1. S. Samuelson. Performance tradeoffs of accelerated first-order optimization algorithms. PhD thesis, University of Southern California, 2024. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient descent, Gradient-flow dynamics, Heavy-ball method, Nesterov's accelerated method, Nonnormal dynamics, Noise amplification, Optimization, Transient growth. [bibtex-entry]



2. D. Ding. Provable reinforcement learning for constrained and multi-agent control systems. PhD thesis, University of Southern California, 2022. Keyword(s): Constrained Markov decision processes, Constrained nonconvex optimization, Function approximation, Game-agnostic convergence, Multi-agent reinforcement learning, Multi-agent systems, Natural policy gradient, Policy gradient methods, Proximal policy optimization, Primal-dual algorithms, Reinforcement learning, Safe exploration, Safe reinforcement learning, Sample complexity, Stochastic optimization. [bibtex-entry]



3. 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]



4. S. Hassan-Moghaddam. Analysis, design, and optimization of large-scale networks of dynamical systems. PhD thesis, University of Southern California, 2019. Keyword(s): Consensus, Control for optimization, Convex Optimization, Distributed control, Forward-backward envelope, Douglas-Rachford splitting, Global exponential stability, Integral quadratic constraints, Networks of dynamical systems, Non-smooth optimization, Polyak-Lojasiewicz inequality, Proximal algorithms, Primal-dual methods, Proximal augmented Lagrangian, Regularization for design, Sparse graphs, Sparsity-promoting optimal control, Structured optimal control, Structure identification, Topology design. [bibtex-entry]



5. N. K. Dhingra. Optimization and control of large-scale networked systems. PhD thesis, University of Minnesota, 2017. Keyword(s): Augmented Lagrangian, Combination drug therapy, Convex optimization, Directed networks, Leader selection, Method of multipliers, Non-smooth optimization, Optimization, Proximal algorithms, Proximal augmented Lagrangian, Regularization, Second order primal-dual method, Sparsity-promoting optimal control, Structured optimal control, Structure identification. [bibtex-entry]



6. A. Zare. Low-complexity stochastic modeling of wall-bounded shear flows. PhD thesis, University of Minnesota, 2016. Keyword(s): Alternating minimization algorithm, Colored noise, Convex optimization, Disturbance dynamics, Flow modeling and control, Low-complexity modeling, Low-rank approximation, Matrix completion problem, Nuclear norm regularization, Structured covariances, Turbulence modeling. [bibtex-entry]



7. F. Lin. Structure identification and optimal design of large-scale networks of dynamical systems. PhD thesis, University of Minnesota, 2012. Keyword(s): Alternating direction method of multipliers, Architectural issues in distributed control design, Consensus networks, Control of vehicular formations, Convex Optimization, Leader selection, Sparsity-promoting optimal control. [bibtex-entry]

1. H. Mohammadi, M. Razaviyayn, and M. R. Jovanovic. Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms. IEEE Trans. Automat. Control, 2024. Note: Doi:10.1109/TAC.2024.3453656. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient descent, Fundamental limitations, Heavy-ball method, Nesterov's accelerated method, Nonnormal dynamics, Noise amplification, Second-order moments. [bibtex-entry]



2. H. Mohammadi, M. Tinati, S. Tu, M. Soltanolkotabi, and M. R. Jovanovic. Stability properties of gradient flow dynamics for the symmetric low-rank matrix factorization problem. IEEE Control Syst. Lett., 2024. Note: Submitted. Keyword(s): Low rank matrix factorization, Nonconvex optimization, Stability of nonlinear systems, Gradient flow dynamics. [bibtex-entry]



3. I. K. Ozaslan, P. Patrinos, and M. R. Jovanovic. Stability of primal-dual gradient flow dynamics for multi-block convex optimization problems. IEEE Trans. Automat. Control, 2024. Note: Submitted; also arXiv:2408.15969. Keyword(s): Augmented Lagrangian, Exponential convergence, Distributed optimization, Global exponential stability, Gradient flow dynamics, Method of multipliers, Non-smooth optimization, Operator splitting, Primal-dual gradient flow dynamics, Proximal algorithms, Proximal augmented Lagrangian, Regularization for design. [bibtex-entry]



