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New Horizons of Parameterized Complexity

Project description

New ground in algorithmic complexity for geometric and algebraic problems

Classic algorithm analysis often struggles to predict performance beyond the worst-case scenario, especially for complex problems involving geometric and algebraic data, including points, vectors, and matrices. While parameterised complexity (PC) has improved understanding in discrete areas such as graphs and Boolean formulas, it has yet to make a big impact on continuous optimisation problems. In this context, the ERC-funded NewPC project will create algorithms that consider multiple key factors influencing their efficiency and accuracy. Specifically, it will rethink tools, including dimension reduction and clustering, through the lens of parameterised complexity. This approach could transform how algorithms are designed in fields ranging from computational geometry and machine learning to optimisation.

Objective

Parameterized Complexity (PC) provides an approach to moving beyond classic worst-case analysis of algorithms by focusing on multivariate algorithms whose performance is influenced by several parameters beyond input size. While it has been successful in various algorithmic domains, particularly those involving discrete objects like graphs, sets, matroids, or Boolean formulas, PC remains underutilized in areas involving geometric and algebraic objects such as points, vectors, linear spaces, or matrices. This project seeks to revolutionize the PC field by developing novel multivariate algorithms for continuous optimization problems involving such objects. The performance of these algorithms—whether measured by approximation ratio, probability error, or running time—is highly sensitive to multiple parameters.

The main challenge in advancing parameterized complexity to these new domains is the need for suitable algorithmic and complexity tools. To address this challenge, the NewPC project proposes to revisit and revise fundamental methods for handling geometric and algebraic objects—such as dimension reduction, sampling, sketching, clustering, and coresets—through the conceptual questions of PC, including determining proper parameterization, quantifying the distance from triviality, and identifying when kernelization is possible.
In short, the proposed Research Program can be summarized as:

Questions of PC + Geometric Methods = New Horizons

The proposed research will open new horizons in parameterized complexity by

- Advancing multivariate approximation and lossy kernelization, both emerging areas within parameterized complexity.

- Establishing multivariate algorithmic analysis as the standard tool for designing provably correct and efficient algorithms in domains such as computational geometry, machine learning, robust statistics, and optimization—areas where the classical worst-case algorithmic analysis often falls short.

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2024-ADG

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Host institution

UNIVERSITETET I BERGEN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 2 500 000,00
Address
MUSEPLASSEN 1
5020 Bergen
Norway

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Region
Norge Vestlandet Vestland
Activity type
Higher or Secondary Education Establishments
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Total cost

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Beneficiaries (1)

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