Linear Algebra Diagonalization
A first course in linear algebra is an introductory textbook designed for university sophomores and juniors.
Linear algebra diagonalization. The present textbook is intended for a one term course at the junior or senior level. Students are expected to be comfortable with numerical linear algebra and multivariate calculus and to have programming experience preferably in matlab. An exception is the computation of norms which also extends to scalars and vectors. It parallels the combination of theory and applications in professor strangs textbook introduction to linear algebra. The operations described in this tutorial are unique to matrices.
Further information on these functions can be found in standard mathematical texts by such authors as golub and van loan or meyer. The book begins with systems of linear equations then covers matrix algebra before taking up finite dimensional vector spaces in full generality. Any capsule summary of linear algebra would have to describe the subject as the interplay of linear transformations and vector spaces. Early in chapter vs we prefaced the definition of a vector space with the comment that it was one of the two most important definitions in the entire course here comes the other. This course covers matrix theory and linear algebra emphasizing topics useful in other disciplines such as physics economics and social sciences natural sciences and engineering.
This tutorial reviews the functions that mathematica provides for carrying out matrix computations. Linear algebra matrix algebra homogeneous systems and vector subspaces basic notions determinants and eigenvalues diagonalization the exponential of a matrix applicationsreal symmetric matrices classification of conics and quadrics conics and the method of lagrange multipliers normal modes. This section contains a complete set of video lectures on linear algebra along with transcripts and related resource files. V v is called diagonalizable if there exists an ordered basis of v with respect to which t is represented by a diagonal matrix. This note covers the following topics.
Typically such a student will have taken calculus but this is not a prerequisite. Most of this article focuses on real and complex matrices that is matrices whose elements are real numbers or complex numbers. It begins with an exposition of the basic theory of finite dimensional vector spaces and proceeds to explain the structure theorems for linear maps including eigenvectors and eigenvalues quadratic and hermitian forms diagonalization of symmetric hermitian and unitary linear maps and matrices. A matrix is a rectangular array of numbers or other mathematical objects for which operations such as addition and multiplication are defined.