Structured matrices play a relevant role in symbolic and numerical computations. In the literature and in applications we encounter several types of structure, which are typically related to the properties of the problems they stem from: banded structure is often associated with locality of functions or operators, Toeplitz structure arises from shift invariance properties, off-diagonal low-rank structure appears in inverses of banded matrices.A common trait of most matrix structures is the availability of fast algorithms that perform fundamental operations, such as matrix-vector or matrix-matrix multiplication, solution of linear systems, or eigenvalue computation. For problems of large size, such algorithms are extremely useful both from a symbolic and a numeric point of view, although of course in a numerical setting one needs to pay attention to possible stability issues.The tutorial will start with a brief overview of matrix structures and of their properties: we are especially interested here in rank structures and in certain forms of sparsity. We will then focus on selected topics concerning the analysis and computation of functions of structured matrices. Functions of matrices have a wide range of applications, for which we will give several examples, from the solution of differential equations to network analysis. Think for instance of the matrix exponential exp(A): it appears in the solution of a multidimensional Cauchy problem with coefficient matrix A, but it also has a combinatorial meaning when A is the adjacency matrix of a graph. If the quantity of interest is the action of a matrix function on a vector, moreover, one can often bypass the explicit construction of the matrix function itself and devise a more efficient approach.Most methods for the computation of matrix functions are ultimately based on polynomial or rational approximation, which are either applied in explicit form, or through iterative methods such as Lanczos or Arnoldi, often in combination with suitable pre- and post-processing steps. If matrix A is structured, it is natural to ask how to tailor such methods to the specific structure of A. We will present a few possible answers to this question and demonstrate their advantages through several practical examples.

### Matrix Structures and Matrix Functions

#### Abstract

Structured matrices play a relevant role in symbolic and numerical computations. In the literature and in applications we encounter several types of structure, which are typically related to the properties of the problems they stem from: banded structure is often associated with locality of functions or operators, Toeplitz structure arises from shift invariance properties, off-diagonal low-rank structure appears in inverses of banded matrices.A common trait of most matrix structures is the availability of fast algorithms that perform fundamental operations, such as matrix-vector or matrix-matrix multiplication, solution of linear systems, or eigenvalue computation. For problems of large size, such algorithms are extremely useful both from a symbolic and a numeric point of view, although of course in a numerical setting one needs to pay attention to possible stability issues.The tutorial will start with a brief overview of matrix structures and of their properties: we are especially interested here in rank structures and in certain forms of sparsity. We will then focus on selected topics concerning the analysis and computation of functions of structured matrices. Functions of matrices have a wide range of applications, for which we will give several examples, from the solution of differential equations to network analysis. Think for instance of the matrix exponential exp(A): it appears in the solution of a multidimensional Cauchy problem with coefficient matrix A, but it also has a combinatorial meaning when A is the adjacency matrix of a graph. If the quantity of interest is the action of a matrix function on a vector, moreover, one can often bypass the explicit construction of the matrix function itself and devise a more efficient approach.Most methods for the computation of matrix functions are ultimately based on polynomial or rational approximation, which are either applied in explicit form, or through iterative methods such as Lanczos or Arnoldi, often in combination with suitable pre- and post-processing steps. If matrix A is structured, it is natural to ask how to tailor such methods to the specific structure of A. We will present a few possible answers to this question and demonstrate their advantages through several practical examples.
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2023
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Utilizza questo identificativo per citare o creare un link a questo documento: `https://hdl.handle.net/11568/1215663`
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