4. W. Wu, J. Chen, M. R. Jovanovic, and T. T. Georgiou. Tannenbaum's gain-margin optimization meets Polyak's heavy-ball algorithm. IEEE Trans. Automat. Control, 2024. Note: Submitted; also arXiv:2409.19882. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient descent, Fundamental limitations, Heavy-ball method, Integral quadratic constraints, Nesterov's accelerated method, Nevanlinna-Pick interpolation, Optimization, Optimal control, Robust control. [bibtex-entry]



5. H. Mohammadi, S. Samuelson, and M. R. Jovanovic. Transient growth of accelerated optimization algorithms. IEEE Trans. Automat. Control, 68(3):1823-1830, March 2023. 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]



6. I. K. Ozaslan and M. R. Jovanovic. Accelerated forward-backward and Douglas-Rachford splitting dynamics. Automatica, 2023. Note: Submitted; also arXiv:2407.20620. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convex Optimization, Forward-backward envelope, Douglas-Rachford splitting, Global exponential stability, Integral quadratic constraints, Nesterov's accelerated method, Non-smooth optimization, Proximal algorithms. [bibtex-entry]



7. I. K. Ozaslan, H. Mohammadi, and M. R. Jovanovic. Computing stabilizing feedback gains via a model-free policy gradient method. IEEE Control Syst. Lett., 7:407-412, July 2023. 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]



8. N. K. Dhingra, S. Z. Khong, and M. R. Jovanovic. A second order primal-dual method for nonsmooth convex composite optimization. IEEE Trans. Automat. Control, 67(8):4061-4076, August 2022. Keyword(s): Augmented Lagrangian, Exponential convergence, Global exponential stability, Method of multipliers, Non-smooth optimization, Proximal algorithms, Proximal augmented Lagrangian, Regularization for design, Second order primal-dual method, Sparsity-promoting optimal control, Structured optimal control, Structure identification. [bibtex-entry]



9. D. Ding, K. Zhang, J. Duan, T. Basar, and M. R. Jovanovic. Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs. J. Mach. Learn. Res., 2022. Note: Submitted; also arXiv:2206.02346. Keyword(s): Constrained Markov decision processes, Constrained nonconvex optimization, Function approximation, Natural policy gradient, Policy gradient methods, Primal-dual algorithms, Sample complexity. [bibtex-entry]



10. 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]



11. S. Hassan-Moghaddam and M. R. Jovanovic. Proximal gradient flow and Douglas-Rachford splitting dynamics: global exponential stability via integral quadratic constraints. Automatica, 123:109311, January 2021. Keyword(s): Control for optimization, Convex Optimization, Forward-backward envelope, Douglas-Rachford splitting, Global exponential stability, Integral quadratic constraints, Non-smooth optimization, Polyak-Lojasiewicz inequality, Proximal algorithms, Primal-dual methods, Proximal augmented Lagrangian. [bibtex-entry]



12. M. R. Jovanovic. From bypass transition to flow control and data-driven turbulence modeling: An input-output viewpoint. Annu. Rev. Fluid Mech., 53(1):311-345, January 2021. Keyword(s): Colored noise, Convex optimization, Drag reduction, Energy amplification, Flow modeling and control, Input-output analysis, Low-complexity modeling, Low-rank approximation, Matrix completion problems, Navier-Stokes equations, Nuclear norm regularization, Simulation-free design, Structured covariances, Transition to turbulence, Turbulence modeling. [bibtex-entry]



13. 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]



14. 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]



15. A. Zare, T. T. Georgiou, and M. R. Jovanovic. Stochastic dynamical modeling of turbulent flows. Annu. Rev. Control Robot. Auton. Syst., 3:195-219, May 2020. Keyword(s): Colored noise, Convex optimization, Disturbance dynamics, Flow modeling and control, Low-complexity modeling, Low-rank approximation, Matrix completion problems, Nuclear norm regularization, Structured covariances, Turbulence modeling. [bibtex-entry]



16. 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]



17. M. Chertkov, M. R. Jovanovic, B. Lesieutre, S. Low, P. van Hentenryck, and L. Wehenkel. Guest Editorial Special Issue on Analysis, Control, and Optimization of Energy Networks. IEEE Trans. Control Netw. Syst., 6(3):922-924, September 2019. Keyword(s): Optimization, Control, Energy networks, Power Networks. [bibtex-entry]



