EPSRC Reference: 
EP/M025179/1 
Title: 
Least Squares: Fit for the Future 
Principal Investigator: 
Scott, Professor JA 
Other Investigators: 

Researcher CoInvestigators: 

Project Partners: 

Department: 
Scientific Computing Department 
Organisation: 
STFC Laboratories (Grouped) 
Scheme: 
Standard Research 
Starts: 
01 October 2015 
Ends: 
30 September 2020 
Value (£): 
970,774

EPSRC Research Topic Classifications: 

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Related Grants: 

Panel History: 
Panel Date  Panel Name  Outcome 
03 Mar 2015

EPSRC Mathematics Prioritisation Panel March 2015

Announced


Summary on Grant Application Form 
This project seeks to develop new algorithms, supporting theory and software for solving least squares problems that arise in science, engineering, planning and economics. Least squares involves finding an approximate solution of overdetermined or inexactly specified systems of equations. Reallife applications abound. Weather forecasters want to produce more accurate forecasts; climatologists want a better understanding of climate change; medics want to produce more accurate images in real time; financiers want to analyse and quantify the systematic risk of an investment by fitting a capital asset pricing model to observed financial data. Finding the 'best' solution commonly involves constructing a mathematical model to describe the problem and then fitting this model to observed data. Such models are usually complicated; models with millions of variables and restrictions are not uncommon, but neither are relatively small but fiendishly difficult ones. It is therefore imperative to implement the model on a computer and to use computer algorithms for solving it. The latter task is at the core of the proposed activities.
Nearly all such largescale problems exhibit an underlying mathematical structure such as sparsity. That is to say, the interactions between the parameters of a large system are often localised and do not involve any direct interaction between all the components. To solve the systems and models represented in this way efficiently involves developing algorithms that are able to exploit these underlying 'simpler' structures, which often reduces the scale of the problems, and thus speeds up their solution. This enterprise commonly leads not only to new software that implements existing methods, but to the creation of new theoretical and practical algorithms. At the other extreme, some problems involve interaction between all components, and while the underlying structure is less transparent, it is nonetheless present. In these cases, the computational burden may be very high and such problems may generally only be solved by sophisticated use of massively parallel computers.
The methods we will develop will aim to solve the given problem efficiently and robustly. Since computers cannot solve most mathematical problems exactly, only approximately, a priority will be to ensure the solution obtained by applying our algorithms is highly accurate, that is, close to the 'true' solution of the problem. But it is also vital that we solve problems fast without sacrificing accuracy; this is particularly true if a simulation requires us to investigate a large number of different scenarios, or if the problem we seek to solve is simply a component in an overall vastly more complicated computation, or if new data arrives in real time and we need to adapt the model accordingly. Developing algorithms that are both fast and accurate on multicore machines presents a key challenge.
We aim to improve upon existing algorithms from several different angles, exploiting new mathematical techniques from areas such as optimization and the solution of partial differential equations. Parallelism will be designed into our new algorithms, allowing modern computer hardware to be exploited. These generic improvements will be coupled with the development of new techniques to exploit special features of problems from important application areas, including Xray microscopy, crystallography and radiative transfer modelling.
Our new software will be made available through the internationally renowned mathematical software libraries HSL, GALAHAD and SPRAL. These are extensively used by the scientific and engineering research community in the UK and abroad, as well as by some commercial companies. Since 2010, more than 50 UK university departments have used HSL for teaching or research in a wide range of disciplines that includes computational chemistry, engineering design, fluid dynamics, portfolio optimization, and circuit theory.

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