Abstract 

This paper is concerned with finding a solution x to a quadratic system of equations  We demonstrate that it is possible to solve unstructured random quadratic systems in n variables exactly from O(n) equations in linear time, that is, in time proportional to reading the data {ai } and {yi }. This is accomplished by a novel procedure, which starting from an initial guess given by a spectral initialization procedure, attempts to minimize a nonconvex objective. The proposed algorithm distinguishes from prior approaches by regularizing the initialization and descent procedures in an adaptive fashion, which discard terms bearing too much influence on the initial estimate or search directions. These careful selection rules—which effectively serve as a variance reduction scheme—provide a tighter initial guess, more robust descent directions, and thus enhanced practical performance. Further, this procedure also achieves a nearoptimal statistical accuracy in the presence of noise. Empirically, we demonstrate that the computational cost of our algorithm is about four times that of solving a least-squares problem of the same size.