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Sublinear Algorithms for Modern Data Analysis

Designing efficient algorithms for fundamental computational tasks as well as understanding the limits of tractability has been the goal of computer science since its inception. Polynomial runtime has been the de facto notion of e... ver más

31/08/2023

EPFL

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-08-31

ERC-2017-STG: ERC Starting Grant

I+D

Cerrada hace 8 años

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

1 Participantes

EPFL

1.47M€ | Lider

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Duración del proyecto: 72 meses Fecha Inicio: 2017-08-30
Fecha Fin: 2023-08-31

Líder del proyecto

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE 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 Designing efficient algorithms for fundamental computational tasks as well as understanding the limits of tractability has been the goal of computer science since its inception. Polynomial runtime has been the de facto notion of efficiency since the introduction of the notion of NP-completeness. As the sizes of modern datasets grow, however, many classical polynomial time (and sometimes even linear time) solutions become prohibitively expensive. This calls for sublinear algorithms, i.e. algorithms whose resource requirements are substantially smaller than the size of the input that they operate on. We propose to design a toolbox of powerful algorithmic techniques with sublinear resource requirements that will form the theoretical foundation of large data analysis, as well as develop a nuanced runtime, space and communication complexity theory to show optimality of our algorithms. Specifically, we propose to: 1. design an algorithmic toolkit for sublinear graph processing that will form the basis of large scale graph analytics; 2. design a new generation of sublinear algorithms for signal processing that will become the method of choice for a wide range of applications; 3. develop a far-reaching set of techniques for lower bounding runtime, space and communication complexity of sublinear algorithms. The problems that we propose to solve are among the most fundamental algorithmic questions on the forefront of the rapidly developing algorithmic theory of large data analysis, which has been the focus of an extensive body of work in the research community. The algorithms and complexity theoretic results that we propose to design will cut at the core of fundamental computational problems and form the theoretical foundation of computing with constrained resources.

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