Syllabus

The complete list of the chapters of the lab, in the order in which they are covered. Every chapter has its own page, its own Python script and its own notebook that opens in Colab.

Tools

1. Theory: linear programming and nonlinear optimization — duality, shadow prices, KKT, sensitivity protocol
2. Solver, linear models and nonlinear — building the model, running it, retrieving the solution, interpreting the output

Deterministic models

1. Multi-period production and inventory — LP/QP
2. Supply chain with congestion and CO₂ — LP/NLP
3. The Markowitz portfolio — QP
4. Pricing and revenue management — NLP
5. Advertising budget — convex NLP
6. Continuous location — convex NLP
7. Electric vehicle charging — LP/QP
8. Queues and service capacity — convex NLP

Decisions under uncertainty

1. The Newsvendor and its variants — stochastic LP
2. VaR and CVaR — scenario-based LP
3. Arbitrage and pricing — LP and duality that prices

Optimization and machine learning

1. Support Vector Machine — QP
2. Robust and quantile regression — LP

The course

1. Organization of the lab — lab sessions and mistakes to avoid

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The four parts and what each one is for are described on the home page.