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Publications of H. Mohammadi
Journal articles
  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]


  2. H. Mohammadi, S. Samuelson, and M. R. Jovanovic. Transient growth of accelerated first-order methods for strongly convex optimization problems. IEEE Trans. Automat. Control, 2021. Note: Submitted; also arXiv:2103.08017. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convex optimization, Gradient descent, Heavy-ball method, Integral quadratic constraints, Nesterov's accelerated method, Nonnormal dynamics, Transient growth. [bibtex-entry]


  3. H. Mohammadi, M. Soltanolkotabi, and M. R. Jovanovic. On the linear convergence of random search for discrete-time LQR. IEEE Control Syst. Lett., 5(3):989-994, July 2021. Keyword(s): Data-driven control, Gradient descent, Linear quadratic regulator, Model-free control, Nonconvex optimization, Optimization, Optimal control, Random search method, Reinforcement learning, Sample complexity. [bibtex-entry]


  4. 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, 2021. Note: Doi:10.1109/TAC.2021.3087455; also arXiv:1912.11899. 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]


  5. A. Zare, H. Mohammadi, N. K. Dhingra, T. T. Georgiou, and M. R. Jovanovic. Proximal algorithms for large-scale statistical modeling and sensor/actuator selection. IEEE Trans. Automat. Control, 65(8):3441-3456, August 2020. Keyword(s): Actuator selection, Augmented Lagrangian, Convex optimization, Low-rank perturbation, Matrix completion problem, Method of multipliers, Non-smooth optimization, Proximal algorithms, Regularization for design, Sensor selection, Sparsity-promoting optimal control, Structured covariances. [bibtex-entry]


Conference articles
  1. 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, 2021. Note: Submitted. 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]


  2. 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]


  3. 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]


  4. S. Samuelson, H. Mohammadi, and M. R. Jovanovic. On the transient growth of Nesterov's accelerated method for strongly convex optimization problems. In Proceedings of the 59th IEEE Conference on Decision and Control, Jeju Island, Republic of Korea, pages 5911-5916, 2020. Note: (Invited paper). Keyword(s): Accelerated first-order algorithms, Control for optimization, Convex optimization, Gradient descent, Integral quadratic constraints, Nesterov's accelerated method, Nonnormal dynamics, Transient growth. [bibtex-entry]


  5. S. Samuelson, H. Mohammadi, and M. R. Jovanovic. Transient growth of accelerated first-order methods. In Proceedings of the 2020 American Control Conference, Denver, CO, pages 2858-2863, 2020. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convex optimization, Gradient descent, Transient growth. [bibtex-entry]


  6. H. Mohammadi, M. Razaviyayn, and M. R. Jovanovic. Performance of noisy Nesterov's accelerated method for strongly convex optimization problems. In Proceedings of the 2019 American Control Conference, Philadelphia, PA, pages 3426-3431, 2019. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convex optimization, Integral quadratic constraints, Linear matrix inequalities, Noise amplification, Second-order moments, Semidefinite programming. [bibtex-entry]


  7. 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]


  8. 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]


  9. 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]


Book chapters
  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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Last modified: Mon Jun 7 10:25:01 2021
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