18. N. K. Dhingra, M. Colombino, and M. R. Jovanovic. Structured decentralized control of positive systems with applications to combination drug therapy and leader selection in directed networks. IEEE Trans. Control Netw. Syst., 6(1):352-362, March 2019. Keyword(s): Combination drug therapy, Convex optimization, Directed Networks, Leader selection, Positive systems, Proximal algorithms, Optimization, Sparsity-promoting optimal control, Structured design. [bibtex-entry]



19. 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]



20. S. Hassan-Moghaddam and M. R. Jovanovic. Topology design for stochastically-forced consensus networks. IEEE Trans. Control Netw. Syst., 5(3):1075-1086, September 2018. Keyword(s): Consensus, Convex optimization, Distributed control, Interior point method, Proximal gradient method, Proximal Newton method, Sparse graphs, Topology design. [bibtex-entry]



21. A. Zare, Y. Chen, M. R. Jovanovic, and T. T. Georgiou. Low-complexity modeling of partially available second-order statistics: theory and an efficient matrix completion algorithm. IEEE Trans. Automat. Control, 62(3):1368-1383, March 2017. Keyword(s): Alternating minimization algorithm, Convex optimization, Disturbance dynamics, Low-rank approximation, Matrix completion problems, Nuclear norm regularization, Structured covariances. [bibtex-entry]



22. A. Zare, M. R. Jovanovic, and T. T. Georgiou. Colour of turbulence. J. Fluid Mech., 812:636-680, February 2017. Keyword(s): Colored noise, Convex optimization, Disturbance dynamics, Flow modeling and control, Low-complexity modeling, Low-rank approximation, Matrix completion problems, Nuclear norm regularization, Structured covariances, Turbulence modeling. [bibtex-entry]



23. M. R. Jovanovic and N. K. Dhingra. Controller architectures: tradeoffs between performance and structure. Eur. J. Control, 30:76-91, July 2016. Keyword(s): Controller architecture, Convex optimization, Distributed control, Networks of dynamical systems, Non-smooth optimization, Performance vs. complexity, Regularization, Sparsity-promoting optimal control, Structured optimal control, Structure identification. [bibtex-entry]



24. M. R. Jovanovic, P. J. Schmid, and J. W. Nichols. Sparsity-promoting dynamic mode decomposition. Phys. Fluids, 26(2):024103 (22 pages), February 2014. Keyword(s): Dynamic Mode Decomposition, Flow modeling and control, Convex optimization, Sparsity. [bibtex-entry]



25. F. Lin, M. Fardad, and M. R. Jovanovic. Algorithms for leader selection in stochastically forced consensus networks. IEEE Trans. Automat. Control, 59(7):1789-1802, July 2014. Keyword(s): Alternating direction method of multipliers, Consensus networks, Convex optimization, Greedy algorithm, Leader selection, Performance bounds, Sparsity. [bibtex-entry]



26. R. Moarref, M. R. Jovanovic, J. A. Tropp, A. S. Sharma, and B. J. McKeon. A low-order decomposition of turbulent channel flow via resolvent analysis and convex optimization. Phys. Fluids, 26(5):051701 (7 pages), May 2014. Keyword(s): Flow modeling and control, Convex optimization, Low-rank approximation. [bibtex-entry]



27. F. Lin, M. Fardad, and M. R. Jovanovic. Design of optimal sparse feedback gains via the alternating direction method of multipliers. IEEE Trans. Automat. Control, 58(9):2426-2431, September 2013. Keyword(s): Alternating direction method of multipliers, Architectural issues in distributed control design, Cardinality minimization, Distributed control, Optimization, Sparsity-promoting optimal control. [bibtex-entry]



28. F. Lin and M. R. Jovanovic. Least-squares approximation of structured covariances. IEEE Trans. Automat. Control, 54(7):1643-1648, July 2009. Keyword(s): Optimization, Large-scale systems, Least-squares approximation, Structured covariances. [bibtex-entry]

