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Department of Information Technology

Applied Optimisation

Working on bringing the science of doing better to real-life applications.

About the Arena

The applied optimisation arena at the Department of Information Technology serves as a platform to enable researchers from multiple divisions of the department to collaborate and network. The arena deals with theories, models, and methods for formulating and solving optimisation problems that arise in a wide spectrum of applications.

The purpose of the arena is:

  • to strengthen the research by bringing together knowledge in optimisation and various application domains.
  • to identify optimisation problems of relevance and develop problem-solving techniques.
  • to pursue synergy effects and added value in research where optimisation is a significant component.

The arena is coordinated by the Optimisation group at the division of Computing Science

Upcoming Events


  • Prof. Gabor Fodor gives the seminar "Receiver Design and Power Control in Multiple-Input Multiple-Output Systems" on Monday 7 October 2019, in ITC 1245, from 10.15 to ~11.00. Abstract: The performance of multiple-input multiple-output systems depends critically on the quality of the acquired channel state information (CSI), the structure of the receiver at the base station and the interference level on the received pilot and data signals. At this seminar we consider the problem of receiver design and setting the pilot and data transmit power in multi-cell systems in the presence of CSI errors, intercell interference and pilot contamination. To analyze and address this problem, we first develop a multi-user receiver that minimizes the mean squared error of the data symbols in the presence of CSI errors. We derive a closed-form expression for the mean squared error and an implicit expression for the average signal-to-interference-plus-noise ratio using random matrix theoretic arguments. Building on this result, we then propose two decentralized power control algorithms based on game theoretic approaches that are applicable in multi-cell systems. We find that both algorithms converge to a Nash equilibrium and provide performance improvements over systems that use prior-art receivers or do not exercise proper power control.
  • Lei You defends his PhD dissertation "Network Optimization of Evolving Mobile Systems with Presence of Interference Coupling" on Monday 7 October 2019, in ITC 1211, at 13.15.
  • Filip Malmberg gave the seminar "Optimization of Max-Norm Objective Functions in Image Processing and Computer Vision" on 25 September 2019.
  • Ghafour Ahani gave his halftime seminar "Optimal scheduling of data and data flows" on 25 September 2019.
  • Niklas Handin from the Department of Pharmacy gave the seminar "A proteomics based deconvolution algorithm for quantification of different cell types" on 29 January 2019.


  • Prashant Singh gave the seminar "Data efficient model driven black-box optimization" on 19 October 2018.
  • André Grce of Netonomics gave the seminar "Optimizing transmission investment" on 22 March 2018.
  • Arie Koster from RWTH Aachen University (Germany) gave the mini-PhD course "Discrete Optimization under Uncertainty" in February 2018. The course description, lecture hours, and information of examination can be found here.
  • Arie Koster from RWTH Aachen University (Germany) gave the seminar "Solving Mixed-Integer Non-Linear Programs by Adaptive Discretization: Two Case Studies" on 27 February 2018. The seminar was on the use of iterative discretization and mixed-integer linear programming for solving very hard non-linear discrete problems. The approach was illustrated by two case studies in decentralized energy system planning and wastewater network design.
  • Armin Biere (Johannes Kepler University, Austria] gave the seminar "Using Computer Algebra to Verify Arithmetic Circuits" on 24 January 2018.
  • Andreas Westerlund from Jeppesen Systems AB gave the seminar "Column generation for airline crew rostering: practical considerations in a production system" on 25 January 2018.


  • Ghafour Ahani gave a seminar on his current research work on 23 November 2017. Seminar title: Cost-Optimal Caching for D2D Networks with Presence of User Mobility.
  • Lei You gave his halftime PhD seminar on 8 November 2017. Seminar title: Modeling and Solving Some Resource Optimization Problems in 4G and 5G networks.
  • The NordConsNet Workshop 2017 of The Nordic Network for researchers and practitioners of Constraint programming was organised by us in Uppsala on 22 May 2017.
  • Pierre Flener gave the seminar Solving Discrete Optimisation Problems Without Knowing How on 5 April 2017.

