Description
Leader of the group Networks and Optimization: Guido Schäfer.
In today’s society, complex systems surround us. From transport and traffic, to behavioral economics and operations management, real-world applications often demand that we identify simple, optimal solutions among a huge set of possibilities. Our research group Networks and Optimization (N&O) does fundamental research to tackle such challenging optimization problems.
We develop algorithmic methods to solve complex optimization problems efficiently. Our research provides efficient algorithms to some of the most challenging problems, for example, in planning, scheduling and routing. To come up with the best optimization algorithms, we combine and extend techniques from different disciplines in mathematics and computer science.
N&O covers a broad spectrum of optimization aspects. Our expertise ranges from discrete to continuous optimization and applies to centralized and decentralized settings. We focus on both problem-specific methods and universal toolkits to solve different types of optimization problems. The key in our investigations is to understand and exploit combinatorial structures, such as graphs, networks, lattices and matroids. Our research is of high scientific impact and contributes to various fields.
In several cooperations with industry partners, the algorithmic techniques that we develop in our group have proven useful to solve complex real-world problems. We are always interested in new algorithmic challenges arising in real-world applications and are open to new cooperations.
Watch our group video to get a glimpse of our activities.
Video about our collaboration with ProRail (in Dutch)
Vacancies
No vacancies currently.
News
Large H2020 grant for optimisation research project ‘MINOA’
Recently, the EU granted the Mixed-Integer Non-Linear Optimisation Applications (MINOA) project, as part of the Horizon 2020 Research and Innovation Framework Programme. The project is a collaborative European Training Network, with 12 research partners, CWI being one of them.
Monique Laurent elected as Fellow of SIAM
CWI researcher Monique Laurent is elected as a Fellow of the international Society for Industrial and Applied Mathematics – SIAM. This prestigious Fellowship honours SIAM members who have made outstanding contributions to these fields. Laurent, who is a member of the management team of CWI and a professor at Tilburg University, receives the fellowship for her contributions to discrete and polynomial optimization and revealing interactions between them.
Rubicon grants for David de Laat and Alessandro Zocca
CWI researchers David de Laat and Alessandro Zocca both received an NWO Rubicon grant. De Laat will study new techniques to compute optimal packings at MIT for one year, and Zocca will study renewables and uncertainty in future power systems at Caltech for two years. NWO granted these subsidies to 22 young, highly promising research talents to gain international research experience at foreign top institutes.
Better ranking for big data using seriation solution
The recent availability of large data sets leads to the challenge of identifying patterns and structures in them. These patterns can be deployed to improve the user experience
Members
Associated Members
Publications
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Brokkelkamp, K.R, van Venetië, R., de Vries, M.J, & Westerdiep, J. (2020). PACE Solver Description: tdULL. In 15th International Symposium on Parameterized and Exact Computation. doi:10.4230/LIPIcs.IPEC.2020.29
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Bansal, N, & Spencer, J.H. (2020). On-line balancing of random inputs. Random Structures & Algorithms, 57(4), 879–891. doi:10.1002/rsa.20955
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Slot, L.F.H, & Laurent, M. (2020). Near-optimal analysis of Lasserre’s univariate measure-based bounds for multivariate polynomial optimization. Mathematical Programming. doi:10.1007/s10107-020-01586-y
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Dadush, D.N, & Tiwari, S.S.K. (2020). On the complexity of branching proofs. In Leibniz International Proceedings in Informatics, LIPIcs. doi:10.4230/LIPIcs.CCC.2020.34
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Bansal, N, & Meka, R. (2020). On the discrepancy of random low degree set systems. Random Structures & Algorithms, 57(3). doi:10.1002/rsa.20935
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Bansal, N, Jiang, H, Singla, S, & Sinha, M. (2020). Online vector balancing and geometric discrepancy. In Proceedings of the Annual ACM Symposium on Theory of Computing (pp. 1139–1152). doi:10.1145/3357713.3384280
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Dadush, D.N, Végh, L.A, & Zambelli, G. (2020). Rescaling algorithms for linear conic feasibility. Mathematics of Operations Research, 45(2), 732–754. doi:10.1287/moor.2019.1011
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Huizing, D, Schäfer, G, van der Mei, R.D, & Bhulai, S. (2020). The median routing problem for simultaneous planning of emergency response and non-emergency jobs. European Journal of Operational Research. doi:10.1016/j.ejor.2020.02.002
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de Klerk, E, & Laurent, M. (2020). Convergence analysis of a Lasserre hierarchy of upper bounds for polynomial minimization on the sphere. Mathematical Programming, 2020. doi:10.1007/s10107-019-01465-1
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Abiad, A, Gribling, S.J, Lahaye, D.J.P, Mnich, M, Regts, G, Vena, L, … Zwaneveld, P.J. (2020). On the complexity of solving a decision problem with flow-depending costs: The case of the IJsselmeer dikes. Discrete Optimization. doi:10.1016/j.disopt.2019.100565
Current projects with external funding
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Continuous Methods in Discrete Optimization ()
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Wiskundecluster DIAMANT ()
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Smart Heuristic Problem Optimization
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Mixed-Integer Non-Linear Optimisation Applications (MINOA)
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Optimization for and with Machine Learning (OPTIMAL)
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Polynomial Optimization, Efficiency through Moments and Algebra (POEMA)
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Vóórkomen en voorkómen van incidenten op het spoor (PPS Prorail)
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Towards a Quantitative Theory of Integer Programming (QIP)
Related partners
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Alma Mater Studiorum-Universita di Bologna
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Alpen-Adria-Universität Klagenfurt
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CNR Pisa
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CNRS
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Dassault Systèmes B.V.
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IBM
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INRIA
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Prorail
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Rheinische Friedrich-Wilhelmus Universitaet Bonn
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Technische Universität Dortmund
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Tilburg University
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Tromsø, Norway
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Universita degli Studi di Firenze
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Universität Konstanz
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University of Birmingham
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Universiteit van Tilburg