Descrizione del progetto
Promuovere la ricerca della simmetria
L’individuazione della simmetria in sistemi di dati ampi e complessi è sempre più importante nell’informatica. Che si stiano cercando equazioni matematiche, grafici, mappe stradali o il modo in cui evolvono i social network, sono presenti alcuni schemi ripetitivi che possono essere identificati e impiegati per rendere il calcolo più efficiente. Soprattutto, l’individuazione della simmetria può contribuire a evitare duplicati e a identificare schemi in sistemi molto complessi, quali le reti neurali impiegate nell’apprendimento automatico o in ampie banche dati, come quelle che elencano le molecole chimiche. Lo sviluppo di algoritmi efficienti per l’individuazione della simmetria è anche noto come il problema dell’isomorfismo tra grafi, uno dei principali quesiti irrisolti nel campo dell’informatica teorica. Il progetto EngageS, finanziato dall’UE, svilupperà modelli teorici e strumenti software per individuare efficientemente la simmetria.
Obiettivo
Symmetry is a phenomenon that appears in many different contexts.
Algorithmic symmetry detection and exploitation is the concept of finding intrinsic symmetries of a given object and then using these symmetries to our advantage. Application areas of algorithmic symmetry detection and exploitation range from convolutional neural networks in machine learning to computer graphics, chemical data bases and beyond.
In contrast to this widespread use, our understanding of the theoretical foundation (namely the graph isomorphism problem) is incomplete and current algorithmic symmetry tools are inadequate for big data applications. Hence, EngageS addresses these key challenges in the field using a systematic approach to the theory and practice of symmetry detection. It thereby also fixes the existing lack of interplay between theory and practice, which is part of the problem.
EngageS' main aims are to tackle the classical and descriptive complexity of the graph isomorphism problem and to design the next generation of symmetry detection algorithms. As key ideas to resolve the complexity, EngageS offers three new approaches on how to prove lower bounds and a new method to settle the descriptive complexity.
EngageS will also develop practical symmetry detection algorithms for big data, exploiting parallelism and memory hierarchies of modern machines, and will introduce the concept of and a road map to exploiting absence of symmetry. Overall EngageS will establish a comprehensive software library that will serve as a platform for integrated research on the algorithmic treatment of symmetry.
In summary, EngageS will develop fast, efficient and accessible symmetry detection tools that will be used to solve complex algorithmic problems in a range of fields including combinatorial algorithms, generation problems, and canonization.
Campo scientifico
- natural sciencescomputer and information sciencessoftware
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-COG - Consolidator GrantIstituzione ospitante
64289 Darmstadt
Germania