1. H. Mohammadi. Robustness of gradient methods for data-driven decision making. PhD thesis, University of Southern California, 2022. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Data-driven control, Gradient descent, Gradient-flow dynamics, Heavy-ball method, Integral quadratic constraints, Linear quadratic regulator, Model-free control, Nesterov's accelerated method, Nonconvex optimization, Nonnormal dynamics, Noise amplification, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity, Second-order moments, Transient growth. [bibtex-entry]

1. H. Mohammadi, M. Razaviyayn, and M. R. Jovanovic. Robustness of accelerated first-order algorithms for strongly convex optimization problems. IEEE Trans. Automat. Control, 66(6):2480-2495, June 2021. Keyword(s): Accelerated first-order algorithms, Consensus networks, Control for optimization, Convex optimization, Integral quadratic constraints, Linear matrix inequalities, Noise amplification, Second-order moments, Semidefinite programming. [bibtex-entry]

1. H. Mohammadi, M. Razaviyayn, and M. R. Jovanovic. Variance amplification of accelerated first-order algorithms for strongly convex quadratic optimization problems. In Proceedings of the 57th IEEE Conference on Decision and Control, Miami, FL, pages 5753-5758, 2018. Keyword(s): Accelerated optimization algorithms, Control for optimization, Input-output analysis, Large-scale networks, Fundamental limitations, Robustness, Variance amplifications. [bibtex-entry]

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