This note
covers systems of linear equations, Row reduction and echelon form, Vector
equations, The matrix equation, Homogeneous and nonhomogeneous systems, Linear
independence, Introduction to linear mappings, Onto, One to one and standard
matrix, Matrix algebra, Invertible matrices, Determinants, Properties of the
determinant, Applications of the determinant, Vector spaces, Linear maps, Linear
independence, Bases and dimension, The rank theorem, Coordinate systems, Change
of basis, Inner products and orthogonality, Eigen values and eigen vectors, The
characteristic polynomial, Diagonalization, Diagonalization of symmetric
matrices, The PageRank algorithm, Discrete dynamical systems.
This note
covers systems of linear equations, Row reduction and echelon form, Vector
equations, The matrix equation, Homogeneous and nonhomogeneous systems, Linear
independence, Introduction to linear mappings, Onto, One to one and standard
matrix, Matrix algebra, Invertible matrices, Determinants, Properties of the
determinant, Applications of the determinant, Vector spaces, Linear maps, Linear
independence, Bases and dimension, The rank theorem, Coordinate systems, Change
of basis, Inner products and orthogonality, Eigen values and eigen vectors, The
characteristic polynomial, Diagonalization, Diagonalization of symmetric
matrices, The PageRank algorithm, Discrete dynamical systems.
This
note explains the following topics: Eigenvalues and Eigenvectors, The
spectral theorem, Tensor Products, Fourier Analysis and Quadrtic Reciprocity.
This note covers the following topics: Linear Algebra, Matrix Algebra,
Homogeneous Systems and Vector Subspaces, Basic Notions, Determinants and
Eigenvalues, Diagonalization, The Exponential of a Matrix, Applications,Real
Symmetric Matrices, Classification of Conics and Quadrics, Conics and the Method
of Lagrange Multipliers, Normal Modes.
This book is
meant to provide an introduction to vectors, matrices, and least squares
methods, basic topics in applied linear algebra. Our goal is to give the
beginning student, with little or no prior exposure to linear algebra, a good
grounding in the basic ideas, as well as an appreciation for how they are used
in many applications, including data fitting, machine learning and artificial
intelligence, tomography, image processing, finance, and automatic control
systems. Topics covered includes: Vectors, Norm and distance, Clustering,
Matrices, Linear equations, Matrix multiplication, Linear dynamical systems,
Least squares, Multi-objective least squares, Constrained least squares.
The purpose with
these notes is to introduce students to the concept of proof in linear algebra
in a gentle manner. Topics covered includes: Matrices and Matrix Operations,
Linear Equations, Vector Spaces, Linear Transformations, Determinants, Eigenvalues and Eigenvectors, Linear Algebra and Geometry.
This is a book on
linear algebra and matrix theory. It provides an introduction to various
numerical methods used in linear algebra. This is done because of the
interesting nature of these methods. Topics covered includes: Matrices And
Linear Transformations, Determinant, Row Operations, Factorizations, Vector
Spaces And Fields, Linear Transformations, Inner Product Spaces, Norms For
Finite Dimensional Vector Spaces.
This textbook is suitable for a
sophomore level linear algebra course taught in about twenty-five lectures. It
is designed both for engineering and science majors, but has enough abstraction
to be useful for potential math majors. Our goal in writing it was to produce
students who can perform computations with linear systems and also understand
the concepts behind these computations.
Author(s): David Cherney,
Tom Denton, Rohit Thomas and Andrew Waldron