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ReduceSearch

Financiado

Cerrado

Rigorous Search Space Reduction

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... ver más

31/12/2023

TU/e

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

TECHNISCHE UNIVERSITEIT EINDHOVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-12-31

ERC-2018-STG: ERC Starting Grant

I+D

Cerrada hace 7 años

0% 100% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

TU/e

1.47M€ | Lider

MEMBRILLO EMILY

217.35K€ | Participante

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Duración del proyecto: 63 meses Fecha Inicio: 2018-09-04
Fecha Fin: 2023-12-31

Líder del proyecto

TECHNISCHE UNIVERSITEIT EINDHOVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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