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Seminar by Robert M. Corless

Bohemian Matrices: An introduction and some open problems

11 Oct 2023 14:00 - 15:00

The name “Bohemian matrices” was proposed for a field that has actually been studied for many years now by different researchers, not recognizing their commonality. The name comes from an acronym for BOunded HEight Matrices, of Integers, although the restriction to integers has been subsequently relaxed.

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11 Oct 2023 14:00 - 15:00
KU Leuven, Aula Arenbergkasteel (01.07)
English
Target audience: Researchers and academics interested in system theory, algebraic geometry, optimization, and numerical algebra.

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The name “Bohemian matrices” was proposed for a field that has actually been studied for many years now by different researchers, not recognizing their commonality. The name comes from an acronym for BOunded HEight Matrices, of Integers, although the restriction to integers has been subsequently relaxed. The “height” of a vector is its infinity norm, and the “height” of a matrix A is the infinity norm of vec(A). The word “height” has been commonly used in the context of univariate polynomials expressed in the monomial basis: the height of a polynomial is the infinity norm of the vector of coefficients. We say “characteristic height” for the height of the characteristic polynomial of a matrix A.

The idea is to restrict the study to families of matrices all of whose entries come from a finite (and hence bounded) set, called the population. Well-known examples include zero-one or binary matrices, Bernoulli matrices whose entries are either 1 or -1, and sign pattern matrices where the entries can be 1, 0, or -1. One can then study Bohemian families of structured matrices, say upper Hessenberg Toeplitz Bohemian matrices, or complex symmetric tridiagonal Bohemian matrices, or symmetric matrices with entries from a restricted population, or Bohemian companion matrices, or other families.

In this talk I will survey some known results, and discuss some problems that remain open. In particular, I will ask for help in finding algorithms to construct “minimal height” companion matrices for polynomials with integer coefficients; such things exist (possibly not uniquely) but there is no known good algorithm for constructing them. I will give examples where the height of the companion matrix is exponentially smaller (in the degree/dimension) than is the characteristic height. This seems interesting because lower height offers the hope of better eigenvalue conditioning. But even that is not well-understood.

This is joint work with many people.

Teacher / speaker

Robert M. Corless

Editor-in-Chief, Maple Transactions
Emeritus Distinguished University Professor, Western University, London, Ontario, Canada

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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