Solving Quadratic Systems With Full-Rank Matrices Using Sparse or Generative Priors

Abstract

The problem of recovering a signal $\boldsymbol{x}\in\mathbb{R}^{n}$ from a quadratic system $\{y_{i}=\boldsymbol{x}^{\top}\boldsymbol{A}_{i}\boldsymbol{x},\ i=1,\ldots,m\}$ with full-rank matrices $\boldsymbol{A}_{i}$ frequently arises in applications such as unassigned distance geometry and sub-wavelength imaging. With i.i.d. standard Gaussian matrices $\boldsymbol{A}_{i}$, this paper addresses the high-dimensional case where $m\ll n$ by incorporating prior knowledge of $\boldsymbol{x}$. First, we consider a $k$-sparse $\boldsymbol{x}$ and introduce the thresholded Wirtinger flow (TWF) algorithm that does not require the sparsity level $k$. TWF comprises two steps: the spectral initialization that identifies a point sufficiently close to $\boldsymbol{x}$ (up to a sign flip) when $m=O(k^{2}\log n)$, and the thresholded gradient descent which, when provided a good initialization, produces a sequence linearly converging to $\boldsymbol{x}$ with $m=O(k\log n)$ measurements. Second, we explore the generative prior, assuming that $\boldsymbol{x}$ lies in the range of an $L$-Lipschitz continuous generative model with $k$-dimensional inputs in an $\ell_{2}$-ball of radius $r$. With an estimate correlated with the signal, we develop the projected gradient descent (PGD) algorithm that also comprises two steps: the projected power method that provides an initial vector with $O\big{(}\sqrt{k\log(L)/m}\big{)}$ $\ell_{2}$-error given $m=O(k\log(Lnr))$ measurements, and the projected gradient descent that refines the $\ell_{2}$-error to $O(\delta)$ at a geometric rate when $m=O(k\log\frac{Lrn}{\delta^{2}})$. Experimental results corroborate our theoretical findings and show that: (i) our approach for the sparse case notably outperforms the existing provable algorithm sparse power factorization; (ii) leveraging the generative prior allows for precise image recovery in the MNIST dataset from a small number of quadratic measurements.