1. K. Sawant, P. Seiler, M. R. Jovanovic, J. Poon, and S. Dhople. Real-time solution strategy for linearly constrained quadratic programs with proportional-integral control and variants. In Proceedings of the 2025 American Control Conference, Denver, CO, 2025. Note: Submitted. Keyword(s): Control for optimization, Convex optimization, Exponential convergence, Global exponential stability, Optimization. [bibtex-entry]



2. W. Wu, J. Chen, M. R. Jovanovic, and T. T. Georgiou. Frequency-domain synthesis of implicit algorithms. In Proceedings of the 2025 American Control Conference, Denver, CO, 2025. Note: Submitted. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient descent, Heavy-ball method, Integral quadratic constraints, Nesterov's accelerated method, Optimization, Optimal control. [bibtex-entry]



3. I. K. Ozaslan and M. R. Jovanovic. From exponential to finite/fixed-time stability: applications to optimization. In Proceedings of the 63rd IEEE Conference on Decision and Control, Milano, Italy, 2024. Note: To appear. Keyword(s): Exponential stability, Finite-time stability, Fixed-time stability, Normalized gradient descent, Gradient flow dynamics, Primal-dual methods. [bibtex-entry]



4. S. Samuelson and M. R. Jovanovic. Tradeoffs between convergence speed and noise amplification in first-order optimization: the role of averaging. In Proceedings of the 2024 American Control Conference, Toronto, Canada, pages 650-655, 2024. Keyword(s): Accelerated first-order algorithms, Averaging, Control for optimization, Convergence rate, Convex optimization, Gradient flow dynamics, Noise amplification, Nonnormal dynamics, Two-step momentum algorithm. [bibtex-entry]



5. D. Ding, X. Wei, Z. Yang, Z. Wang, and M. R. Jovanovic. Provably efficient generalized Lagrangian policy optimization for safe multi-agent reinforcement learning. In Proceedings of 5th Annual Conference on Learning for Dynamics and Control, volume 211 of Proceedings of Machine Learning Research, Philadelphia, PA, pages 315-332, 2023. Keyword(s): Constrained Markov games, Method of Lagrange multipliers, Minimax optimization, Multi-agent reinforcement learning, Primal-dual policy optimization. [bibtex-entry]



6. H. Mohammadi, M. Razaviyayn, and M. R. Jovanovic. Noise amplification of momentum-based optimization algorithms. In Proceedings of the 2023 American Control Conference, San Diego, CA, pages 849-854, 2023. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient descent, Heavy-ball method, Nesterov's accelerated method, Noise amplification, Nonnormal dynamics, Two-step momentum algorithm. [bibtex-entry]



7. 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, pages 3839-3844, 2023. 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]



8. S. Samuelson, H. Mohammadi, and M. R. Jovanovic. Performance of noisy three-step accelerated first-order optimization algorithms for strongly convex quadratic problems. In Proceedings of the 62nd IEEE Conference on Decision and Control, Singapore, pages 1300-1305, 2023. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient flow dynamics, Noise amplification, Nonnormal dynamics, Three-step momentum algorithm. [bibtex-entry]



9. 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]



10. 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]



11. D. Ding, X. Wei, Z. Yang, Z. Wang, and M. R. Jovanovic. Provably efficient safe exploration via primal-dual policy optimization. In 24th International Conference on Artificial Intelligence and Statistics, volume 130, Virtual, pages 3304-3312, 2021. Keyword(s): Safe reinforcement learning, Constrained Markov decision processes, Safe exploration, Proximal policy optimization, Non-convex optimization, Online mirror descent, Primal-dual method. [bibtex-entry]



12. D. Ding, X. Wei, H. Yu, and M. R. Jovanovic. Byzantine-resilient distributed learning under constraints. In Proceedings of the 2021 American Control Conference, New Orleans, LA, pages 2260-2265, 2021. Keyword(s): Byzantine primal-dual optimization, Constrained optimization, Distributed optimization, Robust statistical learning. [bibtex-entry]



13. 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]



14. 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]



