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Schedule
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Thursday 11:00 - 12:20 Routing Problems Room 138 - Chair: Pieter Vansteenwegen
Thursday 11:00 - 12:20 Emergency operations scheduling Room 130 - Chair: El-Houssaine Aghezzaf
Thursday 11:00 - 12:20 Algorithm design Room 126 - Chair: Gerrit Janssens
Thursday 11:00 - 12:20 Multiple Objectives Room 120 - Chair: Filip Van Utterbeeck
Thursday 13:30 - 14:50 Integrated logistics Room 138 - Chair: Kris Braekers
Thursday 13:30 - 14:50 Person transportation Room 130 - Chair: Célia Paquay
Thursday 13:30 - 14:50 Continuous models Room 126 - Chair: Nicolas Gillis
- On computing the distances to stability for matrices
Punit Sharma (University of Mons) Co-authors: Nicolas Gillis Abstract: The stability of a continuous linear time-invariant (LTI) system dx/dt = Ax+Bu, where A is a real matrix of size nxn and B is a real matrix of size nxm, solely depends on the eigenvalues of the stable matrix $A$. Such a system is stable if all eigenvalues of A are in the closed left half of the complex plane and all eigenvalues on the imaginary axis are semisimple. It is important to know that when an unstable LTI system becomes stable, i.e. when it has all eigenvalues in the stability region, or how much it has to be perturbed to be on this boundary. For control systems this distance to stability is well-understood. This is the converse problem of the distance to instability, where a stable matrix A is given and one looks for the smallest perturbation that moves an eigenvalue outside the stability region.
In this talk, I will talk about the distance to stability problem for LTI control systems. Motivated by the structure of dissipative-Hamiltonian systems, we define the DH matrix: a matrix A of size nxn is said to be a DH matrix if A=(J-R)Q for some real nxn matrices J, R, Q such that J is skew-symmetric, R is symmetric positive semidefinite and Q is symmetric positive definite. We will show that a system is stable if and only if its state matrix is a DH matrix. This results in an equivalent optimization problem with a simple convex feasible set. We propose a new very efficient algorithm to solve this problem using a fast gradient method. We show the effectiveness of this method compared to other approaches such as the block coordinate descent method and to several state-of-the-art algorithms.
For more details, this work has been published as [1].
[1] N. Gillis and P. Sharma, On computing the distance to stability for matrices using linear dissipative Hamiltonian systems. Automatica, 85, pp. 113-121, 2017.
- Low-Rank Matrix Approximation in the Infinity Norm
Nicolas Gillis (University of Mons) Co-authors: Yaroslav Shitov
- Benchmarking some iterative linear systems solvers for deformable 3D images registration
Justin Buhendwa Nyenyezi (Université de Namur)
- Spectral Unmixing with Multiple Dictionaries
Jérémy Cohen (UMONS/FNRS) Co-authors: Nicolas Gillis
Thursday 13:30 - 14:50 Integer programming Room 120 - Chair: Bernard Fortz
Thursday 15:20 - 16:20 Material handling and warehousing 1 Room 138 - Chair: Greet Vanden Berghe
Thursday 15:20 - 16:20 Operations management Room 130 - Chair: Roel Leus
Thursday 15:20 - 16:20 Matrix factorization Room 126 - Chair: Pierre Kunsch
Thursday 16:30 - 17:10 Material handling and warehousing 2 Room 138 - Chair: Katrien Ramaekers
Thursday 16:30 - 17:10 Routing and local search Room 130 - Chair: An Caris
Thursday 16:30 - 17:10 Traffic management Room 126 - Chair: Joris Walraevens
Thursday 16:30 - 17:10 Pharmaceutical supply chains Room 120 - Chair: Bart Smeulders
Friday 10:50 - 12:10 Optimization in health care Room 138 - Chair: Jeroen Beliën
Friday 10:50 - 12:10 Network design Room 130 - Chair: Jean-Sébastien Tancrez
Friday 10:50 - 12:10 Local search methodology Room 126 - Chair: Patrick De Causmaecker
Friday 10:50 - 12:10 ORBEL Award Room 120 - Chair: Frits Spieksma
Friday 13:00 - 14:00 Production and inventory management Room 138 - Chair: Tony Wauters
Friday 13:00 - 14:00 Logistics 4.0 Room 130 - Chair: Thierry Pironet
Friday 13:00 - 14:00 Data clustering Room 126 - Chair: Yves De Smet
Friday 13:00 - 14:00 Collective decision making Room 120 - Chair: Bernard De Baets
Friday 14:10 - 15:10 Sport scheduling Room 138 - Chair: Dries Goossens
Friday 14:10 - 15:10 Discrete choice modeling Room 130 - Chair: Virginie Lurkin
Friday 14:10 - 15:10 Data classification Room 126 - Chair: Ashwin Ittoo
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ORBEL - Conference chair: Prof. A. Arda -
Platform: Prof. M. Schyns.
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