Linear optimization modeling
Classic LP model families (allocation, blending, operations planning, shift scheduling) and how to implement them in AMPL from Python. Weeks 3–4
Integer optimization
Fixed charges, capital budgeting, set covering, facility location, network design and scheduling; LP relaxations, branch and bound, and solver logs. Weeks 5–6
Network flow models
Minimum cost network flow, max flow, shortest path, transportation, assignment and matching; total unimodularity and integrality. Weeks 7–8
Lecture notes
Labs open in Colab
Advanced LP and IP models
Traveling salesman and vehicle routing formulations, and the optimization models behind regression, SVMs, clustering and classification trees. Weeks 11–12
Optimization under uncertainty
Two-stage stochastic programs, VSS and EVPI, chance constraints, robust optimization and stochastic integer programs. Week 13
Labs open in Colab
Large-scale methods
Column generation, Dantzig–Wolfe and Benders decomposition, and Lagrangian relaxation. Week 14
Lecture notes
Labs open in Colab
Advanced topics and next steps
Bilevel optimization, and a closing lecture on where to go after this course. Week 15
In-class exercises
Short warm-up problems from class. Try them before opening a solver.