Mathematical analysis of a dynamical system for sparse recovery

Sparse recovery;Neural network;L1-minimization;Nonsmooth optimization;Compressed sensing;Tracking;ISTA;LCA

Balavoine, Aurele ; Romberg, Justin K. Electrical and Computer Engineering Rozell, Christopher J. Hasler, Jennifer 0. Yezzi, Anthony J. Balcan, Maria-Florina Davenport, Mark A. ; Romberg, Justin K.

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Sparse recovery;    Neural network;    L1-minimization;    Nonsmooth optimization;    Compressed sensing;    Tracking;    ISTA;    LCA;   

Others  :  https://smartech.gatech.edu/bitstream/1853/51882/1/BALAVOINE-DISSERTATION-2014.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

This thesis presents the mathematical analysis of a continuous-times system for sparse signal recovery. Sparse recovery arises in Compressed Sensing (CS), where signals of large dimension must be recovered from a small number of linear measurements, and can be accomplished by solving a complex optimization program. While many solvers have been proposed and analyzed to solve such programs in digital, their high complexity currently prevents their use in real-time applications. On the contrary, a continuous-time neural network implemented in analog VLSI could lead to significant gains in both time and power consumption. The contributions of this thesis are threefold. First, convergence results for neural networks that solve a large class of nonsmooth optimization programs are presented. These results extend previous analysis by allowing the interconnection matrix to be singular and the activation function to have many constant regions and grow unbounded. The exponential convergence rate of the networks is demonstrated and an analytic expression for the convergence speed is given. Second, these results are specialized to the L1-minimization problem, which is the most famous approach to solving the sparse recovery problem. The analysis relies on standard techniques in CS and proves that the network takes an efficient path toward the solution for parameters that match results obtained for digital solvers. Third, the convergence rate and accuracy of both the continuous-time system and its discrete-time equivalent are derived in the case where the underlying sparse signal is time-varying and the measurements are streaming. Such a study is of great interest for practical applications that need to operate in real-time, when the data are streaming at high rates or the computational resources are limited. As a conclusion, while existing analysis was concentrated on discrete-time algorithms for the recovery of static signals, this thesis provides convergence rate and accuracy results for the recovery of static signals using a continuous-time solver, and for the recovery of time-varying signals with both a discrete-time and a continuous-time solver.