Learning Transition Operators From Sparse Space-Time Samples

Abstract

We consider the nonlinear inverse problem of learning a transition operator A from partial observations at T different times, in the form of sparse observations of entries of the powers $A,A^2,…,A^T$. We address the nonlinearity of this spatio-temporal transition operator recovery problem by a suitable embedding into block-Hankel matrices, transforming it to a low-rank matrix completion problem, even when A has full rank. For both a uniform and an adaptive random space-time sampling model, we quantify the recoverability of the transition operator via suitable measures of incoherence of these block-Hankel embedding matrices. For graph transition operators these measures of incoherence depend on the interplay between the dynamics and the graph topology. We develop a suitable non-convex iterative reweighted least squares (IRLS) algorithm, establish its quadratic local convergence, and show that, in optimal scenarios, no more than $O(r n log(nT))$ space-time samples are sufficient to ensure accurate recovery of a rank-$r$ operator A of size $n \times n$. We provide an efficient implementation of the proposed IRLS algorithm with space complexity of order $O(r nT)$ and per-iteration time complexity linear in $n$, and confirm in numerical experiments that for several graph transition operators, the theoretical findings accurately track empirical phase transitions.