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Asaf Nachmias

dblp:81/2538 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2010
0000-0002-4852-5645ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
2 papers
Algorithms and data structures · 44% Computational complexity · 34% Graph algorithms and graph theory · 22%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational complexity › property testing › graph property testing
expansion testing
0.112010
Testing the expansion of a graph · Inf. Comput. 2010
Algorithms and data structures › randomized algorithms › sampling
markov chain monte carlo
0.112007
Mixing Time Power Laws at Criticality · FOCS 2007
Algorithms and data structures › markov chains
mixing time
0.112007
Mixing Time Power Laws at Criticality · FOCS 2007
Graph algorithms and graph theory
percolation
0.112007
Mixing Time Power Laws at Criticality · FOCS 2007

Methods — techniques the papers use, named apart from their topics

swendsen-wang dynamics · 0.1random walk analysis · 0.1
YearPublicationVenuePosition
2010 Testing the expansion of a graph
Asaf Nachmias, Asaf Shapira
Inf. Comput.1
2007 Mixing Time Power Laws at Criticality
abstract
We study the mixing time of some Markov chains converging to critical physical models. These models are indexed by a parameter beta and there exists some critical value betacwhere the model undergoes a phase transition. According to physics lore, the mixing time of such Markov chains is often of logarithmic order outside the critical regime, when beta ne betac, and satisfies-some power law at criticality, when beta = betac. We prove this in the two following settings: 1. Lazy random walk on the critical percolation cluster of "mean-field" graphs, which include the complete graph and random d-regular graphs. The critical mixing time here is of order Theta(n). This answers a question of Benjamini, Kozma and Wormald. 2. Swendsen-Wang dynamics on the complete, graph. The critical mixing time, here is of order Theta(n1/4). This improves results of Cooper, Dyer, Frieze and Rue. In both settings, the main tool is understanding the Markov chain dynamics via properties of critical percolation on the underlying graph.
Asaf Nachmias, Yuval Peres
FOCS2