Tensor network complexity of multilinear maps


e study tensor networks as a model of arithmetic computation for evaluating multilinear maps. These capture any algorithm based on low border rank tensor decompositions, such as $O(n^{\omega+\epsilon})$ time matrix multiplication, and in addition many other algorithms such as $O(n \log n)$ time discrete Fourier transform and $O^*(2^n)$ time for computing the permanent of a matrix. However tensor networks sometimes yield faster algorithms than those that follow from low-rank decompositions. For instance the fastest known $O(n^{(\omega +\epsilon)t})$ time algorithms for counting $3t$-cliques can be implemented with tensor networks, even though the underlying tensor has border rank $n^{3t}$ for all $t \geq 2$. For counting homomorphisms of a general pattern graph P into a host graph on $n$ vertices we obtain an upper bound of $O(n^{(\omega+\epsilon)\mathrm{bw}(P)/2})$ where $\mathrm{bw}(P)$ is the branchwidth of $P$. This essentially matches the bound for counting cliques, and yields small improvements over previous algorithms for many choices of $P$. While powerful, the model still has limitations, and we are able to show a number of unconditional lower bounds for various multilinear maps, including: b) an $\Omega(n^{\mathrm{bw}(P)})$ time lower bound for counting homomorphisms from $P$ to an $n$-vertex graph, matching the upper bound if $\omega = 2$. In particular for $P$ a $v$-clique this yields an $\Omega(n^{\mathrm{ceil}[2v/3]})$ time lower bound for counting $v$-cliques, and for $P$ a $k$-uniform $v$-hyperclique we obtain an $\Omega(n^v)$ time lower bound for $k \geq 3$, ruling out tensor networks as an approach to obtaining non-trivial algorithms for hyperclique counting and the Max-3-CSP problem. c) an $\Omega(2^{0.918n})$ time lower bound for the permanent of an $n \times n$ matrix.

10th Innovations in Theoretical Computer Science Conference (ITCS 2019), 7:1-7:21