The Power of Randomness and Continuity in Submodular Optimization

Informazioni relative al progetto

SUBMODULAR

ID dell’accordo di sovvenzione: 852780

DOI

10.3030/852780

Progetto chiuso

Data della firma CE 8 Ottobre 2019

Data di avvio 1 Novembre 2019

Data di completamento 31 Ottobre 2025

Finanziato da

• EXCELLENT SCIENCE - European Research Council (ERC)

Costo totale

€ 1 442 690,00

Contributo UE

€ 1 442 690,00

Coordinato da

TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY
Israel

Periodic Reporting for period 4 - SUBMODULAR (The Power of Randomness and Continuity in Submodular Optimization)

Periodo di rendicontazione: 2024-05-01 al 2025-10-31

Sintesi del contesto e degli obiettivi generali del progetto

This research project deals with the theoretical foundations of submodular maximization in particular and submodular optimization in general.
Submodular optimization affects many aspects of our daily lives, and has found applications ranging from pollution detection, the spread of influence in social networks, and even reduction of gang violence.
Thus, it is no surprise that optimization problems with a submodular objective have been the focus of intense theoretical and practical research for more than a decade.
The overarching goal of this research is to enrich the algorithmic toolkit and devise new algorithmic approaches that can be broadly applied to fundamental problems in submodular and combinatorial optimization.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

The study of new methods for designing and analyzing combinatorial algorithms for submodular maximization problems, as well as continuous and randomized methods for submodular maximization, have been performed.
Two examples includes the maximization of a submodular function given a knapsack constraint (a classic optimization problem which has found numerous practical applications throughout the years) and the use of a Poisson process for submodular maximization over a matroid independence constraint.
Moreover, problems relating to submodularity and other ascepts, e.g. fault tolerance and clustering, ordering, resource allocation, online, and other computational settings have also been studied.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

We plan to study how randomness and continuous approaches can be used for submodular maximization and more broadly applied to fundamental problems in combinatorial optimization.
The ultimate goal is to present such new algorithmic approaches that will enable us to resolve basic problems in the area and provide new algorithmic foundations to submodular optimization.

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