15. D. Ding, K. Zhang, T. Basar, and M. R. Jovanovic. Natural policy gradient primal-dual method for constrained Markov decision processes. In Proceedings of the 34th Conference on Neural Information Processing Systems, volume 33, Vancouver, Canada, pages 8378-8390, 2020. Keyword(s): Constrained Markov decision processes, Constrained nonconvex optimization, Natural policy gradient, Policy gradient methods, Primal-dual algorithms. [bibtex-entry]



16. S. Hassan-Moghaddam and M. R. Jovanovic. Global exponential stability of the Douglas-Rachford splitting dynamics. In Preprints of the 21st IFAC World Congress, Berlin, Germany, pages 7350-7354, 2020. Keyword(s): Control for optimization, Convex Optimization, Forward-backward envelope, Douglas-Rachford splitting, Global exponential stability, Integral quadratic constraints, Non-smooth optimization, Polyak-Lojasiewicz inequality, Proximal algorithms, Primal-dual methods, Proximal augmented Lagrangian. [bibtex-entry]



17. 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]



18. 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]



19. 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]



20. 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]



21. 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]



22. D. Ding, X. Wei, and M. R. Jovanovic. Distributed robust statistical learning: Byzantine mirror descent. In Proceedings of the 58th IEEE Conference on Decision and Control, Nice, France, pages 1822-1827, 2019. Keyword(s): Byzantine mirror descent, Distributed optimization, Dual averaging, Robust statistical learning. [bibtex-entry]



23. D. Ding, X. Wei, Z. Yang, Z. Wang, and M. R. Jovanovic. Fast multi-agent temporal-difference learning via homotopy stochastic primal-dual method. In Optimization Foundations for Reinforcement Learning Workshop, 33rd Conference on Neural Information Processing Systems, Vancouver, Canada, 2019. Keyword(s): Convex optimization, Distributed temporal-difference learning, Multi-agent systems, Primal-dual algorithms, Reinforcement learning, Stochastic optimization. [bibtex-entry]



24. S. Hassan-Moghaddam, M. R. Jovanovic, and S. Meyn. Data-driven proximal algorithms for the design of structured optimal feedback gains. In Proceedings of the 2019 American Control Conference, Philadelphia, PA, pages 5846-5850, 2019. Keyword(s): Data-driven feedback design, Large-scale systems, Non-smooth optimization, Proximal algorithms, Reinforcement learning, Sparsity-promoting optimal control, Structured optimal control. [bibtex-entry]



25. 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]



26. 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]



27. 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]



28. 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]



29. 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]



30. S. Hassan-Moghaddam and M. R. Jovanovic. Topology identification via growing a Chow-Liu tree network. In Proceedings of the 57th IEEE Conference on Decision and Control, Miami, FL, pages 5421-5426, 2018. Keyword(s): Chow-Liu tree, Consensus, Convex optimization, Graphical LASSO, Sparse graphs, Topology identification, Maximum likelihood, Sparse inverse covariance estimation. [bibtex-entry]



31. 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]



32. 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]



33. A. Zare and M. R. Jovanovic. Optimal sensor selection via proximal optimization algorithms. In Proceedings of the 57th IEEE Conference on Decision and Control, Miami, FL, pages 6514-6519, 2018. Keyword(s): Convex optimization, Proximal algorithms, Sensor selection, Semidefinite programming, Sparsity-promoting estimation and control, Quasi-Newton methods. [bibtex-entry]



34. N. K. Dhingra, S. Z. Khong, and M. R. Jovanovic. A second order primal-dual algorithm for non-smooth convex composite optimization. In Proceedings of the 56th IEEE Conference on Decision and Control, Melbourne, Australia, pages 2868-2873, 2017. Keyword(s): Augmented Lagrangian, Method of multipliers, Non-smooth optimization, Proximal methods, Regularization, Sparsity-promoting optimal control, Structured optimal control, Structure identification. [bibtex-entry]



35. S. Hassan-Moghaddam and M. R. Jovanovic. Distributed design of optimal structured feedback gains. In Proceedings of the 56th IEEE Conference on Decision and Control, Melbourne, Australia, pages 6586-6591, 2017. Keyword(s): Alternating direction method of multipliers, Consensus, Distributed control, Optimization, Sparsity-promoting optimal control, Structured optimal control. [bibtex-entry]



