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, realworld 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 problemspecific 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 realworld problems. We are always interested in new algorithmic challenges arising in realworld 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
Postdoc position in the area of Discrete and Continuous Optimization
Centrum Wiskunde & Informatica (CWI) has a vacancy in the Networks & Optimization research group for a talented postdoctoral researcher in the area of discrete and continuous optimization.
News
Current events
Dutch Seminar on Optimization (online series) with Lucas Slot (CWI) and Juan José Maulén (Groningen)
 20211028T16:00:00+02:00
 20211028T17:00:00+02:00
Dutch Seminar on Optimization (online series) with Lucas Slot (CWI) and Juan José Maulén (Groningen)
Start: 20211028 16:00:00+02:00 End: 20211028 17:00:00+02:00
The Dutch Seminar on Optimization is an initiative to bring together researchers from the Netherlands and beyond, with topics that are centered around Optimization in a broad sense. We would like to invite all researchers, especially also PhD students, who are working on related topics to join the events.
Speakers: Lucas Slot (CWI) and Juan José Maulén (RU Groningen)
Title of the talk of Lucas Slot: Degree bounds for positivity certificates and the polynomial kernel method
Abstract of the talk of Lucas Slot:
The classical Positvestellensaetze of Putinar and Schmuedgen state that the positivity of a polynomial f over a (compact) semialgebraic set S can be verified by expanding f as a conic combination of the polynomials that define S, using sums of squares of polynomials as coefficients. Recently, there has been an interest in proving bounds on the largest degree of the sumofsquares coefficients involved in this expansion. Such bounds have direct implications for the convergence rate of Lasserretype hierarchies for polynomial optimization. We present the polynomial kernel method for proving degree bounds on special sets S, which was first used in the case of the hypersphere by Fang and Fawzi.
We apply the PKM to additional sets, including the binary hypercube {1, 1}^n, the unit box [1, 1]^n and the unit ball.
This is based on joint work with Monique Laurent.
Arxiv link: https://arxiv.org/abs/2011.04027
Title of the talk of Juan José Maulén: Acceleration of fixed point algorithms via inertia
Abstract of the talk of Juan José Maulén:
In this talk, we will use the results about the convergence of inertial optimization algorithms, defined as fixed point iterations from a family of cocoercive operators. This result implies that exists inertial versions of the Primaldual splitting algorithm proposed by Briceño and Roldan on 2019, and for the threeoperator splitting scheme proposed by Davis and Yin on 2015. Both algorithms fit on the framework of optimization algorithms defined by cocoercive operators. The inertial versions obtained are tested in several numerical experiments, where better performance with respect to the original algorithms can be observed.
The lecture will be given online. Please visit the website for more information and the zoom link.
Members
Associated Members
Publications

Bansal, N, Naor, J, & Talmon, O. (2021). Efficient online weighted multilevel paging. In Proceedings of the ACM Symposium on Parallelism in Algorithms and Architectures (pp. 94–104). doi:10.1145/3409964.3461801

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:10.1007/9783030738792_30

Slot, L.F.H, & Laurent, M. (2021). Sumofsquares hierarchies for binary polynomial optimization. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 43–57). doi:10.1007/9783030738792_4

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

Bansal, N, Jiang, H, Meka, R, Singla, S, & Sinha, M. (2021). Online discrepancy minimization for stochastic arrivals. In Proceedings of the Annual ACMSIAM Symposium on Discrete Algorithms (pp. 2842–2861). doi:10.1137/1.9781611976465.169

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 WorstCase Analysis of Algorithms. Cambridge University Press.

Dadush, D.N, Huiberts, S, Natura, B, & Végh, L.A. (2020). A scalinginvariant 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 kserver 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 SDPSolvers: Better upper and lower bounds. Quantum, 4. doi:10.22331/q20200214230
Current projects with external funding

Smart Heuristic Problem Optimization ()

Wiskundecluster DIAMANT ()

MixedInteger NonLinear 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 StudiorumUniversita di Bologna

AlpenAdriaUniversität Klagenfurt

CNR Pisa

CNRS

Dassault Systèmes B.V.

IBM

INRIA

Prorail

Rheinische FriedrichWilhelmus Universitaet Bonn

Technische Universität Dortmund

Tilburg University

Tromsø, Norway

Universita degli Studi di Firenze

Universität Konstanz

University of Birmingham

Universiteit van Tilburg