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

• URL: http://arxiv.org/abs/2309.09032v1
• Date: Sat, 16 Sep 2023 16:00:07 GMT
• Title: Solving Quadratic Systems with Full-Rank Matrices Using Sparse or Generative Priors
• Authors: Junren Chen, Shuai Huang, Michael K. Ng, Zhaoqiang Liu
• Abstract summary: This paper addresses the problem of recovering a signal from a quadratic system $y_i=boldsymbolxtopboldsymbolA_iboldsymbolx. We introduce the thresholded Wirtinger flow (TWF) algorithm that does not require the sparsity level $k$. We show that our approach for the sparse case notably outperforms the existing provable algorithm power factorization.
• Score: 44.94521933974231
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (with a good initialization) that 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 $r$. We develop the projected gradient descent (PGD) algorithm that also comprises two steps: the projected power method that provides an initial vector with $O\big(\sqrt{\frac{k \log L}{m}}\big)$ $\ell_2$-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.

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