# Efficient linear algebra for block structured matrices

## Supervisor

## Suitable for

## Abstract

Many large systems of linear equations are sparse, i.e. if the matrix that describes the linear system is of size N times N, where N is large, there are very few non-zero entries in each row. Under these conditions there may not be enough memory to store the whole matrix, and so only the non-zero entries are stored. This prevents techniques such as LU decomposition being used to solve the linear system; instead an iterative technique such as the conjugate gradient technique for symmetric positive definite matrices, or GMRES for more general matrices is used. The number of iterations needed with these techniques can be large, rendering these techniques inefficient. To prevent this, preconditioning techniques are used - if the linear system is defined by Ax=b, then a preconditioner P is used and the system solved is instead PAx = Pb, where P is cheap to calculate and both PAx and Pb are cheap to evaluate. In this project we will investigate matrices with a block structure that arises in many fields, such as constrained optimisation and continuum mechanics. We will utilise the block structure of these matrices to heuristically derive candidate preconditioners, and compare their performances.

Prerequisites: linear algebra, continuous mathematics