Papers for

computational complexity analysts

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Generalized fibonacci gates correspond to algebra structures on all domains

Generalized Fibonacci gates are Frobenius algebras: a proof of Liu's conjecture

Abstract: Liu introduced generalized Fibonacci gates on domains of size 3 and 4, proved that Holant problems built from them are tractable by a cut-and-merge algorithm, and conjectured that Fibonacci gates with matching polynomial-time algorithms exist on all higher domains. We extend the definition uniformly to every domain size $k$ and show that a family of domain-$k$ generalized Fibonacci gates is the same thing as a $k$-dimensional commutative Frobenius (equivalently, Artinian Gorenstein) algebra $A$ equipped with an orthonormal basis containing $1$. Under this dictionary Liu's parameter constraints are exactly associativity of $A$, a gate is an element $w\in A$, merging two gates is multiplication in $A$, and the Holant value of a connected graph $G$ is $τ\bigl(h^{\,|E|-|V|+1}\prod_v w_v\bigr)$ for a fixed ``handle'' element $h\in A$. This proves Liu's conjecture for every $k$, with an algorithm whose cost is polynomial in $k$ as well as in the graph. We also give a closure criterion for linear recurrences of any depth (a balance condition implies closure, and is necessary when the unit is a combination of the colours), and we map the landscape: semisimple algebras are exactly Liu's orthogonal frames, smoothable algebras with nilpotents are collided frames, and families whose algebra is not smoothable (``frameless'' families) exist exactly when $k\ge 14$. Finally we show that every non-semisimple part of a family is invisible to the Holant on connected graphs with two or more independent cycles. This note states the results and sketches the proofs; the proofs of the main theorems are short and are given essentially in full.

Tue 13 OctComputational Complexity
The gist
This paper solves a math and computer science puzzle about special gadgets called generalized Fibonacci gates, which help solve certain counting problems efficiently. The authors show that these gates on any domain size can be understood exactly as a kind of algebra called a commutative Frobenius algebra. This insight matches previous guesses and gives a method that works efficiently for all sizes. They also classify different families of these gates and explain which ones affect the counting in different cases.
Open → 2610.19230v1

Spectral criteria clarify planar graph homomorphism complexity classes

Spectral Methods for the Complexity of Planar Graph Homomorphisms

Abstract: We explore the frontier beyond the recently discovered barrier represented by the \emph{quantum automorphism group} $qut(M)$ in the classification theory of planar graph homomorphisms $PlGH(M)$. We show that analyzing the spectral relations of $M$ can prove \#P-hardness when traditional vertex separation and domain-reduction methods with planar edge gadgets provably fail due to the $\qut(M)$ barrier. We prove two criteria of \#P-hardness for $PlGH(M)$: a spectral criterion and a determinant criterion. It is known that the core problem for the classification of $PlGH(M)$ for nonnegative matrices $M$ is for positive definite entry-wise positive matrices. We use the spectral criterion to show that $PlGH(M)$ is \#P-hard for all circulant matrices of prime order $q \ge 3$, while for $q=2$ it is precisely the matchgate case and is P-time computable by the FKT algorithm (for planar perfect matching). We also prove a complexity dichotomy for $\PlGH$ problems defined by tensor products of 2 by 2 matrices. This gives a complete complexity classification for this class of matrices, and the FKT algorithm together with a holographic transformation is \emph{universal}---every $PlGH(M)$ is either (1) P-time computable over all graphs, or (2) \#P-hard in general but P-time computable over planar graphs, or (3) \#P-hard over planar graphs; furthermore, $PlGH(M)$ in (2) consists of precisely those computable by FKT with a holographic transformation.

Mon 28 SeptComputational Complexity
The gist
Understanding how to count graph homomorphisms, which are ways of mapping one graph's points to another, helps solve many problems in computer science. The authors study this problem specifically for planar graphs, those that can be drawn without crossing edges. They use spectral properties (related to eigenvalues of matrices) to decide when counting these mappings is very hard versus when it is easy or possible in polynomial time. Their work classifies all such planar cases for certain matrix types, connecting previously known polynomial-time methods with proofs of hardness.
Open → 2609.36072v1