Selected Topics in Optimization

Objectives: The course aims to give knowledge in the selected optimization topics.

Content: The course deals with optimization theory and algorithms. Exact content wil vary from year to year.

Topic for Autumn 2026: AI based Optimization

The course provides a comprehensive introduction to modern optimization methods used in machine learning, engineering, and decision-support systems. The course covers fundamental optimization concepts, multi-objective modelling, machine learning for optimization, hyperparameter search strategies, and surrogate-assisted optimization for expensive problems. The course also presents advanced and emerging optimization topics, along with real-world case studies in engineering, energy systems, and intelligent decision-making.

On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student

• knows the theory of the selected optimization topics that have been lectured

For Autumn 2026:

Knowledge

The student should be able to

• explain the core principles behind modern optimization methods used in engineering
• describe the theory and practice of multi-objective optimization and Pareto-based reasoning
• understand surrogate modelling techniques for expensive or simulation-based optimization
• understand how ML is applied in optimization.

Skills

The student should be able to

• apply a wide range of optimization methods to solve single-objective and multi-objective problems
• implement multi-objective algorithms and surrogate-assisted optimization pipelines
• design and evaluate hyperparameter optimization strategies, including Bayesian optimization

General competence

• be able to analyse an optimization problem and select appropriate methods for solving it
• understand trade-offs in multi-objective and data-driven decision-making contexts
• evaluate how problem complexity, dimensionality, and cost influence optimization strategy selection
• apply optimization thinking to real-world systems such as supply chains, energy systems, and robotics applications

Irregular

Is held autumn 2026.