This module introduces and develops fluency with lines and planes in space, represented by vector and Cartesian equations. Students learn how to exploit the arithmetic of geometric vectors to solve difficult optimisation problems such as finding the closest point to a plane or finding the closest points on a pair of skew lines. The module introduces systems of linear equations and then develops the method of Gaussian elimination, using elementary row operations, followed by back substitution, to express solutions in terms of parameters. Systems of equations are also explored and solved using modular arithmetic with respect to a given prime number.

This module introduces matrix arithmetic and the theory of determinants. Students first learn how to add matrices of the same size and how to multiply by a scalar. They then learn how to multiply matrices of compatible sizes, using cascades of dot products of rows with columns. This is described succinctly using Sigma notation, which is then used to prove associativity of matrix multiplication. Students learn about elementary matrices, which are basic building blocks in matrix arithmetic, closely related to elementary row operations used in Gaussian elimination. Students learn about matrix inverses and how to find them. Students learn about determinants and their properties, including the multiplicative property and a simple criterion for recognising invertibility of a matrix. Students see applications to cross products of vectors and for exploring spatial relationships between points and triangles.

This module continues the development of matrix arithmetic by introducing eigenvalues and associated eigenvectors. Interpreted geometrically, these allow one to find directions in which a given linear operator associated with a matrix moves vectors in straight lines. This leads to the technique of diagonalisation, enabling one to solve difficult problems in matrix arithmetic, including finding formulae for powers of a given square matrix. This has many applications, including, for example, in exploring the behaviour of a Markov process described by a stochastic matrix, the mathematics of which underlies search engines on the internet. Eigenvalues can be found, in principle, by solving the characteristic equation of a matrix. Associated eigenvectors can then be found by solving an associated homogeneous system of equations. In practice, there are iterative numerical techniques for finding approximations of eigenvalues and eigenvectors, using a technique associated with Perron’s Theorem. Diagonalisation is a manifestation of the general Conjugation Principle, explored in different contexts. Linear transformations are introduced, focusing on transformation of the plane. Rotations and reflections of the plane combine to form the two-dimensional orthogonal group. Scalar dilations and rotations combine to form a copy of the field of complex numbers. A sketch of Smale’s proof of the Fundamental Theorem of Algebra is given, which says that any nonconstant polynomial with complex coefficients has a complex root, so that all square matrices have eigenvalues, when working over the field of complex numbers.