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Grant: Discovery Grants Program - Individual

University of Regina

$26,000.00

Matrix Theory with Applications to Positivity and Discrete Mathematics

FY 2021-22

Summary
My research interests are centered on `Positivity' in Linear Algebra and Graph Theory. Along these lines, I have proposed exciting new and continuing research studies involving totally positive matrices and studies concerning the properties of certain matrices associated with graphs and their related combinatorial parameters along with corresponding algebraic invariants. Both of these research areas are rooted in history, of current interest, and rich in applications (both theoretical and practical), including control theory, finite automata, matrix rigidity, and inverse problems related to entanglement and decoherence. Total positivity is a well studied and fundamental class of matrices that appears in numerous applications, including statistics, mathematical biology, and computer aided geometric design. I intend to investigate a number of key problems associated with this class by making use of the combinatorial framework associated with certain matrix factorizations, and explore some exciting new directions involving important connections to an underlying fundamental algebraic structure. The plan that I have developed will lead to a deeper understanding of this class of matrices by setting out a sequence of concrete objectives and cutting-edge advances. The main issue of interest concerning matrices that are derived from graphs comes from a classical problem known as an inverse eigenvalue problem (that is, the eigenvalues are provided and the corresponding matrix is desired). An important part of this inverse eigenvalue problem is the minimum rank problem, which, in turn, is equivalent to maximizing the nullity or dimension associated with the zero eigenvalue or characteristics value of a matrix. This connection between the two follows since rank and nullity are intimately related. The hope here is to appeal to the algebraic properties of matrices and combine them with the important combinatorial characteristics of graphs to yield interesting results and advances about this special collection of matrices. In summation, I will combine existing theoretical knowledge along with new innovative sophistication to explore a number of fundamental advances and connections to important applications, including communication complexity in computer science, control of quantum systems in mathematical physics and compelling variations on certain searching problems in networks and graphs. This contemporary and important research is of current interest across the globe, as it brings together material from the theory of matrices, central notions in combinatorics, and develops a sequence of expanding fundamental issues. My research is rooted in theoretical aspects of linear algebra and discrete mathematics, but is implicitly connected to many branches of mathematics and with a wide range of emerging and interesting applications. For example, some of my work is concerned with graph propagation (or infection) and certain graph or network searching models.
Program
Discovery Grants Program - Individual
Location
Saskatchewan, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
RGPIN-2019-03934
Name nom
Fallat, Shaun
Department département
Mathematics and Statistics - Mathematics and Statistics
Organization id
17
Institution établissement
University of Regina
Province en
Saskatchewan
Province fr
Saskatchewan
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2021
Competition year année de concours
2019
Award amount
26000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1508
Committee name en
Mathematics and Statistics
Committee name fr
Mathématiques et statistique
Area of application code
1205
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Mathematical sciences
Area of application fr
Sciences mathématiques
Research subject code
2914
Research subject group en
Pure mathematics
Research subject group fr
Mathématiques pures
Research subject en
Combinatorics
Research subject fr
Combinatoire
Installment
inst:3
Partie
1
Nb partie
5
Application title
Matrix Theory with Applications to Positivity and Discrete Mathematics
Keyword
NSERC
Application summary
My research interests are centered on `Positivity' in Linear Algebra and Graph Theory. Along these lines, I have proposed exciting new and continuing research studies involving totally positive matrices and studies concerning the properties of certain matrices associated with graphs and their related combinatorial parameters along with corresponding algebraic invariants. Both of these research areas are rooted in history, of current interest, and rich in applications (both theoretical and practical), including control theory, finite automata, matrix rigidity, and inverse problems related to entanglement and decoherence. Total positivity is a well studied and fundamental class of matrices that appears in numerous applications, including statistics, mathematical biology, and computer aided geometric design. I intend to investigate a number of key problems associated with this class by making use of the combinatorial framework associated with certain matrix factorizations, and explore some exciting new directions involving important connections to an underlying fundamental algebraic structure. The plan that I have developed will lead to a deeper understanding of this class of matrices by setting out a sequence of concrete objectives and cutting-edge advances. The main issue of interest concerning matrices that are derived from graphs comes from a classical problem known as an inverse eigenvalue problem (that is, the eigenvalues are provided and the corresponding matrix is desired). An important part of this inverse eigenvalue problem is the minimum rank problem, which, in turn, is equivalent to maximizing the nullity or dimension associated with the zero eigenvalue or characteristics value of a matrix. This connection between the two follows since rank and nullity are intimately related. The hope here is to appeal to the algebraic properties of matrices and combine them with the important combinatorial characteristics of graphs to yield interesting results and advances about this special collection of matrices. In summation, I will combine existing theoretical knowledge along with new innovative sophistication to explore a number of fundamental advances and connections to important applications, including communication complexity in computer science, control of quantum systems in mathematical physics and compelling variations on certain searching problems in networks and graphs. This contemporary and important research is of current interest across the globe, as it brings together material from the theory of matrices, central notions in combinatorics, and develops a sequence of expanding fundamental issues. My research is rooted in theoretical aspects of linear algebra and discrete mathematics, but is implicitly connected to many branches of mathematics and with a wide range of emerging and interesting applications. For example, some of my work is concerned with graph propagation (or infection) and certain graph or network searching models.