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Exact Aspects of Complexity Theory

Cel

The classical definition of efficient algorithms as those running in poly-time is long recognized as insufficient. In the era of big data, for example, a linear-time algorithm can be deemed feasible in situations where a quadratic one is not. On the other end of the spectrum, even modest improvements to the base of the exponent in an exp-time algorithm can dramatically expand the range of problem instances for which a solution remains viable.
This research aims to refine complexity theory by developing precise characterizations of worst-case running times for fundamental problems, identifying input instances that require that worst-case complexity, and establishing bidirectional relationships between the exact hardness of poly-time problems and NP-complete ones.
More specifically, our major objectives are (1) Refine upper bounds on the running times of algorithms for core NP-complete problems (such as SAT, Graph Coloring, Subset Sum) to better ground, sharpen, or test fine-grained complexity conjectures; (2) Characterize the structure of worst-case instances for central NP-complete problems, agnostically to the running time itself; (3) Establish a bidirectional equivalence between the exact hardness of certain poly-time problems and NP-complete ones, building on insights from fine-grained complexity that provide one-directional reductions.
Our prior work includes significant advances in the relevant areas. We achieved the first improvements in decades for the running times of key NP-complete problems such as k-SAT and Graph Coloring. Additionally, we provided foundational conditional lower bounds for important approximation and graph algorithmic problems through fine-grained complexity theory. The fusion of expertise from exact algorithms and fine-grained complexity positions us uniquely to tackle the ambitious goals above. Achieving those may establish a theory of completely tying up exact running times of efficient problems to classical conjectures such as NP!=P.

Dziedzina nauki (EuroSciVoc)

Projekt nie został jeszcze sklasyfikowany według klasyfikacji EuroSciVoc.
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System finansowania

HORIZON-ERC - HORIZON ERC Grants

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Zaproszenie do składania wniosków

(odnośnik otworzy się w nowym oknie) ERC-2026-STG

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Instytucja przyjmująca

TEL AVIV UNIVERSITY
Wkład UE netto
€ 1 500 000,00
Koszt całkowity
€ 1 500 000,00

Beneficjenci (1)