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NACS

Financiado

New Approaches to Counting and Sampling

Probabilistic models have been adopted in almost all scientific disciplines. Many related computational problems arise, involving estimating the probability of certain events, or drawing samples from a desired distribution. These... ver más

30/06/2026

UEDIN

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

THE UNIVERSITY OF EDINBURGH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-07-31

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2020-STG: ERC STARTING GRANTS

I+D

Cerrada hace 5 años

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2 Participantes

UEDIN

1.47M€ | Lider

ALARIO OBRA CIVIL, S.L.

163.93K€ | Participante

Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 70 meses Fecha Inicio: 2020-07-31
Fecha Fin: 2026-06-30

Líder del proyecto

THE UNIVERSITY OF EDINBURGH

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto Probabilistic models have been adopted in almost all scientific disciplines. Many related computational problems arise, involving estimating the probability of certain events, or drawing samples from a desired distribution. These problems, technically known as counting and sampling problems, are the main focus of the NACS project, with an emphasis on rigorous mathematical analysis. Markov chain Monte Carlo algorithms have been designed and applied for counting and sampling long before the computational complexity theory, and they are among the oldest randomised algorithms. There had been dramatic progress in the rigorous analysis of Markov chains in the golden era of early 90s, which has lead to many landmark results. Nevertheless, after about 30 years' development, the complexity of many fundamental problems remains open. In the last few years, a number of exciting approaches have emerged, resulting from new perspectives and surprising connections. Consequently, a lot of old challenges start to crumble. It is now the right time to revisit some of the oldest problems in counting complexity. The emerging techniques include brand new sampling frameworks, such as partial rejection sampling, as well as new ways to analyse traditional algorithms, especially Markov chain algorithms. They can be classified under two themes: those connected with the Lovasz local lemma and those with the geometry of polynomials. The goal of this project is to unleash the full power of the new approaches, to establish novel algorithmic paradigms, and to attack major open problems, such as sampling perfect matchings in general graphs. Due to the fundamental nature of counting and sampling problems, success of the project will also benefit a number of related areas, ranging from combinatorial optimisation, machine learning, and randomised algorithms, to combinatorics, probability theory, statistical physics, and quantum computation.

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