Course content

The course is a continuation of MA1201. We start with general vector spaces over the real and complex numbers, and linear maps (including related subspaces – kernel and image – and representations in matrix form given bases). We study operators on finite dimensional vector spaces by looking at eigenvectors, eigenspaces, generalized eigenspaces, aiming for the Cayley-Hamilton theorem and normal forms. Inner product spaces are a concept generalizing the dot product. Studying these, both over the real and complex numbers, is an important part of this course. Orthogonal bases are constructed by using the Gram Schmidt algorithm. Then various types of operators on inner product spaces are studied (orthogonal, real symmetric, unitary, normal, self-adjoint), including the corresponding matrices. The course can contain more advanced concepts from linear algebra, such as dual spaces, bilinear forms and quotient spaces. Several applications are illustrated; these may vary from year to year. Examples: Markov chains, population growth (Leslie matrices), game theory, systems of differential equations, Fourier analysis, and fractals.

Learning outcome

1. Knowledge. The student is familiar with basic concepts concerning general vector spaces, matrices and linear transformations as discussed above. The student is familiar with several applications of linear algebra. 2. Skills. The student masters various algorithms and methods to make calculations involving vector spaces, inner product spaces, and linear transformations. Central skills are applying the Gram-Schmidt algorithm, finding eigenspaces, diagonalizing matrices, and applications varying from year to year. The student is able to write simple mathematical proofs.

Learning methods and activities

Lectures and exercises. Parts of this course can be taught in English.

Compulsory assignments

• Exercises

Recommended previous knowledge

MA1201 Linear algebra and geometry.

Course materials

Will be announced at the start of the semester.

Credit reductions