Applied Optimisation

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

• Peter J. Stuckey of Monash University (Australia) visits us on 6-13 February 2019 and gives the seminar Multi-Agent Path Finding on Friday 8 February 2019.

• Niklas Handin from the Department of Pharmacy gives a seminar with title "A proteomics based deconvolution algorithm for quantification of different cell types" Tuesday 29 January, in ITC 1345, from 15.30 till ~16.30

• Prashant Singh gives a seminar "Data efficient model driven black-box optimization" on Friday 19 October 2018, in ITC 2414b, from 11.15 to ~12.00.

• André Grce of Netonomics givesa seminar "Optimizing transmission investment" on Thursday 22 March 2018, in ITC 1245, from 13.15 to ~14.30.

• Arie Koster from RWTH Aachen University (Germany) gives a mini-PhD course "Discrete Optimization under Uncertainty" in the last week of February 2018. Course description, lecture hours, and information of examination can be found here.

• Arie Koster from RWTH Aachen University (Germany) gives a seminar "Solving Mixed-Integer Non-Linear Programs by Adaptive Discretization: Two Case Studies" On Tuesday 27 February 2018 in room ITC 1245 from 15.15 to circa 16.15. The seminar will be on the use of iterative discretization and mixed-integer linear programming for solving very hard non-linear discrete problems. The approach is illustrated by two case studies in decentralized energy system planning and wastewater network design.

• Fredrik Ygge of Trade Extensions gives the guest lecture Using Optimisation for Electronic Negotiations on Monday 22 January 2018 in room 1245 from 15:15 to max 17:00.

• Armin Biere (Johannes Kepler University, Austria] gives a seminar "Using Computer Algebra to Verify Arithmetic Circuits" Wednesday 24 januari 2018 in room ITC 1111, from 11:15 to 12:00.

• Andreas Westerlund from Jeppesen Systems AB gives a seminar with title "Column generation for airline crew rostering: practical considerations in a production system" on Thursday 25 January 2018 in room ITC 1311 from 10.15 to max 11.30.

• A PhD course on numerical optimisation is offered in autumn 2017.

• Jean-Noël Monette of Tacton Systems AB in Stockholm gave the seminar Constraint Programming for Product Configuration on 27 November 2017.

• 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.

• Mats Carlsson of RISE SICS gave the seminar An Integrated Constraint Programming Approach to Scheduling Sports Leagues with Divisional and Round-Robin Tournaments on 20 November 2017.

• Lei You gave his half-time PhD seminar on 8 November 2017. Seminar title: Modeling and Solving Some Resource Optimization Problems in 4G and 5G networks.

• The PhD course Modelling for Discrete Optimisation was offered in autumn 2017.

• The NordConsNet Workshop 2017 of The Nordic Network for researchers and practitioners of Constraint programming was organised by us in Uppsala on Monday 22 May 2017.

• The PhD course in Discrete Optimization with Applications to Communication Networks was given in spring 2017.

• Pierre Flener gave the seminar Solving Discrete Optimisation Problems Without Knowing How on 5 April 2017.

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

• Modelling for Combinatorial Optimisation (1DL448, 5 credits) is taught every spring term. It addresses declarative problem modelling, with experiments on solvers from a wide range of combinatorial optimisation technologies: CP, MIP, SAT, SMT, SLS, and hybrids.

• 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).

• Combinatorial Optimisation using Constraint Programming (1DL441, 10 credits) is taught every autumn term. The course explains in detail the algorithms behind solvers of constraint programming (CP) technology.

• Optimisation (1TD184, 5 credits) is taught every autumn term. The course covers mathematical modelling and formulation, and basic concepts and methods in optimisation.

• Systems Analysis and Operations Research