Summary
"This proposal presents a research program aimed at breaking new ground in theoretical, algorithmic and practical aspects of the notoriously difficult matching problems. The main goal is developing a unified algorithmic framework for matching problems that enjoys theoretical guarantees and high practical value for ``real-life'' applications.
The core methodological aspect is the modelling of matching problems as high-dimensional, lifted convex problems that can be efficiently approximated. We present several case-studies of this methodology, constructing efficient algorithms for approximating the solutions of important instances of the matching problem. The results already demonstrate state of the art performance and are backed-up with novel theoretical analysis proving tightness and correctness of the suggested convexifications for certain classes of non-trivial input.
This proposal has ``high risk - high impact'' profile: The ``high-risk"" aspect of this proposal comes from the hardness of general matching problems which, when faithfully represented, are NP-hard. However, as we demonstrate, combining convex optimization tools in a careful way, that takes into account computational complexity, is likely to push forward the limits of current algorithmic solutions. The ``High-impact"" will be immediate: As matching problems exist in almost every aspect of scientific research, practical generic algorithms for matching problems are likely to have a significant influence.
We believe that the inroads and preliminary results presented in this proposal already provide strong evidence for the potential success of the suggested research program."
The core methodological aspect is the modelling of matching problems as high-dimensional, lifted convex problems that can be efficiently approximated. We present several case-studies of this methodology, constructing efficient algorithms for approximating the solutions of important instances of the matching problem. The results already demonstrate state of the art performance and are backed-up with novel theoretical analysis proving tightness and correctness of the suggested convexifications for certain classes of non-trivial input.
This proposal has ``high risk - high impact'' profile: The ``high-risk"" aspect of this proposal comes from the hardness of general matching problems which, when faithfully represented, are NP-hard. However, as we demonstrate, combining convex optimization tools in a careful way, that takes into account computational complexity, is likely to push forward the limits of current algorithmic solutions. The ``High-impact"" will be immediate: As matching problems exist in almost every aspect of scientific research, practical generic algorithms for matching problems are likely to have a significant influence.
We believe that the inroads and preliminary results presented in this proposal already provide strong evidence for the potential success of the suggested research program."
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/771136 |
| Start date: | 01-03-2018 |
| End date: | 28-02-2023 |
| Total budget - Public funding: | 1 675 640,00 Euro - 1 675 640,00 Euro |
Cordis data
Original description
"This proposal presents a research program aimed at breaking new ground in theoretical, algorithmic and practical aspects of the notoriously difficult matching problems. The main goal is developing a unified algorithmic framework for matching problems that enjoys theoretical guarantees and high practical value for ``real-life'' applications.The core methodological aspect is the modelling of matching problems as high-dimensional, lifted convex problems that can be efficiently approximated. We present several case-studies of this methodology, constructing efficient algorithms for approximating the solutions of important instances of the matching problem. The results already demonstrate state of the art performance and are backed-up with novel theoretical analysis proving tightness and correctness of the suggested convexifications for certain classes of non-trivial input.
This proposal has ``high risk - high impact'' profile: The ``high-risk"" aspect of this proposal comes from the hardness of general matching problems which, when faithfully represented, are NP-hard. However, as we demonstrate, combining convex optimization tools in a careful way, that takes into account computational complexity, is likely to push forward the limits of current algorithmic solutions. The ``High-impact"" will be immediate: As matching problems exist in almost every aspect of scientific research, practical generic algorithms for matching problems are likely to have a significant influence.
We believe that the inroads and preliminary results presented in this proposal already provide strong evidence for the potential success of the suggested research program."
Status
TERMINATEDCall topic
ERC-2017-COGUpdate Date
27-04-2024
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