1. H. Mohammadi, A. Zare, M. Soltanolkotabi, and M. R. Jovanovic. Convergence and sample complexity of gradient methods for the model-free linear-quadratic regulator problem. IEEE Trans. Automat. Control, 67(5):2435-2450, May 2022. Keyword(s): Data-driven control, Gradient descent, Gradient-flow dynamics, Linear quadratic regulator, Model-free control, Nonconvex optimization, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity. [bibtex-entry]



2. N. K. Dhingra, S. Z. Khong, and M. R. Jovanovic. The proximal augmented Lagrangian method for nonsmooth composite optimization. IEEE Trans. Automat. Control, 64(7):2861-2868, July 2019. Keyword(s): Augmented Lagrangian, Control for optimization, Exponential convergence, Global exponential stability, Method of multipliers, Non-smooth optimization, Primal-dual gradient flow dynamics, Proximal algorithms, Proximal augmented Lagrangian, Regularization for design, Sparsity-promoting optimal control, Structured optimal control, Structure identification. [bibtex-entry]

1. I. K. Ozaslan and M. R. Jovanovic. On the global exponential stability of primal-dual dynamics for convex problems with linear equality constraints. In Proceedings of the 2023 American Control Conference, San Diago, CA, 2023. Note: Submitted. Keyword(s): Global exponential stability, Gradient flow dynamics, Lagrangian, Lyapunov functions, Primal-dual methods. [bibtex-entry]



2. S. Samuelson, H. Mohammadi, and M. R. Jovanovic. Performance of noisy higher-order accelerated gradient flow dynamics for strongly convex quadratic optimization problems. In Proceedings of the 2023 American Control Conference, San Diego, CA, 2023. Note: Submitted. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient flow dynamics, Noise amplification, Nonnormal dynamics, Two-step momentum algorithm. [bibtex-entry]



3. H. Mohammadi and M. R. Jovanovic. On the noise amplification of primal-dual gradient flow dynamics based on proximal augmented Lagrangian. In Proceedings of the 2022 American Control Conference, Atlanta, GA, pages 926-931, 2022. Keyword(s): Control for optimization, Convex Optimization, Integral quadratic constraints, Linear matrix inequalities, Noise amplification, Non-smooth optimization, Proximal algorithms, Primal-dual gradient flow dynamics, Primal-dual methods, Proximal augmented Lagrangian, Second-order moments, Semidefinite programming. [bibtex-entry]



4. I. K. Ozaslan, S. Hassan-Moghaddam, and M. R. Jovanovic. On the asymptotic stability of proximal algorithms for convex optimization problems with multiple non-smooth regularizers. In Proceedings of the 2022 American Control Conference, Atlanta, GA, pages 132-137, 2022. Keyword(s): Control for optimization, Convex Optimization, Douglas-Rachford splitting, Global asymptotic stability, Lyapunov-based analysis, Non-smooth optimization, Proximal algorithms, Primal-dual gradient flow dynamics, Primal-dual methods, Proximal augmented Lagrangian. [bibtex-entry]



5. I. K. Ozaslan and M. R. Jovanovic. Exponential convergence of primal-dual dynamics for multi-block problems under local error bound condition. In Proceedings of the 61th IEEE Conference on Decision and Control, Cancun, Mexico, 2022. Note: To appear. Keyword(s): Gradient flow dynamics, Lyapunov functions, Proximal algorithms, Primal-dual gradient flow dynamics, Primal-dual methods, Proximal augmented Lagrangian, Operator splitting. [bibtex-entry]



6. H. Mohammadi, M. Soltanolkotabi, and M. R. Jovanovic. On the lack of gradient domination for linear quadratic Gaussian problems with incomplete state information. In Proceedings of the 60th IEEE Conference on Decision and Control, Austin, TX, pages 1120-1124, 2021. Keyword(s): Data-driven control, Gradient descent, Gradient-flow dynamics, Model-free control, Nonconvex optimization, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity. [bibtex-entry]



7. D. Ding and M. R. Jovanovic. Global exponential stability of primal-dual gradient flow dynamics based on the proximal augmented Lagrangian: A Lyapunov-based approach. In Proceedings of the 59th IEEE Conference on Decision and Control, Jeju Island, Republic of Korea, pages 4836-4841, 2020. Keyword(s): Augmented Lagrangian, Control for optimization, Convex optimization, Global exponential stability, Lyapunov-based approach, Non-smooth optimization, Primal-dual gradient flow dynamics, Primal-dual methods, Proximal augmented Lagrangian. [bibtex-entry]



