Manchester Postgraduate Study Fair

Wednesday 14 November 12.30pm - 3.30pm

University of Bristol Featured PhD Programmes
Norwich Research Park Featured PhD Programmes
Imperial College London Featured PhD Programmes
University of Kent Featured PhD Programmes
Max Planck Society Featured PhD Programmes

Modern Numerical Linear Algebra for Huge-Scale Optimization Problems

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Dr J Pearson
    Prof Jacek Gondzio
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

The fast and efficient solution of huge-scale optimization problems on modern computing architectures is ubiquitous in areas such as linear and quadratic programming, machine learning, data science, semidefinite programming, optimal control, design engineering, and many more. It is therefore essential to construct state-of-the-art numerical algorithms with the goal of solving the large and structured matrix systems that reveal the solution of the optimization problem as a whole.

The project supervisory team have previously demonstrated the viability of solving a number of problems of this form, using a bespoke class of mathematical solvers coupled with appropriate preconditioners. These may be embedded within suitable iterative methods to greatly accelerate the convergence of the solver, in such a way that one may solve large-scale problems that were previously beyond the capability of existing computing technologies. The aim of this project is to discover new methods for examining problems of wide scientific interest, thus opening up vast new avenues of research in this area.

During the course of study, the successful candidate will be tasked with accomplishing the following:

1. Investigating the potency of this iterative solution approach to a number of the application areas described above, by devising new preconditioners and computer storage strategies for these optimization problems.

2. Devising methods for each problem that have the potential to be applied in parallel over many computational units, using both mathematical theory and practical high performance computing.

3. Producing high quality software, in MATLAB, C++ or a similar programming language, which can be made publicly available, and therefore be readily used by experts from academia and industry.

This project is associated with the EPSRC Fellowship EP/M018857/2, ’Fast solvers for real-world PDE-constrained optimization problems’. Please see http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/M018857/2 for further details.

Funding Notes

This project is fully funded for the successful candidate by the School of Mathematics, including PhD fees, as well as a living stipend for 3.5 years, starting in September 2018 or an alternative date by mutual agreement. This studentship is available for all UK and EU students.

This project is suitable for the University of Edinburgh’s PhD programmes in Applied and Computational Mathematics (https://www.ed.ac.uk/studying/postgraduate/degrees?r=site/view&id=511&cw_xml), or Operational Research and Optimization (https://www.ed.ac.uk/studying/postgraduate/degrees?r=site/view&id=514&cw_xml). Candidates are asked to submit their applications at one of these links, according to their programme of interest.

Informal enquiries are encouraged, and may be made to [Email Address Removed].

How good is research at University of Edinburgh in Mathematical Sciences?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 56.80

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities


FindAPhD. Copyright 2005-2018
All rights reserved.

Let us know you agree to cookies

We use cookies to give you the best online experience. By continuing, we'll assume that you're happy to receive all cookies on this website. To read our privacy policy click here

Ok