-Hastings and Simulated Annealing through Markov chains and stationary distributions.
Covers optimization and simulation techniques for drawing from multivariate distributions and dealing with correlations.
Discusses algorithms for balancing exploration and exploitation in decision-making processes.
Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.