36. S. Hassan-Moghaddam, X. Wu, and M. R. Jovanovic. Edge addition in directed consensus networks. In Proceedings of the 2017 American Control Conference, Seattle, WA, pages 5592-5597, 2017. Keyword(s): Alternating direction method of multipliers, Consensus, Directed networks, Nonconvex optimization, Sparsity-promoting optimal control. [bibtex-entry]



37. A. Zare, N. K. Dhingra, M. R. Jovanovic, and T. T. Georgiou. Structured covariance completion via proximal algorithms. In Proceedings of the 56th IEEE Conference on Decision and Control, Melbourne, Australia, pages 3775-3780, 2017. Keyword(s): Augmented Lagrangian, Convex optimization, Low-rank perturbation, Matrix completion problem, Method of multipliers, Non-smooth optimization, Proximal methods, Regularization, Sparsity-promoting optimal control, Structured covariances. [bibtex-entry]



38. J. Annoni, P. Seiler, and M. R. Jovanovic. Sparsity-promoting dynamic mode decomposition for systems with inputs. In Proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, NV, pages 6506-6511, 2016. Note: (Invited paper). Keyword(s): Dynamic Mode Decomposition, Flow modeling and control, Convex optimization, Sparsity. [bibtex-entry]



39. M. Colombino, N. K. Dhingra, M. R. Jovanovic, A. Rantzer, and R. S. Smith. On the optimal control problem for a class of monotone bilinear systems. In Proceedings of the 22nd International Symposium on Mathematical Theory of Network and Systems, Minneapolis, MN, pages 411-413, 2016. Note: (Invited paper). Keyword(s): Convex optimization, Networks, Monotone systems, Positive systems, Proximal algorithms, Optimization, Sparsity-promoting optimal control, Structured design. [bibtex-entry]



40. M. Colombino, N.K. Dhingra, M. R. Jovanovic, and R. S. Smith. Convex reformulation of a robust optimal control problem for a class of positive systems. In Proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, NV, pages 5263-5268, 2016. Note: (Invited paper). Keyword(s): Combination drug therapy, Convex optimization, Networks, Positive systems, Proximal algorithms, Optimization, Sparsity-promoting optimal control, Structured design, Robust Control. [bibtex-entry]



41. N. K. Dhingra, M. Colombino, and M. R. Jovanovic. Leader selection in directed networks. In Proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, NV, pages 2715-2720, 2016. Note: (Invited paper). Keyword(s): Consensus networks, Convex optimization, Positive systems, Leader selection. [bibtex-entry]



42. N. K. Dhingra, M. Colombino, and M. R. Jovanovic. On the convexity of a class of structured optimal control problems for positive systems. In Proceedings of the 2016 European Control Conference, Aalborg, Denmark, pages 825-830, 2016. Keyword(s): Combination drug therapy, Convex optimization, Leader selection, Networks, Positive systems, Proximal algorithms, Optimization, Sparsity-promoting optimal control, Structured design. [bibtex-entry]



43. N. K. Dhingra and M. R. Jovanovic. A method of multipliers algorithm for sparsity-promoting optimal control. In Proceedings of the 2016 American Control Conference, Boston, MA, pages 1942-1947, 2016. Note: (Invited paper). Keyword(s): Augmented Lagrangian, Method of multipliers, Proximal algorithms, Optimization, Sparsity-promoting optimal control. [bibtex-entry]



44. N. K. Dhingra, X. Wu, and M. R. Jovanovic. Sparsity-promoting optimal control of systems with invariances and symmetries. In Proceedings of the 10th IFAC Symposium on Nonlinear Control Systems, Monterey, CA, pages 648-653, 2016. Keyword(s): Architectural issues in distributed control design, Cardinality minimization, Convex optimization, Distributed control, Sparsity-promoting optimal control, Spatially-invariant systems, Symmetric systems. [bibtex-entry]



45. C. Grussler, A. Zare, M. R. Jovanovic, and A. Rantzer. The use of the $r*$ heuristic in covariance completion problems. In Proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, NV, pages 1978-1983, 2016. Keyword(s): Convex optimization, $k$-support-norm, Low-rank approximation, Matrix completion problems, Nuclear norm regularization, Structured covariances. [bibtex-entry]



