
Math 130 Linear Algebra


Please bookmark this page, http://aleph0.clarku.edu/~ma130/, so you can readily access it.
General information
Syllabus
We won’t cover all of the topics listed below at the same depth. Some topics are fundamental and we’ll cover them in detail; others indicate further directions of study in linear algebra and we’ll treat them as surveys. Besides those topics listed below, we will discuss some applications of linear algebra to other parts of mathematics and statistics and to physical and social sciences.
Matrices and vectors
Matrices.
Matrix addition and scalar multiplication.
Matrix multiplication. Matrix algebra. Matrix inverses.
Powers of a matrix. The transpose and symmetric matrices.
Vectors: their addition, subtraction, and multiplication by scalars (i.e. real numbers). Graphical interpretation of these vector operations
Developing geometric insight.
Inner products and norms in R^{n}: inner products of vectors (also called dot products), norm of a vector (also called length), unit vectors.
Applications of inner products in R^{n}: lines,
planes in R^{3}, and
lines and hyperplanes in R^{n}.
Systems of linear equations
Matrix inversion and determinants
Rank, range and linear equations
The rank of a matrix.
Rank and systems of linear equations.
Range.
Vector spaces
Fields.
Vector spaces.
Subspaces.
Linear span.
Linear independence, bases and dimension
Linear independence.
Bases. Coordinates.
Dimension.
Basis and dimension in R^{n}.
Linear transformations and change of basis
Linear transformations.
Range and null space.
Coordinate change.
Change of basis and similarity
Diagonalisation
Eigenvalues and eigenvectors.
Diagonalisation of a square matrix.
When is diagonalisation possible?
Applications of diagonalisation
Powers of matrices.
Systems of difference equations.
Linear systems of differential equations.
Inner products and orthogonality
Inner products.
Orthogonality.
Orthogonal matrices.
GramSchmidt orthonormalisation process
Orthogonal diagonalisation and its applications
Orthogonal diagonalisation of symmetric matrices.
Quadratic forms.
Direct sums and projections
The direct sum of two subspaces.
Orthogonal complements.
Projections.
Characterizing projections and orthogonal projections.
Orthogonal projection onto the range of a matrix. Minimizing the distance to a subspace.
Fitting functions to data: least squares approximation.
Complex matrices and vector spaces
Complex numbers.
Dave’s Short Course on Complex Numbers.
Complex vector spaces.
Complex matrices.
Complex inner product spaces.
Hermitian conjugates.
Unitary diagonalisation and normal matrices.
Spectral decomposition.
Class notes, quizzes, tests, homework assignments
To be filled in as the course progresses.
 First assignment. Exercises 1.1 through 1.7 page 53, and problems 1.1 through 1.6 page 55.
 Second assignment. Problems 1.8 through 1.14 page 57.
 Third assignment. Problems 2.1 through 2.8 page 86.
 Writing Proofs: structures of theorems and proofs,
synthetic and analytic proofs, logical symbols, and wellwritten proofs
 A little bit about sets
 Notes on Matlab
 Vector Spaces, their axiomatic definition
 Properties of vector spaces that follow from the axioms
 Subspaces of vector spaces
 Linear differential equations, an application of
linear algebra
 Linear combinations and basis
 Span, and independence
 The replacement theorem and dimension
 Where dimension doesn’t work
 Isomorphisms
 Linear transformations, matrices, and linear operators
 Some linear transformations of the plane
R^{2}
 Coordinates
 Composition and categories, composition of linear transformations and multiplication of matrices
 Algebra of linear transformations and matrices
 Kernel, image, nullity, and rank
 Change of coordinates
 Rank and nullity of matrices
 Introduction to determinants, 2x2 and 3x3 determinants, areas of triangles and parallelagrams in the plane, volumes of parallelepipeds, Jacobians
 Permutations and determinants
 Characterizing properties and constructions of determinants, cofactors, diagonal and triangular matrices
 More properties of determinants, an algorithm for evaluating determinants, determinants of products, inverses, and transposes, Cramer’s rule
 Eigenvalues, eigenvectors, and eigenspaces of linear operators
 Rotations and complex eigenvalues
 Diagonalizable operators and matrices
 Norm and inner products in C^{n}
and abstract inner product spaces
 Cauchy’s inequality
 Quaternions
 Cross products
Some old linear algebra tests
Web pages for related courses
Pages on the web that you may find interesting
This page is located on the web at
http://aleph0.clarku.edu/~ma130/