Back to the numerical linear algebra roots of polynomials and nonlinear eigenvalue problems
This event features presentations of four experts in algebraic geometry and numerical linear algebra, delving into topics such as multivariate polynomials and nonlinear/multiparameter eigenvalue problems.
Praktische info:
Inschrijven?
- Voorwaarden: foundational understanding of artificial intelligence, numerical optimization, and mathematical concepts
- Prijs: free
georganiseerd door:
Leertraject
Schedule
12:00-12:45 Informal walking lunch (in ESAT B00.35)
12:45-13:30 Presentation by prof. dr. Fatemeh Mohammadi
- “Polynomial systems arising in the formal verification of programs”
13:30-14:15 Presentation by prof. dr. Bor Plestenjak
- “Numerical methods for rectangular multiparameter eigenvalue problems”
14:15-14:30 Coffee break
14:30-15:15 Presentation by prof. dr. Karl Meerbergen
- “Linearizations for NEPv: nonlinear eigenvalue problems with eigenvector nonlinearity”
15:15-16:00 Presentation by prof. dr. Bernard Mourrain
- “Linear algebra for non-linear problems”
Back to the Roots Seminar Series
The ERC research project "Back to the roots of data-driven dynamical system identification", led by Prof. Dr. Bart De Moor (KU Leuven, ESAT-STADIUS), focuses on system identification, where mathematical models are derived from observed data generated by systems such as medical monitoring, electricity consumption and industrial processes. Utilizing optimization algorithms, one seeks to identify the best model in a chosen model class. This methodology finds widespread application across thousands of use cases within the AI community. However, there is no guarantee that optimization algorithms will find the best model. Present-day optimization practices are heuristic in nature, yielding results that may not be reproducible and consequently difficult to interpret.
The main objective of the Back to the Roots project is to develop a theoretical framework that combines model classes and optimization algorithms, enabling the calculation of the optimal model within the specified model class with 100% certainty.
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