Optimization (OPT)

To optimize is to find the best possible solution to a problem. Optimization is a mathematical science and is based on mathematical models that define what is good/bad and what is feasible/infeasible. The best solution to such a model is found with the help of efficient solutions methods, based on mathematical algorithms and efficient implementations. This way one can solve real life problems within many different areas, such as telecommunication, forestry, railway, finance, transportation, production etcetera.

Show/Hide content

Show/Hide content

Latest publications



Per Berglund, Per Dannetun, Wai Lee Chan, Julie Gold, Sam Han, Heidi Hansson, Simon Harvey, Jun Song Huang, Ann-Charlotte Larsson, Steven Linton, Gerald McInerney, Marie Magnell, Oleg Popov, Nils-Hassan Quttineh, Tobias Richards, Juha Song, Adam D. Switzer, Kristina Tegler Jerselius, Susanne Vikström, Martin Wikström, Kang Yang Trevor Yu, Jesvin Puay-Hwa Yeo, Nabil Zary, Hans Pohl, Ulf Ellervik (2019) Linking Education and Research: A Roadmap for Higher Education Institutions at the Dawn of the Knowledge Society

Show/Hide content

Show/Hide content

Undergraduate courses

Most of the engineering programmes and the mathematical programme take at least one required course in optimization.

The courses include modeling, theory, and solution methods of optimization problems in mainly the areas linear, nonlinear, network and integer optimization. We also give a number of supplementary courses which include advanced modeling, Lagrangean relaxation, subgradient optimization, column generation, decomposition methods, heuristics, and meta heuristics.

Link to our undergraduate courses at Optimization

Doctoral studies

Show/Hide content

Contact us
Show/Hide content

Department and campus
Show/Hide content