Sharp Time--Data Tradeoffs for Linear Inverse Problems

TL;DR

This paper establishes precise time-data tradeoffs for solving linear inverse problems using gradient projection, demonstrating linear convergence rates near the phase transition with empirical validation.

Contribution

It provides a unified analysis of convergence rates for gradient projection in linear inverse problems, including nonconvex cases, with sharp bounds related to measurement complexity.

Findings

Linear convergence is achievable even without strong convexity.

Convergence rate sharply characterized for various measurement ensembles.

Results match empirical performance closely.

Abstract

In this paper we characterize sharp time-data tradeoffs for optimization problems used for solving linear inverse problems. We focus on the minimization of a least-squares objective subject to a constraint defined as the sub-level set of a penalty function. We present a unified convergence analysis of the gradient projection algorithm applied to such problems. We sharply characterize the convergence rate associated with a wide variety of random measurement ensembles in terms of the number of measurements and structural complexity of the signal with respect to the chosen penalty function. The results apply to both convex and nonconvex constraints, demonstrating that a linear convergence rate is attainable even though the least squares objective is not strongly convex in these settings. When specialized to Gaussian measurements our results show that such linear convergence occurs when the number of measurements is merely 4 times the minimal number required to recover the desired signal at all (a.k.a. the phase transition). We also achieve a slower but geometric rate of convergence precisely above the phase transition point. Extensive numerical results suggest that the derived rates exactly match the empirical performance.