2018/06/05 by Daniel J. Lum, Lum, Daniel J.
Computer Science · Engineering · Physics and Astronomy · #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Optical Coherence Tomography Applications #Quantum Information and Cryptography #Quantum Physics (quant-ph) #Random lasers and scattering media #Sparse and Compressive Sensing Techniques #physics.comp-ph #quant-ph
paper · pdf · doi:10.48550/arxiv.1806.01829
PhD thesis, Univ. Rochester (2018)
openalex publication_date 2018/06/05 · arxiv created 2018/06/06 · arxiv updated 2018/06/07 · openalex created_date 2022/08/30 · openalex updated_date 2026/07/28
This University of Rochester Physics Ph.D. dissertation introduces concepts in compressive sensing, quantum entanglement, FMCW LiDAR, and quantum data locking. Additionally, the appendix serves as a thorough reference for those interested in applying the alternating direction method of multipliers (ADMM) to optimize an augmented Lagrangian and can easily be tailored to specific optimization problems. In particular, I show how fast Hadamard transforms and the ADMM can be used for L1-minimization with different sparse-basis transforms along with total-variation minimization of both images and video. The simple examples given demonstrate how to minimize high-dimensional problems with little memory overhead. The original version of this dissertation can be accessed through ProQuest.