WASP at Department of Mathematics (MAI)

About WASP at MAI

One of the research environments connected to WASP - Wallenberg AI, Autonomous Systems and Software Program at Linköping University (LiU), is located at the Department of Mathematics (MAI) on Campus Valla in Linköping. 

This page is about WASP Mathematics. You can read about our two research groups:

  • Mathematics and algorithms for intelligent decision-making
  • Optimisation for machine learning

Mathematics and algorithms for intelligent decision-making

There are many benefits of using mathematics and algorithms to aid decision-making in complex settings. Our main focus is on discrete optimisation for decision problems that aim to schedule or allocate resources. In our research projects, we both find new ways to address decision problems by optimisation and develop new methods for solving them.

Artificial intelligence

Thanks to digitalisation, the available amount of data to support decision-making has reached a high level of maturity, and so has the capability of processing this data. Alongside the data science disciplines, optimisation — used for decision support — plays a key role in the further development of tools to support advanced decision-making. The scale and complexity of the problems relevant to address continue to increase and this calls for research that address the mathematics and algorithms needed in optimisation methods.

Intelligent decision-making

We believe that truly intelligent decision-making is achieved through an integration of model-based and data-driven approaches that are designed or used in interaction with a human decision-maker. To successfully apply mathematics and algorithms as part of real-world decision-making requires careful mathematical modelling and data collection. An inherent property of many decision problems of practical relevance is that they are computationally challenging. Solving such a problem within a reasonable amount of time often requires the development of specialised methods that exploit the mathematical structure of the problem.

On the journey towards sustainability, our contribution is to develop mathematical models and solution methods for practically relevant but computationally challenging problems in scheduling and resource allocation.

Discrete optimisation

There are many types of decision problems that aim to schedule or allocate resources, and these lend themselves to be addressed as discrete optimisation problems. Most practically relevant discrete optimisation problems are NP-hard, which means that their worst-case solution times grow exponentially with the problem size. The practical consequence of this is that, for challenging instances, even the state-of-the-art optimisation solvers will frequently fail to deliver a solution within weeks or even thousands of years of computational time.

Over the last decades, there has been an impressive development of methods for solving discrete optimisation problems. Thanks to this, many important planning and scheduling problems can be solved with a reasonable computational effort—however, many practically relevant problems still pose great challenges.


This research direction, formerly called Operations Research Methods for Scheduling and Resource Allocation Problems has been established through funding from the Center for Industrial Information Technology (CENIIT) and is carried out at the Division of Applied Mathematics (TIMA) in the Department of Mathematics (MAI). Leader of the group is Elina Rönnberg.

Some of our applied projects and PhD thesis projects are highlighted in the list of research projects below.


Research projects

Ongoing projects

Previous projects

Optimisation for machine learning

Our group focuses on developing more efficient algorithms for machine learning.

Continuous optimization plays a central role in modern machine learning. Every learning process requires solving a high-dimensional minimization problem. The faster we solve it, the fewer resources (e.g., energy, time, or money) we waste.

Current state-of-the-art algorithms include some heuristics and often require a lot of tuning. The theoretical justification of these heuristics and the developing of new, more efficient algorithms is the main focus of our group.