Schedule

Two lectures and one lab a week (offered at two overlapping times), plus a drop-in session. Both lab sessions cover the same content: if you clash with one, join the other; there's no need to sign up.

Monday: 13:00 Drop-in
Tuesday: 11:00 Lecture A; 15:00 Lab A
Thursday: 9:00 Lecture B; 15:00 Lab B

Lectures cover the theory, with some problem-solving sessions modelling and coding optimisation problems live. Drop-ins are for targeted questions and feedback. Labs are where you practice with one-on-one tutor support: they're built around each topic's problem set, with solutions pushed to the repository and presented in-lab about a week later.

Bring assignment or lecture questions to labs too; tutors will clarify confusion and help build your understanding, though they won't give strong hints on assignments. If you haven't kept up with lectures, don't expect labs to re-cover that material for you.

Week by week

Preliminary, and may change based on student feedback. Slides are posted before each lecture and recordings after, both on the course's GitLab.

Weekly topics
Week	Dates	Lecture A	Lecture B	Lab
1	27 Jul – 31 Jul	Course overview and introduction	LP: modelling	None
2	3 Aug – 7 Aug	LP: feasibility and optimality	LP: simplex	LP: Q1, 11, 12
3	10 Aug – 14 Aug	LP: approximations and duality	MIP: relaxation and modelling	Drop-in
4	17 Aug – 21 Aug	MIP: branch and bound	MIP: cutting planes	MIP: Q2, 4, 7
5	24 Aug – 28 Aug	Decomposition: column generation	Decomposition: Benders	Drop-in
6	31 Aug – 4 Sep	Convexity	TBD	None
7	21 Sep – 25 Sep	Convex optimisation and Lagrangians	Interior point methods	Lab: decomposition
8	28 Sep – 2 Oct	Construction heuristics	Local search	CVX: Q2, 5, 8
9	5 Oct – 9 Oct	Metaheuristics I	Metaheuristics II	None
10	12 Oct – 16 Oct	Stochastic optimisation	Multi-objective optimisation	Lab: metaheuristics
11	19 Oct – 23 Oct	Network flow	Path planning	Drop-in
12	26 Oct – 30 Oct	Guest lecture	TBD	None

Full assessment due dates live on the Assessment page.