Matrix algebra refresher

Hopefully Chapters 1 and 2 have refreshed your memory of early studies in matrix algebra. In this chapter we have tried to review the basic steps used to solve a system of linear equations using elementary matrix algebra. In addition, basic row operations... 

By now some of you must be thinking that there must be an easier way to solve systems of equations than wrestling with manual row operations. Well, of course there are better ways, which is why we will refresh your memory on the concept of determinants in the next chapter. After we have introduced determinants we will conclude our introductory coverage of matrix algebra and MLR with some final remarks. 

The book is at an introductory level, and only basic mathematical and statistical knowledge is assumed. However, we do not present chemometrics without equations —the book is intended for mathematically interested readers. Whenever possible, the formulae are in matrix notation, and for a clearer understanding many of them are visualized schematically. Appendix 2 might be helpful to refresh matrix algebra. 

Due to the special structure of MATLAB, readers should be familiar with the mathematical concepts pertaining to matrices, such as systems of linear equations, Gaussian elimination, size and rank of a matrix, matrix eigenvalues, basis change in n-dimensional space, matrix transpose, etc. For those who need a refresher on these topics there is a concise Appendix on linear algebra and matrices at the end of the book. 

Let s recall how to find eigenvalues and eigenvectors. (If your memory needs more refreshing, see any text on linear algebra.) In general, the eigenvalues of a matrix A are given by the characteristic equation det(A - Af) = 0, where 1 is the identity matrix. For a 2 x 2 matrix... 

Abstract This chapter refreshes such necessary algebraic knowledge as will be needed in this book. It introduces function spaces, the meaning of a linear operator, and the properties of unitary matrices. The homomorphism between operations and matrix multiplications is established, and the Dirac notation for function spaces is defined. For those who might wonder why the linearity of operators need be considered, the final section introduces time reversal, which is anti-linear. 

To understand the concepts of the methods described in the following chapters, one should have an understanding of the basics of linear algebra. This chapter will serve as a short refresher. Linear algebra essentially deals with vector and matrix manipulations, all of which can easily be performed by using the MATLAB software. However, an insight into some of the concepts behind the operations may be helpful. 

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