Covers the implementation and analysis of disjoint sets data structure and introduces the concept of minimum spanning trees.
Explores stochastic softmax tricks, reparametrization, and argmax, addressing challenges in expectation estimation and gradient variance.
Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Explores Two-Stage Stochastic Programs, problem reformulation, Benders decomposition, extreme points, and sensitivity analysis.