Research Interests

Division of Computing Science
The Optimisation Group addresses practical applications and the following research topics in optimisation:

  • Models and methods for fundamental capacity characterisation and optimisation for information and communication technology and networks.
  • Large-scale optimisation for transportation systems and logistics, and applications in biology, medicine, and healthcare.
  • Improved inference for constraints on integer timeseries, and inference for constraints on decision variables of string type.
  • High-level language for specifying local-search heuristics as annotations to declarative constraint-based models, and extension of our back-box local-search backend to the MiniZinc language to support search annotations, string variables, and string constraints.

Division of Computer Systems

  • SAT/SMT techniques for analysis, synthesis, and repair of programs or models. This includes the development of new solvers in this area, in particular for data-types like floats, bit-vectors, and strings, and considering extensions like interpolation and fixed-point solving.
  • Optimisation problems in sensing and communication in Internet of Things (IoT), including incentive allocation in mobile crowdsourcing, coordination of stationary and mobile sensors in sensing and communication, etc.
  • Optimisation techniques for smart-city applications and city planning. 

Division of Scientific Computing

  • Parameter estimation and likelihood maximisation in Bayesian inference with (ordinary or partial) differential equation modelling. The models call for simplification to be included in an optimisation loop of solving repeating equations.
  • Form and topology optimisation with partial differential equations (PDE) as constraints, and PDE-constrained optimisation problem in general with many control variables (with applications within geophysics).
  • Convex and non-convex optimization for phase retrieval and 3D alignment in flash X-ray imaging.
  • Modified hidden Markov models with “parameter flipping”, Markov chain Monte Carlo and deep learning approaches for modelling haplotype and genome structure in humans, animals, and plants.
  • General issues regarding numerical accuracy in iterative optimization schemes with a high number of parameters.

Division of Systems and Control

  • Estimation of parameters in linear/nonlinear, static/dynamic, models, giving rise to convex or nonconvex optimisation problems.
  • Formulating real-world problems as tractable optimisation problems that can be solve within a reasonable time frame, and developing fast application-specific minimisation methods for nonlinear problems.
  • General continuous convex optimisation: linear programming (LP), quadratic programming (QP), semi-definite programming (SDP), second-order cone programming (SOCP).
  • The target applications are machine learning, system identification, automatic control, Markov chain and sequential Monte Carlo, network inference and control, target tracing, filter design, beam forming and array processing, spectral analysis, etc.

Visual Information and Interaction

  • TBA

Relevant Courses

  • Algorithms and Data Structures III (1DL481, 5 credits) is taught every spring term. The course includes introductory material on combinatorial optimisation: mixed integer linear programming (MIP), local search (LS), Boolean satisfaction (SAT), and SAT modulo theories (SMT).
  • Optimisation (1TD184, 5 credits) is taught every autumn term. The course covers mathematical modelling and formulation, and basic concepts and methods in optimisation, including MIP.

Contact us

The Applied Optimisation Arena represents a networking effort at the Department of Information Technology and is situated at the Information Technology Center (ITC) in Uppsala, Sweden.

Arena Coordinator
Di Yuan (Computing Science Division)
Marcus Björk (Division of Systems and Control)
Pierre Flener (Computing Science Division)
Ken Mattsson (Division of Scientific Computing)
Edith Ngai (Division of Computer Systems)
Senior Researchers
PhD Students


  • The arena is a site member of SOAF, the Swedish Operations Research Association.
  • Some researchers are members of NordConsNet, the Nordic Network for researchers and practitioners of Constraint programming, a Special Interest Group of SAIS, the Swedish Artificial Intelligence Society.
  • Many are members of CIM, the Centre for Interdisciplinary Mathematics at Uppsala University.

Contact the Department of Information Technology

Updated  2020-11-12 12:53:52 by Di Yuan.