Summary
In our world of big data and theoretically intractable problems, automated preprocessing to simplify problem formulations before solving them algorithmically is growing ever more important. Suitable preprocessing has the potential to reduce computation times from days to seconds. In the last 15 years, a framework for rigorously studying the power and limitations of efficient preprocessing has been developed. The resulting theory of kernelization is full of deep theorems, but it has overshot its goal: it does not explain the empirical success of preprocessing algorithms, and most questions it poses do not lead to the identification of preprocessing techniques that are useful in practice. This crucial flaw stems from the fact that the theoretical kernelization framework does not address the main experimentally observed cause of algorithmic speed-ups: a reduction in the search space of the subsequently applied problem-solving algorithm.
REDUCESEARCH will re-shape the theory of effective preprocessing with a focus on search-space reduction. The goal is to develop a toolkit of algorithmic preprocessing techniques that reduce the search space, along with rigorous mathematical guarantees on the amount of search-space reduction that is achieved in terms of quantifiable properties of the input. The three main algorithmic strategies are: (1) reducing the size of the solution that the solver has to find, by already identifying parts of the solution during the preprocessing phase; (2) splitting the search space into parts with limited interaction, which can be solved independently; and (3) identifying redundant constraints and variables in a problem formulation, which can be eliminated without changing the answer.
This will raise the scientific study of preprocessing to the next level. Since physical limits form a barrier to further speeding up computer hardware, future advances in computing power rely on algorithmic breakthroughs as envisioned here.
REDUCESEARCH will re-shape the theory of effective preprocessing with a focus on search-space reduction. The goal is to develop a toolkit of algorithmic preprocessing techniques that reduce the search space, along with rigorous mathematical guarantees on the amount of search-space reduction that is achieved in terms of quantifiable properties of the input. The three main algorithmic strategies are: (1) reducing the size of the solution that the solver has to find, by already identifying parts of the solution during the preprocessing phase; (2) splitting the search space into parts with limited interaction, which can be solved independently; and (3) identifying redundant constraints and variables in a problem formulation, which can be eliminated without changing the answer.
This will raise the scientific study of preprocessing to the next level. Since physical limits form a barrier to further speeding up computer hardware, future advances in computing power rely on algorithmic breakthroughs as envisioned here.
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| Web resources: | https://cordis.europa.eu/project/id/803421 |
| Start date: | 01-01-2019 |
| End date: | 31-12-2023 |
| Total budget - Public funding: | 1 473 020,00 Euro - 1 473 020,00 Euro |
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Original description
In our world of big data and theoretically intractable problems, automated preprocessing to simplify problem formulations before solving them algorithmically is growing ever more important. Suitable preprocessing has the potential to reduce computation times from days to seconds. In the last 15 years, a framework for rigorously studying the power and limitations of efficient preprocessing has been developed. The resulting theory of kernelization is full of deep theorems, but it has overshot its goal: it does not explain the empirical success of preprocessing algorithms, and most questions it poses do not lead to the identification of preprocessing techniques that are useful in practice. This crucial flaw stems from the fact that the theoretical kernelization framework does not address the main experimentally observed cause of algorithmic speed-ups: a reduction in the search space of the subsequently applied problem-solving algorithm.REDUCESEARCH will re-shape the theory of effective preprocessing with a focus on search-space reduction. The goal is to develop a toolkit of algorithmic preprocessing techniques that reduce the search space, along with rigorous mathematical guarantees on the amount of search-space reduction that is achieved in terms of quantifiable properties of the input. The three main algorithmic strategies are: (1) reducing the size of the solution that the solver has to find, by already identifying parts of the solution during the preprocessing phase; (2) splitting the search space into parts with limited interaction, which can be solved independently; and (3) identifying redundant constraints and variables in a problem formulation, which can be eliminated without changing the answer.
This will raise the scientific study of preprocessing to the next level. Since physical limits form a barrier to further speeding up computer hardware, future advances in computing power rely on algorithmic breakthroughs as envisioned here.
Status
SIGNEDCall topic
ERC-2018-STGUpdate Date
27-04-2024
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