On the Complexity of Entrywise Power Matrix Factorization

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.