Browsing by Author "Wang, Gang"
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Item Non-Convex Phase Retrieval Algorithms and Performance Analysis(2018-04) Wang, GangHigh-dimensional signal estimation plays a fundamental role in various science and engineering applications, including optical and medical imaging, wireless communications, and power system monitoring. The ability to devise solution procedures that maintain high computational and statistical efficiency will facilitate increasing the resolution and speed of lensless imaging, identifying artifacts in products intended for military or national security, as well as protecting critical infrastructure including the smart power grid. This thesis contributes in both theory and methods to the fundamental problem of phase retrieval of high-dimensional (sparse) signals from magnitude-only measurements. Our vision is to leverage exciting advances in non-convex optimization and statistical learning to devise algorithmic tools that are simple, scalable, and easy-to-implement, while being computationally and statistically (near-)optimal. Phase retrieval is approached from a non-convex optimization perspective. To gain statistical and computational efficiency, the magnitude data (instead of the intensities) are fitted based on the least-squares or maximum likelihood criterion, which leads to optimization models that trade off smoothness for ‘low-order’ non-convexity. To solve the resultant challenging nonconvex and non-smooth optimization, the present thesis introduces a two-stage algorithmic framework, that is termed amplitude flow. The amplitude flows start with a careful initialization, which is subsequently refined by a sequence of regularized gradient-type iterations. Both stages are lightweight, and they scale well with problem dimensions. Due to the highly non-convex landscape, judicious gradient regularization techniques such as trimming (i.e., truncation) and iterative reweighting are devised to boost the exact phase recovery performance. It is shown that successive iterates of the amplitude flows provably converge to the global optimum at a geometric rate, corroborating their efficiency in terms of computational, storage, and data resources. The amplitude flows are also demonstrated to be stable vis-a-vis additive noise. Sparsity plays a instrumental role in many scientific fields - what has led to the upsurge of research referred to as compressive sampling. In diverse applications, the signal is naturally sparse or admits a sparse representation after some known and deterministic linear transformation. This thesis also accounts for phase retrieval of sparse signals, by putting forth sparsity-cognizant amplitude flow variants. Although analysis, comparisons, and corroborating tests focus on non-convex phase retrieval in this thesis, a succinct overview of other areas is provided to highlight the universality of the novel algorithmic framework to a number of intriguing future research directions.Item Parallel Traffic Flow Simulation of Freeway Networks, Phase 1: Final Report(1994-03) Chronopoulos, Anthony; Rhee, Choong-Yul; Wang, GangNumerical methods for solving simple macroscopic traffic flow continuum models have been studied and efficiently implemented in traffic simulation codes. The Lax method is an explicit method which has been implemented to solve simple continuum model in the traffic simulation package KRONOS. In this part of the project we studied the implementation of the Lax method for solving a high-order flow conservation traffic model on Parallel Computers. We wrote an experimental code in C to simulate a freeway traffic flow. Tests with real data collected from the 1-35 W freeway in Minneapolis were conducted on a workstation computer. We then implemented the high order Lax method on a parallel machine and run tests with real data collected from an 18-mile stretch of the 1-494 freeway. The parallel implementation demonstrated significant execution time speedup.