46. S. Hassan-Moghaddam, N. K. Dhingra, and M. R. Jovanovic. Topology identification of undirected consensus networks via sparse inverse covariance estimation. In Proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, NV, pages 4624-4629, 2016. Keyword(s): Consensus, Convex optimization, Sparse graphs, Topology identification, Maximum likelihood, Sparse inverse covariance estimation. [bibtex-entry]



47. S. Hassan-Moghaddam and M. R. Jovanovic. Customized algorithms for growing connected resistive networks. In Proceedings of the 10th IFAC Symposium on Nonlinear Control Systems, Monterey, CA, pages 986-991, 2016. Keyword(s): Convex optimization, Coordinate descent algorithm, Networks, Proximal algorithms. [bibtex-entry]



48. A. Zare, Y. Chen, M. R. Jovanovic, and T. T. Georgiou. An alternating minimization algorithm for structured covariance completion problems. In Proceedings of the 22nd International Symposium on Mathematical Theory of Network and Systems, Minneapolis, MN, pages 117-119, 2016. Keyword(s): Alternating minimization algorithm, Convex optimization, Disturbance dynamics, Low-rank approximation, Matrix completion problems, Nuclear norm regularization, Structured covariances. [bibtex-entry]



49. A. Zare, M. R. Jovanovic, and T. T. Georgiou. Perturbation of system dynamics and the covariance completion problem. In Proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, NV, pages 7036-7041, 2016. Keyword(s): Convex optimization, Low-rank perturbation, Matrix completion problems, Sparsity-promoting optimal control, Structured covariances. [bibtex-entry]



50. S. Hassan-Moghaddam and M. R. Jovanovic. An interior point method for growing connected resistive networks. In Proceedings of the 2015 American Control Conference, Chicago, IL, pages 1223-1228, 2015. Keyword(s): Consensus, Convex optimization, Distributed control, Interior point method, Sparse graphs, Topology design. [bibtex-entry]



51. A. Zare, M. R. Jovanovic, and T. T. Georgiou. Alternating direction optimization algorithms for covariance completion problems. In Proceedings of the 2015 American Control Conference, Chicago, IL, pages 515-520, 2015. Keyword(s): Alternating direction method of multipliers, Alternating minimization algorithm, Convex optimization, Low-rank approximation, Matrix completion problems, Nuclear norm regularization, Structured covariances. [bibtex-entry]



52. N. K. Dhingra, M. R. Jovanovic, and Z. Q. Luo. An ADMM algorithm for optimal sensor and actuator selection. In Proceedings of the 53rd IEEE Conference on Decision and Control, Los Angeles, CA, pages 4039-4044, 2014. Note: (Invited paper). Keyword(s): Alternating direction method of multipliers, Convex optimization, Actuator selection, Sensor selection. [bibtex-entry]



53. M. Fardad and M. R. Jovanovic. On the design of optimal structured and sparse feedback gains using semidefinite programming. In Proceedings of the 2014 American Control Conference, Portland, OR, pages 2438-2443, 2014. Keyword(s): Architectural issues in distributed control design, Cardinality minimization, Convex optimization, Distributed control, Sparsity-promoting optimal control. [bibtex-entry]



54. M. Fardad, F. Lin, and M. R. Jovanovic. On optimal link creation for facilitation of consensus in social networks. In Proceedings of the 2014 American Control Conference, Portland, OR, pages 3802-3807, 2014. Keyword(s): Consensus, Optimization, Social influence, Social networks, Stochastic matrices. [bibtex-entry]



55. M. Fardad, X. Zhang, F. Lin, and M. R. Jovanovic. On the properties of optimal weak links in consensus networks. In Proceedings of the 53rd IEEE Conference on Decision and Control, Los Angeles, CA, pages 2124-2129, 2014. Note: (Invited paper). Keyword(s): Optimization, Perturbation analysis, Social influence, Social networks, Stochastic matrices. [bibtex-entry]



56. A. Zare, M. R. Jovanovic, and T. T. Georgiou. Completion of partially known turbulent flow statistics. In Proceedings of the 2014 American Control Conference, Portland, OR, pages 1680-1685, 2014. Note: (Invited paper; Finalist, Best Student Paper Award). Keyword(s): Alternating direction method of multipliers, Convex optimization, Flow modeling and control, Low-rank approximation, Matrix completion problems, Nuclear norm regularization, Structured covariances. [bibtex-entry]



