The Information Bottleneck is NP-hard

Abstract: Given a joint distribution $P_{XY}$ on a finite alphabet, the information bottleneck method seeks a representation $T$ of $X$ that is as informative as possible about $Y$ while compressing $X$, where $T$ and $Y$ are conditionally independent given $X$. We show that computing the optimal value $$\mathrm{IB}(P_{XY},R) = \sup_T { I(Y;T) : I(X;T) \le R }$$ of the information bottleneck problem, as well as its Lagrangian relaxation $L_β(P_{XY}) = \sup_T \big[I(Y;T) - βI(X;T)\big]$, is NP-hard. Moreover, unless $\mathrm{P} = \mathrm{NP}$, neither $\mathrm{IB}(P_{XY},R)$ nor $L_β(P_{XY})$ can be approximated in polynomial time within an additive error of order $|\mathcal{X}|<sup>{-3/2}$.