Add to ORKGIn this work we look into the parallelization (in the NC sense) of the Markov Chain approach to almost random generation (as described by Sinclair (1993)). We prove that for several problems rapid mixing i
We will look at random number generation from the point-of-view of Monte Carlo computations. Thus...
We will look at random number generation from the point-of-view of Monte Carlo computations. Thus...
We describe several RNC algorithms for generating graphs and subgraphs uniformly at random. For exam...
In this work we look into the parallelization (in the NC sense) of the Markov Chain approach to almo...
In this work we look into the parallelization (in the NC sense) of the Markov Chain approach to almo...
In this work we look into the parallelization (in the NC sense) of the Markov Chain approach to almo...
In this work we look into the parallelization (in the NC sense) of the Markov Chain approach to almo...
The paper studies effective approximate solutions to combinatorial counting and uniform generation p...
AbstractThe paper studies effective approximate solutions to combinatorial counting and unform gener...
AbstractThe paper studies effective approximate solutions to combinatorial counting and unform gener...
We give a RNC algorithm to sample matchings from a distribution on the set of matchings in a graph. ...
In the first part of this work, we present an RNC uniform generator of matchings of any size in a gr...
This monograph studies two classical computational problems: counting the elements of a finite set o...
We will look at random number generation from the point-of-view of Monte Carlo computations. Thus...
We will look at random number generation from the point-of-view of Monte Carlo computations. Thus...
We will look at random number generation from the point-of-view of Monte Carlo computations. Thus...
We will look at random number generation from the point-of-view of Monte Carlo computations. Thus...
We describe several RNC algorithms for generating graphs and subgraphs uniformly at random. For exam...
In this work we look into the parallelization (in the NC sense) of the Markov Chain approach to almo...
In this work we look into the parallelization (in the NC sense) of the Markov Chain approach to almo...
In this work we look into the parallelization (in the NC sense) of the Markov Chain approach to almo...
In this work we look into the parallelization (in the NC sense) of the Markov Chain approach to almo...
The paper studies effective approximate solutions to combinatorial counting and uniform generation p...
AbstractThe paper studies effective approximate solutions to combinatorial counting and unform gener...
AbstractThe paper studies effective approximate solutions to combinatorial counting and unform gener...
We give a RNC algorithm to sample matchings from a distribution on the set of matchings in a graph. ...
In the first part of this work, we present an RNC uniform generator of matchings of any size in a gr...
This monograph studies two classical computational problems: counting the elements of a finite set o...
We will look at random number generation from the point-of-view of Monte Carlo computations. Thus...
We will look at random number generation from the point-of-view of Monte Carlo computations. Thus...
We will look at random number generation from the point-of-view of Monte Carlo computations. Thus...
We will look at random number generation from the point-of-view of Monte Carlo computations. Thus...
We describe several RNC algorithms for generating graphs and subgraphs uniformly at random. For exam...