Spanning Tree Constrained Determinantal Point Processes are Hard to (Approximately) Evaluate

TL;DR

This paper proves that computing the normalizing constant for spanning-tree constrained determinantal point processes is computationally hard, establishing a significant complexity barrier for their evaluation and approximation.

Contribution

It demonstrates the extsf{#P}-hardness of evaluating spanning-tree DPPs and provides a reduction from the mixed discriminant, highlighting the computational difficulty.

Findings

Computing the normalizing constant for spanning-tree DPPs is extsf{#P}-hard.

Approximate evaluation of these DPPs is also computationally intractable.

Similar hardness results apply to forest-constrained DPPs.

Abstract

We consider determinantal point processes (DPPs) constrained by spanning trees. Given a graph $G=(V,E)$ and a positive semi-definite matrix $\mathbf{A}$ indexed by $E$, a spanning-tree DPP defines a distribution such that we draw $S\subseteq E$ with probability proportional to $\det(\mathbf{A}_S)$ only if $S$ induces a spanning tree. We prove $\sharp\textsf{P}$-hardness of computing the normalizing constant for spanning-tree DPPs and provide an approximation-preserving reduction from the mixed discriminant, for which FPRAS is not known. We show similar results for DPPs constrained by forests.