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
QuSoft/CWI team earns 4th place in Benelux Algorithm Programming Contest 2019
On Saturday 19 October 2019, Arjan Cornelissen, Farrokh Labib and Ruben Brokkelkamp formed a combined QuSoft/CWI team in the BAPC 2019 programming contest. They earned the 4th place, competing against companies such as ASML, Booking.com, Ortec and Sioux.
CWI research connects quantum computing and optimization
Sander Gribling studied if and how quantum computers can help solve optimization problems, like finding the shortest route. He developed new quantum algorithms that can help to solve them more efficiently. He defends his PhD thesis on 30 September.
Cum laude for CWI research quantifying effect of safety margins in route planning
When drivers avoid certain highways to escape the risk of usual traffic jams, they can significantly increase the average travel time for everyone in the road network. CWI researcher Pieter Kleer developed models to analyze this.
Jie Li and Sophie Huiberts selected to participate in the 7th Heidelberg Laureate Forum
Jie Li from the DIS group and Sophie Huiberts from the N&O group have been selected to participate in the 7th Heidelberg Laureate Forum (HLF). The annual HLF event selects a small group of 200 most qualified Young Researchers worldwide to meet pre-eminent scientists from the fields of mathematics and computer science.
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