40.302 Advanced Topics in Optimisation
This course introduces you to convex optimisation, a powerful optimisation model with effective algorithms and strong guarantees of finding the best solution. Convex optimisation is widely used in fields like engineering and machine learning, where making accurate decisions is important. We will cover modelling techniques, explore duality, and learn about sensitivity analysis, which helps us see how changes in parameters affect solutions. In the course we will also cover basic algorithms needed to solve convex optimisation problems.
Learning objectives
At the end of the term, students will be able to:
- Use optimisation techniques as a decision-making tool in scheduling applications.
- Model optimisation problems using algebraic modelling languages and spreadsheets.
- Solve moderate-sized yet practical optimization problems that are not simple enough to be solved by hand using techniques such as integer programming, heuristics, deterministic and stochastic dynamic optimization.
Measurable outcomes
- Formulate practical optimisation problems in the scheduling domain that effectively tradeoff realism with tractability
- Use an algebraic modelling language to solve scheduling problems using the tool of integer programming
- Identify and develop appropriate methodologies to solve optimisation problems in scheduling
Prerequisites
Course instructor
Number of credits: 6