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Projekt Druckansicht

Approximationsalgorithmen für Geometrische Optimierungsprobleme

Antragsteller Dr. Morteza Monemizadeh
Fachliche Zuordnung Theoretische Informatik
Förderung Förderung von 2013 bis 2016
Projektkennung Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 228173343
 
Erstellungsjahr 2021

Zusammenfassung der Projektergebnisse

Not all the problems that I defined in this grant were solved. As an example, one of the major problems which remained open was to understand and exploit the connections between streaming, sublinear and approximation algorithms not only in the context of geometric optimization problems, but in general for graph problems. We partially solved this problem. In particular we studied which property testing and sublinear time algorithms can be transformed into graph streaming algorithms for random order streams. Our main result was that for bounded degree graphs, any property that is constant-query testable in the adjacency list model can be tested with constant space in a single-pass in random order streams. Our result was obtained by estimating the distribution of local neighborhoods of the vertices on a random order graph stream using constant space. We then showed that our approach can also be applied to constant time approximation algorithms for bounded degree graphs in the adjacency list model: As an example, we obtained a constant-space single-pass random order streaming algorithms for approximating the size of a maximum matching with additive error n (n is the number of nodes). Our result established for the first time that a large class of sublinear algorithms can be simulated in random order streams, while Ω(n) space is needed for many graph streaming problems for adversarial orders.

Projektbezogene Publikationen (Auswahl)

 
 

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