8. H. Mohammadi, M. Soltanolkotabi, and M. R. Jovanovic. Learning the model-free linear quadratic regulator via random search. In Proceedings of Machine Learning Research, 2nd Annual Conference on Learning for Dynamics and Control, volume 120, Berkeley, CA, pages 1-9, 2020. Keyword(s): Data-driven control, Gradient descent, Gradient-flow dynamics, Linear quadratic regulator, Model-free control, Nonconvex optimization, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity. [bibtex-entry]



9. H. Mohammadi, M. Soltanolkotabi, and M. R. Jovanovic. Random search for learning the linear quadratic regulator. In Proceedings of the 2020 American Control Conference, Denver, CO, pages 4798-4803, 2020. Keyword(s): Data-driven control, Gradient descent, Gradient-flow dynamics, Linear quadratic regulator, Model-free control, Nonconvex optimization, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity. [bibtex-entry]



10. D. Ding and M. R. Jovanovic. Global exponential stability of primal-dual gradient flow dynamics based on the proximal augmented Lagrangian. In Proceedings of the 2019 American Control Conference, Philadelphia, PA, pages 3414-3419, 2019. Keyword(s): Convex optimization, Global exponential stability, Non-smooth optimization, Primal-dual gradient flow dynamics, Proximal augmented Lagrangian method. [bibtex-entry]



11. H. Mohammadi, A. Zare, M. Soltanolkotabi, and M. R. Jovanovic. Global exponential convergence of gradient methods over the nonconvex landscape of the linear quadratic regulator. In Proceedings of the 58th IEEE Conference on Decision and Control, Nice, France, pages 7474-7479, 2019. Keyword(s): Data-driven control, Global exponential stability, Gradient descent, Gradient-flow dynamics, Model-free control, Nonconvex optimization, Optimization, Optimal control, Reinforcement learning. [bibtex-entry]



12. D. Ding, B. Hu, N. K. Dhingra, and M. R. Jovanovic. An exponentially convergent primal-dual algorithm for nonsmooth composite minimization. In Proceedings of the 57th IEEE Conference on Decision and Control, Miami, FL, pages 4927-4932, 2018. Keyword(s): Control for optimization, Convex optimization, Euler discretization, Exponential convergence, Global exponential stability, Integral quadratic constraints, Proximal augmented Lagrangian, Non-smooth optimization, Primal-dual gradient flow dynamics, Proximal algorithms, Regularization. [bibtex-entry]



13. D. Ding and M. R. Jovanovic. A primal-dual Laplacian gradient flow dynamics for distributed resource allocation problems. In Proceedings of the 2018 American Control Conference, Milwaukee, WI, pages 5316-5320, 2018. Keyword(s): Primal-dual gradient flow dynamics, Proximal augmented Lagrangian, Distributed resource allocation, Economic dispatch. [bibtex-entry]



14. S. Hassan-Moghaddam and M. R. Jovanovic. Distributed proximal augmented Lagrangian method for nonsmooth composite optimization. In Proceedings of the 2018 American Control Conference, Milwaukee, WI, pages 2047-2052, 2018. Keyword(s): Consensus, Distributed Optimization, Non-smooth optimization, Primal-dual gradient flow dynamics, Proximal augmented Lagrangian. [bibtex-entry]



15. S. Hassan-Moghaddam and M. R. Jovanovic. On the exponential convergence rate of proximal gradient flow algorithms. In Proceedings of the 57th IEEE Conference on Decision and Control, Miami, FL, pages 4246-4251, 2018. Note: (Invited paper). Keyword(s): Control for optimization, Distributed optimization, Forward-backward envelope, Exponential convergence, Global exponential stability, Gradient flow dynamics, Large-scale systems, Non-smooth optimization, Primal-dual method, Proximal algorithms, Proximal augmented Lagrangian. [bibtex-entry]



16. H. Mohammadi, M. Razaviyayn, and M. R. Jovanovic. On the stability of gradient flow dynamics for a rank-one matrix approximation problem. In Proceedings of the 2018 American Control Conference, Milwaukee, WI, pages 4533-4538, 2018. Keyword(s): Nonconvex optimization, Stability of nonlinear systems, Matrix approximation, Gradient flow dynamics. [bibtex-entry]

1. H. Mohammadi, M. Soltanolkotabi, and M. R. Jovanovic. Model-free linear quadratic regulator. In K. G. Vamvoudakis, Y. Wan, F. Lewis, and D. Cansever, editors, Handbook of Reinforcement Learning and Control. Springer International Publishing, 2021. Note: Doi:10.1007/978-3-030-60990-0. Keyword(s): Data-driven control, Gradient descent, Gradient-flow dynamics, Linear quadratic regulator, Model-free control, Nonconvex optimization, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity. [bibtex-entry]

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