| EPSRC Reference: |
EP/P021298/1 |
| Title: |
PARTIAL Analysis of Relations in Tasks of Inversion for Algorithmic Leverage |
| Principal Investigator: |
Valkonen, Dr TJM |
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| Department: |
Mathematical Sciences |
| Organisation: |
University of Liverpool |
| Scheme: |
First Grant - Revised 2009 |
| Starts: |
26 May 2017 |
Ends: |
25 November 2018 |
Value (£): |
101,042
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Summary on Grant Application Form |
The solution of inverse problems forms a crucial part of many modern technologies, such as medical and astronomical imaging devices. We want to obtain from possibly very limited and corrupt measurements, a good description of the phenomenon under study. Often this description takes the form of an image, as we humans like to process visual information, but it can also take many other forms, such as the location of an earthquake, the source of a sound, or the detection of a planet.
As physical technologies improve, and become more widespread, we are faced with increased amounts of data, as well as the need to process them into higher-quality results, such as higher-resolution images. We would also like to produce the results with as little energy as possible, and ideally in real time, instead of waiting computers to crunch numbers for hours after the measurement was taken. For this we need really good optimisation algorithms: instructions for a computer to find the point where a mathematical function reaches its smallest value, akin to the lowest part of a valley among mountains. This is because the solutions we are looking are such minimising points of suitable functions modelling our inverse problem.
The objective of the proposed project is to develop efficient optimisation algorithms for the solution of large-scale inverse problems. This will be based on a study to gain a better understanding into the stability of the solutions to these problems under perturbations of the measurements and the parameters of the model. Besides inverse problems, our work will be applicable to a diverse range of disciplines from financial planning to machine learning.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.liv.ac.uk |