57. A. Zare, M. R. Jovanovic, and T. T. Georgiou. Completion of partially known turbulent flow statistics via convex optimization. In Proceedings of the 2014 Summer Program, Center for Turbulence Research, Stanford University/NASA, pages 345-354, 2014. Keyword(s): Convex optimization, Flow modeling and control, Low-rank approximation, Matrix completion problems, Nuclear norm regularization, Structured covariances. [bibtex-entry]



58. D. M. Zoltowski, N. K. Dhingra, F. Lin, and M. R. Jovanovic. Sparsity-promoting optimal control of spatially-invariant systems. In Proceedings of the 2014 American Control Conference, Portland, OR, pages 1261-1266, 2014. Keyword(s): Architectural issues in distributed control design, Cardinality minimization, Convex optimization, Distributed control, Sparsity-promoting optimal control, Spatially-invariant systems. [bibtex-entry]



59. Y. Chen, M. R. Jovanovic, and T. T. Georgiou. State covariances and the matrix completion problem. In Proceedings of the 52nd IEEE Conference on Decision and Control, Florence, Italy, pages 1702-1707, 2013. Keyword(s): Convex optimization, Low-rank approximation, Matrix completion problems, Nuclear norm regularization, Structured covariances. [bibtex-entry]



60. M. Fardad, F. Lin, X. Zhang, and M. R. Jovanovic. On new characterizations of social influence in social networks. In Proceedings of the 2013 American Control Conference, Washington, DC, pages 4784-4789, 2013. Keyword(s): Optimization, Social influence, Social networks, Stochastic matrices. [bibtex-entry]



61. M. R. Jovanovic and F. Lin. Sparse quadratic regulator. In Proceedings of the 12th European Control Conference, Zurich, Switzerland, pages 1047-1052, 2013. Keyword(s): Alternating direction method of multipliers, Cardinality minimization, Distributed control, Optimization, Sparsity-promoting optimal control. [bibtex-entry]



62. F. Lin, M. R. Jovanovic, and T. T. Georgiou. An ADMM algorithm for matrix completion of partially known state covariances. In Proceedings of the 52nd IEEE Conference on Decision and Control, Florence, Italy, pages 1684-1689, 2013. Keyword(s): Alternating direction method of multipliers, Convex optimization, Low-rank approximation, Matrix completion problems, Nuclear norm regularization, Structured covariances. [bibtex-entry]



63. M. Fardad, X. Zhang, F. Lin, and M. R. Jovanovic. On the optimal dissemination of information in social networks. In Proceedings of the 51th IEEE Conference on Decision and Control, Maui, HI, pages 2539-2544, 2012. Keyword(s): Optimization, Social influence, Social networks, Stochastic matrices. [bibtex-entry]



64. M. R. Jovanovic, P. J. Schmid, and J. W. Nichols. Low-rank and sparse dynamic mode decomposition. In Center for Turbulence Research Annual Research Briefs, pages 139-152, 2012. Keyword(s): Dynamic Mode Decomposition, Flow modeling and control, Convex optimization, Sparsity. [bibtex-entry]



65. M. Fardad, F. Lin, and M. R. Jovanovic. Algorithms for leader selection in large dynamical networks: noise-free leaders. In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, pages 7188-7193, 2011. Keyword(s): Consensus networks, Convex optimization, Leader selection, Performance bounds, Sparsity. [bibtex-entry]



66. F. Lin, M. Fardad, and M. R. Jovanovic. Algorithms for leader selection in large dynamical networks: noise-corrupted leaders. In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, pages 2932-2937, 2011. Keyword(s): Alternating direction method of multipliers, Consensus networks, Convex optimization, Greedy algorithm, Leader selection, Performance bounds, Sparsity. [bibtex-entry]



67. F. Lin and M. R. Jovanovic. On the least-squares approximation of structured covariances. In Proceedings of the 2007 American Control Conference, New York City, NY, pages 2648-2653, 2007. Keyword(s): Optimization, Large-scale systems, Least-squares approximation, Structured covariances. [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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