Model Reduction for Optimization and Control

Mat-Dyn-Net Winter School on Model Reduction for Optimization and Control is held on 19 – 23 February 2024 in Dubrovnik, Croatia

The school aims to provide a coherent set of lectures that will elucidate the mathematical foundations of the control of dynamical systems, with particular emphasis on optimization and computational methods for large-scale systems based on model reduction. It is particularly aimed at PhD students, but not limited to.


  • Tobias Breiten, Institute of Mathematics, TU Berlin, Berlin, Germany
  • Serkan Gugercin, Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, USA
  • Olga Mula, TU Eindhoven, Eindhoven, The Netherlands
  • Martin StollDepartment of Mathematics, TU Chemnitz, Chemnitz, Germany

Lectures will be held at Centre for Advanced Academic Studies.


  • Registration until 20 October 2023.


The school will begin on Monday lunchtime, February 19th, 2024, and end by lunchtime on Friday, February 23rd, 2024.

Monday TuesdayWednesdayThursdayFriday
8:30-10:00Olga MulaMartin StollTobias BreitenTobias Breiten
10:00-10:30coffee breakcoffee breakcoffee breakcoffee break
10:30-12:00Martin StollTobias BreitenSerkan GugercinSerkan Gugercin
12:00-14:00lunch breaklunch breaklunch break
14:00-15:30Olga MulaMartin StollSerkan Gugercin
15:30-16:00coffee breakcoffee breakcoffee break
16:00-17:30Olga Mula


Tobias Breiten and Serkan Gugercin: System Theoretic Methods
Part I (Tobias Breiten)
Control systems
Transfer functions
System norms
Bode plots
Linear balanced truncation

Part II (Serkan Gugercin)
Rational interpolation
The Loewner framework
Optimal interpolation

Part III (Tobias Breiten)
Bilinear control theory
Algebraic Gramians
Volterra series interpolation
Optimal interpolation

Part IV (Serkan Gugercin)
Data-driven barycentric interpolants
Least squares rational fitting, AAA
(Parametric) extensions
Olga Mula: Nonlinear, Geometric Reduced Models for Forward and Inverse Problems
This course will discuss the role of nonlinear approximation and geometry to address bottlenecks in reduced order modelling when applied to forward and inverse problems.
Martin Stoll: Low-rank methods for PDE-constrained optimization
Solving PDE-constrained optimization problems leads to very large linear systems in structured form. In my talk I will first explain how to discretize these problems and arrive at the large linear systems and how to solve them efficiently using preconditioned Krylov solvers. We then illustrate that the systems can be written in very structured form and we can utilize this structure for developing low-rank methods that approximate the solution in space and time. 

Organization and registration


  • The eligible participants will be covered within the COST action (CA18232): Mathematical models for interacting dynamics on networks (Mat-Dyn-Net). The cap for the travel is up to 450 eur and the daily allowance for the trainees 90 EUR per day, for further information please contact GH Manager Mojca Premuš (
  • In order to register for the school, interested students should apply on email:
    • with the following data: first name, last name, affiliation, city, country, e-mail address, letter of motivation (max one A4 page).
  • Participants should register until 20 October 2023.
  • Due to the fact that the number of participants is limited, notification of acceptance will be sent to each registered student by 5 November 2023.


  • Lectures will be held at Centre for Advanced Academic Studies 
  • Accommodation for participants will be organized at Centre for Advanced Academic Studies Dubrovnik, Croatia Centre for Advanced Academic Studies:
  • address: University of Zagreb
    Centre for Advanced Academic Studies
    Don Frana Bulica 4
    20000 Dubrovnik

Local organizers

Supported by

School will be held in Dubrovnik. Dubrovnik is the city on the Adriatic Sea in the region of Dalmatia.  It is one of the most famous tourist destinations in the Croatia and the centre of Dubrovnik-Neretva County (about Dubrovnik).