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Electronic Colloquium on Computational Complexity

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REPORTS > KEYWORD > #P-COMPLETENESS:
Reports tagged with #P-completeness:
TR05-051 | 18th March 2005
Predrag Tosic

On Complexity of Counting Fixed Points in Certain Classes of Graph Automata

Revisions: 2

We study the computational complexity of counting the fixed point configurations in certain discrete dynamical systems. We prove that both exact and approximate counting in Sequential and Synchronous Dynamical Systems (SDSs and SyDS, respectrively) is computationally intractable, even when each node is required to update according to a symmetric Boolean ... more >>>


TR05-091 | 11th August 2005
Predrag Tosic

Counting Fixed Points and Gardens of Eden of Sequential Dynamical Systems on Planar Bipartite Graphs

Revisions: 1

We study counting various types of con gurations in certain classes of graph
automata viewed as discrete dynamical systems. The graph automata models
of our interest are Sequential and Synchronous Dynamical Systems (SDSs and
SyDSs, respectively). These models have been proposed as a mathematical foun-
dation for a theory of ... more >>>


TR06-159 | 3rd October 2006
Predrag Tosic

Computational Complexity of Some Enumeration Problems About Uniformly Sparse Boolean Network Automata

We study the computational complexity of counting the fixed point configurations (FPs), the predecessor configurations and the ancestor configurations in certain classes of graph or network automata viewed as discrete dynamical systems. Early results of this investigation are presented in two recent ECCC reports [39, 40]. In particular, it is ... more >>>


TR23-145 | 20th September 2023
Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran

Total Variation Distance Estimation Is as Easy as Probabilistic Inference

In this paper, we establish a novel connection between total variation (TV) distance estimation and probabilistic inference. In particular, we present an efficient, structure-preserving reduction from relative approximation of TV distance to probabilistic inference over directed graphical models. This reduction leads to a fully polynomial randomized approximation scheme (FPRAS) for ... more >>>




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