Sharp Time-Data Tradeoffs for Linear Inverse Problems

• Published in: IEEE transactions on information theory Vol. 64; no. 6; pp. 4129 - 4158
• Main Authors: Oymak, Samet, Recht, Benjamin, Soltanolkotabi, Mahdi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Complexity theory; Convergence; Cost function; Economic models; Empirical analysis; Inverse problems; Least squares; linear inverse problems; Noise measurement; noncovex and nonsmooth optimization; Penalty function; Phase transitions; Prediction algorithms; projected gradient descent; run time prediction of optimization algorithms; Time-data tradeoffs; Tradeoffs; Transition points
• ISSN: 0018-9448; 1557-9654
• DOI: 10.1109/TIT.2017.2773497

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 four times the minimal number required to recover the desired signal at all (also known as 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.