Pastore, Tommaso (2018) Logic learning and optimized drawing: two hard combinatorial problems. [Tesi di dottorato]

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Tipologia del documento: Tesi di dottorato
Lingua: English
Titolo: Logic learning and optimized drawing: two hard combinatorial problems
Autori:
Autore
Email
Pastore, Tommaso
tommaso.pastore@unina.it
Data: 10 Dicembre 2018
Istituzione: Università degli Studi di Napoli Federico II
Dipartimento: Matematica e Applicazioni "Renato Caccioppoli"
Dottorato: Scienze matematiche e informatiche
Ciclo di dottorato: 31
Coordinatore del Corso di dottorato:
nome
email
De Giovanni, Francesco
francesco.degiovanni2@unina.it
Tutor:
nome
email
Festa, Paola
[non definito]
Data: 10 Dicembre 2018
Parole chiave: Combinatorial Optimization; Metaheuristic Algorithms; Graph Drawing Problems
Settori scientifico-disciplinari del MIUR: Area 01 - Scienze matematiche e informatiche > MAT/09 - Ricerca operativa
Depositato il: 19 Dic 2018 09:15
Ultima modifica: 26 Giu 2020 08:37
URI: http://www.fedoa.unina.it/id/eprint/12550

Abstract

Nowadays, information extraction from large datasets is a recurring operation in countless fields of applications. The purpose leading this thesis is to ideally follow the data flow along its journey, describing some hard combinatorial problems that arise from two key processes, one consecutive to the other: information extraction and representation. The approaches here considered will focus mainly on metaheuristic algorithms, to address the need for fast and effective optimization methods. The problems studied include data extraction instances, as Supervised Learning in Logic Domains and the Max Cut-Clique Problem, as well as two different Graph Drawing Problems. Moreover, stemming from these main topics, other additional themes will be discussed, namely two different approaches to handle Information Variability in Combinatorial Optimization Problems (COPs), and Topology Optimization of lightweight concrete structures.

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