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1987 | Buch

Matrix Theory

A Second Course

verfasst von: James M. Ortega

Verlag: Springer US

Buchreihe : The University Series in Mathematics

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Über dieses Buch

Linear algebra and matrix theory are essentially synonymous terms for an area of mathematics that has become one of the most useful and pervasive tools in a wide range of disciplines. It is also a subject of great mathematical beauty. In consequence of both of these facts, linear algebra has increasingly been brought into lower levels of the curriculum, either in conjunction with the calculus or separate from it but at the same level. A large and still growing number of textbooks has been written to satisfy this need, aimed at students at the junior, sophomore, or even freshman levels. Thus, most students now obtaining a bachelor's degree in the sciences or engineering have had some exposure to linear algebra. But rarely, even when solid courses are taken at the junior or senior levels, do these students have an adequate working knowledge of the subject to be useful in graduate work or in research and development activities in government and industry. In particular, most elementary courses stop at the point of canonical forms, so that while the student may have "seen" the Jordan and other canonical forms, there is usually little appreciation of their usefulness. And there is almost never time in the elementary courses to deal with more specialized topics like nonnegative matrices, inertia theorems, and so on. In consequence, many graduate courses in mathematics, applied mathe­ matics, or applications develop certain parts of matrix theory as needed.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Review of Basic Background
Abstract
In this chapter we review some of the basic ideas of matrix theory. It is assumed that the reader is (or was at one time) familiar with the contents of this chapter, or at least most of it, and a rather quick reading will serve to recall these basic facts as well as to establish certain notation that will be used in the remainder of the book. Some of the topics covered, especially linear equations and eigenvalues, will be expanded upon later in different ways.
James M. Ortega
Chapter 2. Linear Spaces and Operators
Abstract
Matrix theory can be studied with no mention of linear spaces, and most of the results in this book are of such a nature. However, the introduction of linear spaces and the role of matrices in defining or representing linear transformations on such spaces add considerably to our insight. Most importantly, perhaps, the notions of linear spaces and linear transformations give a geometrical basis to matrix theory, which aids both in understanding as well as in suggesting proofs and new results.
James M. Ortega
Chapter 3. Canonical Forms
Abstract
In this chapter we consider the following question: Given a linear operator A: RS, where R and S are finite-dimensional linear spaces, what is the “simplest” form that a matrix representation of A can take by judicious choice of bases in R and S? By the results of Section 2.2, this question is equivalent to the following one: Given an m × n matrix A, what is the “simplest” form that the matrix PAQ can take by judicious choice of nonsingular matrices P and Q?
James M. Ortega
Chapter 4. Quadratic Forms and Optimization
Abstract
We now begin several applications of the theory developed in the last two chapters. In the present chapter we consider first the geometry of the solution sets of quadratic equations in n variables. These solution sets generalize ellipses, parabolas, and hyperbolas in two variables, and a classification of their geometry in n dimensions is given by the inertia of the coefficient matrix of the quadratic form. In the next section we treat the unconstrained quadratic optimization problem and show that a necessary and sufficient condition for a unique solution is that the coefficient matrix be definite. We then consider the special constrained optimization problem of a quadratic function on the unit sphere and show that the maximum and minimum are just the largest and smallest eigenvalues of the coefficient matrix. This leads to the famous min-max representation of the eigenvalues of a Hermitian matrix. In Section 4.3 we specialize the minimization problem to the very important least squares problem and give the basic result on the existence and uniqueness of a solution. Particular examples are the linear regression and polynomial approximation problems. Then we treat the least squares problem in a more general way and obtain a minimum norm solution in the case that the original problem has infinitely many solutions. We show that this minimum norm solution can be represented in terms of a generalized inverse based on the singular value decomposition.
James M. Ortega
Chapter 5. Differential and Difference Equations
Abstract
In this chapter we treat various questions about ordinary differential and difference equations. We first define and give various properties of the exponential of a matrix, which allows us to express the solution of a system of differential equations in a concise way. The Jordan form is the main tool that allows us to obtain the basic properties of a matrix exponential, and through these properties we are able to express the solution of a system of differential equations ẋ = A x with constant coefficients in terms of the eigensystem of A. Higher-order equations can be reduced to a first-order system and, thus, treated in the same way. In Section 5.2 we ascertain the stability of solutions when the initial condition is changed, and in Section 5.3 we obtain corresponding results for difference equations. These stability results for difference equations can be interpreted also as convergence theorems for certain iterative methods. Finally, in Section 5.4, we treat Lyapunov’s criterion for stability as well as several related results.
James M. Ortega
Chapter 6. Other Topics
Abstract
We collect in this final chapter a number of different topics. In Section 6.1 we deal with matrices that have nonnegative or positive entries, or whose inverses have this property. Such matrices arise in a number of application areas, and there are many beautiful and striking results concerning them. In Section 6.2 we treat various extensions of the eigenvalue problem, including the so-called generalized eigenvalue problem and higher-order problems. In Section 6.3 we consider some very special, but important, types of matrices, including Kronecker products and circulants. Finally, in Section 6.4, we deal with matrix equations and commutativity of matrices.
James M. Ortega
Backmatter
Metadaten
Titel
Matrix Theory
verfasst von
James M. Ortega
Copyright-Jahr
1987
Verlag
Springer US
Electronic ISBN
978-1-4899-0471-3
Print ISBN
978-0-306-42433-5
DOI
https://doi.org/10.1007/978-1-4899-0471-3