The Exponential-Time Complexity of the complex weighted #CSP

TL;DR

This paper establishes a fine-grained complexity classification for Boolean #CSP problems under #ETH, identifying conditions for polynomial-time solvability versus exponential-time hardness, even with bounded degree constraints.

Contribution

It provides a dichotomy theorem for Boolean #CSP problems based on algebraic function sets, extending to bounded degree cases and analyzing pinning techniques.

Findings

#CSP with certain function sets is polynomial-time solvable.

Otherwise, #CSP cannot be solved in sub-exponential time unless #ETH fails.

Pinning preserves sub-exponential lower bounds in #CSP problems.

Abstract

In this paper, I consider a fine-grained dichotomy of Boolean counting constraint satisfaction problem (#CSP), under the exponential time hypothesis of counting version (#ETH). Suppose $\mathscr{F}$ is a finite set of algebraic complex-valued functions defined on Boolean domain. When $\mathscr{F}$ is a subset of either two special function sets, I prove that #CSP($\mathscr{F}$) is polynomial-time solvable, otherwise it can not be computed in sub-exponential time unless #ETH fails. I also improve the result by proving the same dichotomy holds for #CSP with bounded degree (every variable appears at most constant constraints), even for #R$_3$-CSP. An important preparation before proving the result is to argue that pinning (two special unary functions $[1,0]$ and $[0,1]$ are used to reduce arity) can also keep the sub-exponential lower bound of a Boolean #CSP problem. I discuss this issue by utilizing some common methods in proving #P-hardness of counting problems. The proof illustrates the internal correlation among these commonly used methods.