Lectures on Linear Algebra¶
Introduction¶
A Primer on Linear Systems¶
Space of the column vectors over a field.
Geometric interpretation of consistent
and inconsistent systems of equations.
Transformation of a system of equations
to the echelon form by elementary operations.
Algebra of Matrices¶
Matrix as a rectangular layout of elements from a field.
Matrix addition and scalar multiplication.
The \(\,m\times n\,\) matrices over a field \(K\)
form the vector space over \(K\).
Row and column rules defining product of two matrices.
Practical vector and matrix operations in Sage.
Operations upon Matrices¶
Row-echelon and reduced row-echelon form of a matrix.
Elimination method applied to the augmented matrix
of a system of linear equations.
Transpose of a matrix and its properties.
Symmetric and skew-symmetric matrices.
Inverse of a matrix. Elementary matrices.
Permutation matrices.
A practical algorithm for the matrix inversion
by Gauss-Jordan elimination.
Determinants¶
Matrix Decompositions¶
LU decomposition of a matrix
into the product of lower- and upper-triangular factors.
Tensor (Kronecker) Product of Matrices¶
Systems of Linear Equations: Theory and Practice¶
Rank of a matrix and the Kronecker-Capelli consistency condition.
Practical implementation of the general theorems
on systems of linear equations.
An instructive example with comprehensive discussion.
Application to mechanics: Equilibrium of a set of masses on springs.
Solving systems of linear equations in Sage.
Linear Transformations¶
Properties of linear transformations.
Isomorphic vector spaces.
Matrices of linear transformations.
Change of basis and related formulae.
Eigenvalues and Eigenvectors¶
General solution of the eigenproblem in finite-dimensional vector spaces.
Application to the theory of systems
of ordinary 1st order differential equations.
Similarity and diagonalization of matrices.
Unitary Spaces¶
Inner (scalar) product in complex and real spaces.
Definition and examples of unitary (complex) and Euclidean (real) spaces.
Schwarz inequality and its specific implementations.
Orthogonality of vectors. Orthogonal complement of a subspace.
Orthogonal and orthonormal basis of a unitary space.
Gram-Schmidt method for orthonormalizing
a set of vectors and the QR decomposition.
Hermitian and unitary matrices vs Hermitian and unitary operators.
Properties of normal matrices and operators.
Diagonalization of Matrices¶
Proofs of Selected Theorems¶
Problems in Linear Algebra¶
Problems from this Chapter (or similar ones) may occur on the Exam.
