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

Daniel Dadush and Samarth Tiwari receive Best Paper Award at CCC'20
Daniel Dadush and Samarth Tiwari from CWI's Networks and Optimization research group receive the Best Paper Award at the Computational Complexity Conference 2020 for their work 'On the Complexity of Branching Proofs'.

Daniel Dadush receives prestigious Van Dantzig Prize
Daniel Dadush has been awarded the Van Dantzig Prize, which is considered the highest Dutch award in statistics and operations research. It is awarded by the VVSOR once every five years.

CWI researchers involved in two NWO-Groot grants
In the NWO Open Competition ENW-GROOT programme, four CWI researchers received in total two grants to study machine learning and neural networks: Nikhil Bansal, Monique Laurent, Benjamin Sanderse and Leen Stougie.

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.
Current events
Dutch Seminar on Optimization (online series)
- 2021-01-28T16:00:00+01:00
- 2021-01-28T17:00:00+01:00
Dutch Seminar on Optimization (online series)
Start: 2021-01-28 16:00:00+01:00 End: 2021-01-28 17:00:00+01:00
The Dutch Seminar on Optimization is an initiative to bring together researchers from the Netherlands and beyond. The objective is to establish a new forum for the Dutch optimization community to come together, to help provide a spotlight for up and coming local talent, and to bring in high quality international speakers.
Next talk by David de Laat (TU Delft)
For more information please visit the website.
NMC 2021 (Dutch Mathematical Congress)
- 2021-01-28T16:00:00+01:00
- 2021-01-28T17:00:00+01:00
NMC 2021 (Dutch Mathematical Congress)
Start: 2021-01-28 16:00:00+01:00 End: 2021-01-28 17:00:00+01:00
The organizing committee of NMC has composed a programme containing various online activities at different times during the year: we proudly present the NMC 2021 Series!
Opening date 28 January 2021 with the Stieltjes Prize award ceremony.
For more information and registration please visit the website.
Members
Associated Members
Publications
-
Dadush, D.N, & Huiberts, S. (2020). A friendly smoothed analysis of the simplex method. SIAM Journal on Computing, 49(5). doi:10.1137/18M1197205
-
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.
-
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
-
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
-
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
-
van Apeldoorn, J.T.S. (2020, February 6). A quantum view on convex optimization. ILLC Dissertation Series.
-
de Klerk, E, & Laurent, M. (2020). Worst-case Examples for Lasserre’s Measure–Based Hierarchy for Polynomial Optimization on the Hypercube. Mathematics of Operations Research, 45(1). doi:10.1287/moor.2018.0983
-
Slot, L.F.H, & Laurent, M. (2020). Improved convergence analysis of Lasserre’s measure-based upper bounds for polynomial minimization on compact sets. Mathematical Programming, 2020. doi:10.1007/s10107-020-01468-3
-
van Apeldoorn, J.T.S, Gilyén, A.P, Gribling, S.J, & de Wolf, R.M. (2020). Convex optimization using quantum oracles. Quantum, 4. doi:10.22331/q-2020-01-13-220
-
Bansal, N, & Meka, R. (2020). On the discrepancy of random low degree set systems. In Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms. doi:10.1137/1.9781611975482.157
Current projects with external funding
-
Continuous Methods in Discrete Optimization ()
-
Wiskundecluster DIAMANT ()
-
Smart Heuristic Problem Optimization
-
Mixed-Integer Non-Linear Optimisation Applications (MINOA)
-
Optimization for and with Machine Learning (OPTIMAL)
-
Polynomial Optimization, Efficiency through Moments and Algebra (POEMA)
-
Vóórkomen en voorkómen van incidenten op het spoor (PPS Prorail)
-
Towards a Quantitative Theory of Integer Programming (QIP)
Related partners
-
Alma Mater Studiorum-Universita di Bologna
-
Alpen-Adria-Universität Klagenfurt
-
CNR Pisa
-
CNRS
-
Dassault Systèmes B.V.
-
IBM
-
INRIA
-
Prorail
-
Rheinische Friedrich-Wilhelmus Universitaet Bonn
-
Technische Universität Dortmund
-
Tilburg University
-
Tromsø, Norway
-
Universita degli Studi di Firenze
-
Universität Konstanz
-
University of Birmingham
-
Universiteit van Tilburg