On the Complexity of Entrywise Power Matrix Factorization

2026-07-06

AI Summary

This study investigates the computational complexity of element-wise power matrix factorization (EPMF): given a nonnegative matrix \(X\), a rank \(r\), and a real exponent \(p\), the task is to determine whether there exists a rank-\(r\) matrix \(X_r\) such that \(X = |X_r|^{\circ p}\) exactly, or approximately in Frobenius norm. By reducing exact EPMF to the sign-flipping problem, the authors establish for the first time that exact EPMF is strongly NP-hard—previously only weak NP-hardness was known—and further show that it becomes polynomial-time solvable when the rank is fixed and fixed-parameter tractable when parameterized by the input rank. For the approximate variant, they prove NP-hardness even when \(r = 2\). This work thus provides a complete characterization of the complexity landscape of EPMF in both exact and approximate settings.

Abstract

Given a nonnegative matrix $X$, a factorization rank $r$ and a real parameter $p$, entrywise power matrix factorization (EPMF) looks for a low-rank matrix $X_r$ such that $X = |X_r|^{\circ p}$ (exact case) or $X \approx |X_r|^{\circ p}$ (approximate case), where $(\cdot)^{\circ p}$ denotes the component-wise exponent. EPMF includes the modulus model ($p=1$) and component-wise square factorization ($p=2$) as special cases, the latter being closely related to the square root rank. We analyze the computational complexity of the exact decision problem and the Frobenius-norm approximation problem, and establish a complete complexity landscape. In the exact case, we show that EPMF is equivalent to the combinatorial problem of flipping the signs of the entries of a given matrix $X$ to obtain a rank-$r$ matrix, which we refer to as the signing problem. We first show that the signing problem, and hence exact EPMF, is strongly NP-hard, improving a weak NP-hardness result for the square-root-rank of Fawzi et al. (Math. Prog., 2015). We then show that the signing problem can be solved in polynomial-time when $r$ is fixed. Moreover, when the rank $r$ is part of the input, we show that for generic matrices the algorithm is fixed-parameter tractable (FPT) in the parameter $r$; in fact, the running time is linear in the input size $X$. In the approximate case using the Frobenius norm as an error measure, we show that EPMF is NP-hard, already when $r=2$, the smallest nontrivial case.

Problem

• matrix factorization
• computational complexity
• entrywise power
• low-rank approximation
• NP-hardness

Innovation

• entrywise power matrix factorization
• signing problem
• computational complexity
• fixed-parameter tractability
• NP-hardness