About Ayed M. Alrashdi Ayed M. Alrashdi Ph.D. Student (former), Electrical and Computer Engineering statistical signal processing Compressive Sensing high dimensional statistics mathematical optimization statistical learning Wireless Communications Ph.D. degree in electrical and computer engineering from the King Abdullah University of Science and Technology (KAUST) working with Professor Tareq Al-Naffouri in the Information System Lab (ISL),Assistant Professor with the Electrical Engineering Department at University of Ha'il Events Presented Events Feb 21 - Feb 27, 2021 High-Dimensional Analysis of Regularized Convex Optimization Problems with Application to Massive MIMO Wireless Communication Systems Ayed M. Alrashdi, Ph.D. Student (former), Electrical and Computer Engineering Feb 21, 17:00 - 18:00 KAUST In this thesis, we focus on precisely analyzing the high dimensional error performance of such regularized convex optimization problems under the presence of different impairments (such as uncertainties and/or correlations) in the measurement matrix, which has independent Gaussian entries. The precise nature of our analysis allows performance comparison between different types of these estimators and enables us to optimally tune the involved hyperparameters. In particular, we study the performance of some of the most popular cases in linear inverse problems, such as the Least Squares (LS), Regularized Least Squares (RLS), LASSO, Elastic Net, and their box-constrained variants.
High-Dimensional Analysis of Regularized Convex Optimization Problems with Application to Massive MIMO Wireless Communication Systems Ayed M. Alrashdi, Ph.D. Student (former), Electrical and Computer Engineering Feb 21, 17:00 - 18:00 KAUST In this thesis, we focus on precisely analyzing the high dimensional error performance of such regularized convex optimization problems under the presence of different impairments (such as uncertainties and/or correlations) in the measurement matrix, which has independent Gaussian entries. The precise nature of our analysis allows performance comparison between different types of these estimators and enables us to optimally tune the involved hyperparameters. In particular, we study the performance of some of the most popular cases in linear inverse problems, such as the Least Squares (LS), Regularized Least Squares (RLS), LASSO, Elastic Net, and their box-constrained variants.
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