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
PhD student, within the research project OPTIMAL
Centrum Wiskunde & Informatica (CWI) has a vacancy in the Networks & Optimization research group for a talented PhD student, within the research project OPTIMAL. OPTIMAL (Optimization for and with Machine Learning) is a Dutch ENW-groot project funded by NWO (2019-2025), offering 5 PhD and 6 PostDoc positions in the Netherlands. The institutes and researchers involved in OPTIMAL are University of Amsterdam, Amsterdam (Dick den Hertog), Tilburg University, Tilburg (Etienne de Klerk), Centrum Wiskunde & Informatica (CWI), Amsterdam (Monique Laurent, Guido Schäfer and Leen Stougie) and Delft University of Technology, Delft (Karen Aardal and Leo van Iersel).
News
CWI researcher Monique Laurent elected as KNAW member
Researcher and MT member Monique Laurent from Centrum Wiskunde & Informatica (CWI) was elected as a member of the Royal Netherlands Academy of Arts and Sciences (KNAW). The KNAW announced this on 19 April 2018. KNAW members are prominent scientists who are nominated by fellow researchers within and outside the Academy.
Vici grants for Nikhil Bansal and Roeland Merks
CWI researchers Nikhil Bansal and Roeland Merks have been awarded Vici grants from NWO. With the 1.5 million euro grant, Bansal and Merks can develop their own research lines in the next five years.
CWI and ProRail predict railway incidents
Centrum Wiskunde & Informatica and ProRail recently started a research collaboration in the area of the handling of incidents at the Dutch railways. It concerns a public private partnership between CWI and ProRail striking up a four year period. The research is aimed at prevention and handling of a broad spectrum of railway incidents, like disturbance of the railway infrastructure, people walking on the tracks and collisions.
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.
Members
Associated Members
Publications
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Bansal, N, Naor, J, & Talmon, O. (2021). Efficient online weighted multi-level paging. In Proceedings of the ACM Symposium on Parallelism in Algorithms and Architectures (pp. 94–104). doi:10.1145/3409964.3461801
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Borst, S.J, Dadush, D.N, Huiberts, S, & Tiwari, S.S.K. (2021). On the integrality gap of binary integer programs with Gaussian data. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 427–442). doi:10.1007/978-3-030-73879-2_30
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Slot, L.F.H, & Laurent, M. (2021). Sum-of-squares hierarchies for binary polynomial optimization. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 43–57). doi:10.1007/978-3-030-73879-2_4
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Kuiper, A, Mandjes, M.R.H, de Mast, J, & Brokkelkamp, K.R. (2021). A flexible and optimal approach for appointment scheduling in healthcare. Decision Sciences. doi:10.1111/deci.12517
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Bansal, N, Jiang, H, Meka, R, Singla, S, & Sinha, M. (2021). Online discrepancy minimization for stochastic arrivals. In Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 2842–2861). doi:10.1137/1.9781611976465.169
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Dadush, D.N, & Huiberts, S. (2020). A friendly smoothed analysis of the simplex method. SIAM Journal on Computing, 49(5). doi:10.1137/18M1197205
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Dadush, D.N, & Huiberts, S. (2020). Smoothed analysis of the simplex method. In T Roughgarden (Ed.), Beyond the Worst-Case Analysis of Algorithms. Cambridge University Press.
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Dadush, D.N, Huiberts, S, Natura, B, & Végh, L.A. (2020). A scaling-invariant algorithm for linear programming whose running time depends only on the constraint matrix. In Proceedings of the Annual ACM Symposium on Theory of Computing (pp. 761–774). doi:10.1145/3357713.3384326
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Bienkowski, M, Byrka, J, Coester, C.E, & Jeż, L. (2020). Unbounded lower bound for k-server against weak adversaries. In Proceedings of the Annual ACM SIGACT Symposium on Theory of Computing (pp. 1165–1169). doi:10.1145/3357713.3384306
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van Apeldoorn, J.T.S, Gilyén, A.P, Gribling, S.J, & de Wolf, R.M. (2020). Quantum SDP-Solvers: Better upper and lower bounds. Quantum, 4. doi:10.22331/q-2020-02-14-230
Current projects with external funding
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Continuous Methods in Discrete Optimization ()
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Smart Heuristic Problem Optimization ()
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Wiskundecluster DIAMANT ()
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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