Fast bicriteria streaming algorithms for submodular cover problem under noise models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10256810" target="_blank" >RIV/61989100:27240/25:10256810 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0920548924000527?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0920548924000527?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csi.2024.103883" target="_blank" >10.1016/j.csi.2024.103883</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast bicriteria streaming algorithms for submodular cover problem under noise models

Popis výsledku v původním jazyce

The Submodular Cover (SC) problem has attracted the attention of researchers because of its wide variety of applications in many domains. Previous studies on this problem have focused on solving it under the assumption of a non-noise environment or using the greedy algorithm to solve it under noise. However, in some applications, the data is often large-scale and brings a noisy version, so the existing solutions are ineffective or not applicable to large and noisy data. Motivated by this phenomenon, we study the Submodular Cover under Noises (SCN) problem and propose two efficient streaming algorithms, which provide a solution with theoretical bounds under two common noise models, multiplicative and additive noises. The experimental results indicate that our proposed algorithms not only provide the solution with a high objective function value but also outperform the state-of-the-art algorithm in terms of both the number of queries and the running time.

Název v anglickém jazyce

Fast bicriteria streaming algorithms for submodular cover problem under noise models

Popis výsledku anglicky

The Submodular Cover (SC) problem has attracted the attention of researchers because of its wide variety of applications in many domains. Previous studies on this problem have focused on solving it under the assumption of a non-noise environment or using the greedy algorithm to solve it under noise. However, in some applications, the data is often large-scale and brings a noisy version, so the existing solutions are ineffective or not applicable to large and noisy data. Motivated by this phenomenon, we study the Submodular Cover under Noises (SCN) problem and propose two efficient streaming algorithms, which provide a solution with theoretical bounds under two common noise models, multiplicative and additive noises. The experimental results indicate that our proposed algorithms not only provide the solution with a high objective function value but also outperform the state-of-the-art algorithm in terms of both the number of queries and the running time.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computer Standards and Interfaces

ISSN

0920-5489

e-ISSN

1872-7018

Svazek periodika

91

Číslo periodika v rámci svazku

leden 2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

nestránkováno

Kód UT WoS článku

001265395800001

EID výsledku v databázi Scopus

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