The framework to unify all complexity dichotomy theorems for Boolean tensor networks
Mingji Xia

TL;DR
This paper introduces a comprehensive framework to unify all complexity dichotomy theorems for Boolean tensor network counting problems, focusing on group-theoretic classifications of functions to understand problem complexity.
Contribution
It proposes a novel framework based on group classifications to unify and extend existing dichotomy theorems for Boolean tensor network problems.
Findings
Classifies unsolved problems into 9 group categories
Simplifies matrix forms using transposition closure property
Resolves higher-order cyclic group cases
Abstract
Fixing an arbitrary set of complex-valued functions over Boolean variables yields a counting problem . Taking only functions from to form a tensor network as the problem's input, the counting problem asks for the value of the tensor network. These dichotomy or quasi-dichotomy theorems form a partial order according to the inclusion relations of the problem subclasses they characterize. As the number of known dichotomy theorems increases, the number of maximal elements in this partially ordered set first grows, and then shrinks when a new dichotomy theorem unifies several previous maximal ones; currently, there are about five or six. More can be artificially defined. However, it might be the timing to directly study the maximum element in the total partial order, namely, the entire class. This paper proposes such a framework,…
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Taxonomy
TopicsGene Regulatory Network Analysis · Cellular Automata and Applications · Graph theory and applications
