Fifteen weeks, weighted toward the part that is actually hard. Weeks 1–5, model construction: turning a plant problem into algebra that says what you meant. Week 6: drafting models with AI assistance, and proving them right or wrong. Weeks 7–13, theory and methods: what solvers do, why they behave as they do, and where data-driven methods beat mathematical programming.
25 multiple-choice questions on modeling and interpretation, with no optimization algorithms. Each new attempt shuffles the question order and the choices. Check your score and explanations after submitting. Practice results stay in your browser and are not submitted to the instructor.
12 October 2026 · Version A. The original questions and all four answer choices, followed by the correct answer and a detailed explanation for each of the 30 questions.
40 slides with diagrams and worked solutions, covering inequality constraints, KKT conditions, Hessian eigenvalues, and curvature. Includes a 25-minute narrated video and a PowerPoint with embedded audio.
Study convex and nonconvex geometry, gradient descent, Newton steps, and numerical result checks. Allow three hours including practice and the Python lab.
Open the complete learning package →
Four video chapters · NLP Exit Ticket · NLP feedback · Announcement
60 slides with English narration · Narrated PowerPoint · Slide PDF · Python lab · Exercises · Worked solutions
The instructor will be away. Complete the three-hour guided lesson on integer decisions, binary modeling, branch-and-bound, and capacity selection.
Open the MILP learning hub and five narrated videos →
Slides with audio · Slide PDF · Python lab · Exercises · Worked solutions
23 slides with English male narration, about 13 minutes. Step-by-step explanations of dual variables, primal–dual optimality, and the shadow-price range. Includes the corrected production decision when b₁ = 6 and a practice question with a worked solution.
Open the narrated review · PowerPoint with audio · PDF · Read the announcement
100 slides with English male neural narration (about 45 minutes), a 13-step guided lab, a 9-minute code demonstration, an executable Notebook, and independent exercises with answers.
Listen to the slides · Start the guided lab · Watch the demonstration · Download student pack
26 October 2026 · Three-hour class. 100 slides covering LP geometry, basic feasible solutions, Simplex, primal and dual problems, complementary slackness, the connection to KKT, shadow prices, and sensitivity analysis. Includes explanatory figures, worked examples, practice solutions, and an exit ticket with answers.
70 slides covering local and global optima, Hessian eigenvalues, Lagrange multipliers, and the four KKT conditions. Includes a three-step explanation of gradients and Lagrange multipliers, geometric illustrations, worked examples, practice solutions, and QR codes for the exit ticket and class feedback.
5 October 2026. A three-hour session: 60 minutes on LLM-assisted formulation, followed by 120 minutes of examination preparation including a break. Detailed blending and supplier-contract problems, Pyomo examples, and a 40-minute mock examination. This complete deck includes worked solutions and instructor notes.
These open in the browser — no download, and they work on a phone. Press F for full screen, E for a printable layout, ? for every shortcut.
Predict, explore a checked optimization case, and explain the decision. The interactive companion connects both Quarto volumes.
Read the software setup sheet before Week 2. Everything else you can pick up as it becomes relevant.
Workbooks and notebooks for the hands-on half of each session. The workbooks are the student version: the data and the layout are there, and the formulas are yours to write.
| # | Session | Date | Hands-on | Files |
|---|---|---|---|---|
| 1 | Introduction, the optimization workflow, and the tool stack Orientation |
10 Aug | Excel Solver | 3 |
| 2 | Modeling I: linear models, product mix, multiperiod planning and inventory Model construction |
17 Aug | OpenSolver | 5 |
| 3 | Modeling II: networks, flows and process superstructures Model construction |
24 Aug | OpenSolver | 5 |
| 4 | Modeling III: blending, pooling and quality specifications Model construction |
31 Aug | Excel, then Python | 6 |
| 5 | Modeling IV: logical and discrete decisions Model construction |
7 Sep | OpenSolver and Pyomo | 4 |
| 6 | AI-assisted modeling and model debugging Tools and verification |
14 Sep | Pyomo · workshop | — |
| 7 | Linear programming theory and solver behaviour Theory and algorithms |
21 Sep | Python | — |
| 8 | Duality and sensitivity analysis Theory and algorithms |
28 Sep | Excel and Pyomo | — |
| 9 | Unconstrained nonlinear programming Theory and algorithms |
5 Oct | Python | — |
| 10 | Constrained nonlinear programming Theory and algorithms |
12 Oct | Pyomo and Ipopt | — |
| 11 | Integer and mixed-integer programming Theory and algorithms |
19 Oct | Pyomo and CBC | — |
| 12 | Optimization under uncertainty Advanced topics |
26 Oct | Pyomo | — |
| 13 | Data-driven and AI-based optimization Advanced topics |
2 Nov | Python | — |
| 14 | Project presentations, part 1 Synthesis |
9 Nov | — | — |
| 15 | Project presentations, part 2, and wrap-up Synthesis |
16 Nov | — | — |
| Weight | Component | What it asks |
|---|---|---|
| 35% | Final examination | Cumulative, weighted to Weeks 9 to 13 — nonlinear and integer programming, uncertainty, data-driven methods |
| 25% | Midterm examination | Weeks 1 to 8, weighted to model construction. Includes a model-verification question. No nonlinear programming |
| 15% | Assignments | Four problem sets — model construction, LP and duality, nonlinear programming, integer programming |
| 15% | Computational project | A real optimization problem of your own, modelled in Pyomo, with a test suite and a presentation |
| 10% | Participation and quizzes | In-class activities, the weekly notebook, short quizzes |