1. 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.
   

   @PHDTHESIS{dongsheng-phd22,
   AUTHOR = {D. Ding},
   SCHOOL = {University of Southern California},
   TITLE = {Provable reinforcement learning for constrained and multi-agent control systems},
   YEAR = {2022},
   KEYWORDS = {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},
   PDF = {https://viterbi-web.usc.edu/~mihailo/theses/DongshengDing-PhD.pdf} 
   }
   

1. H. A. Castillo, M. R. Jovanovic, S. Kumar, A. Morozov, V. Shankar, G. Subramanian, and H. J. Wilson. Understanding viscoelastic flow instabilities: Oldroyd-B and beyond. J. Non-Newtonian Fluid Mech., 302:104742 (39 pages), April 2022. Note: Part of the special issue commemorating the birth centenary of James Oldroyd. Keyword(s): Flow modeling and control, Input-output analysis, Elastic turbulence, Energy amplification, Transition to turbulence, Uncertainty quantification in PDEs, Spatio-temporal frequency responses, Viscoelastic fluids, Viscoelastic instabilities.
   

   @article{oldroyd100,
   author = {H. A. Castillo and M. R. Jovanovi\'c and S. Kumar and A. Morozov and V. Shankar and G. Subramanian and H. J. Wilson},
   title = {Understanding viscoelastic flow instabilities: {O}ldroyd-{B} and beyond},
   journal = {J. Non-Newtonian Fluid Mech.},
   volume = {302},
   pages = {104742 (39 pages)},
   month = {April},
   year = {2022},
   note = {{P}art of the special issue commemorating the birth centenary of {J}ames {O}ldroyd},
   PDF = {https://viterbi-web.usc.edu/~mihailo/papers/oldroyd100.pdf},
   keywords = {Flow modeling and control, Input-output analysis, Elastic turbulence, Energy amplification, Transition to turbulence, Uncertainty quantification in PDEs, Spatio-temporal frequency responses, Viscoelastic fluids, Viscoelastic instabilities} 
   }
   




2. 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.
   

   @ARTICLE{dhikhojovTAC22,
   AUTHOR = {N. K. Dhingra and S. Z. Khong and M. R. Jovanovi\'c},
   TITLE = {A second order primal-dual method for nonsmooth convex composite optimization},
   JOURNAL = {IEEE Trans. Automat. Control},
   VOLUME = {67},
   NUMBER = {8},
   PAGES = {4061-4076},
   MONTH = {August},
   YEAR = {2022},
   PDF = {https://viterbi-web.usc.edu/~mihailo/papers/dhikhojovTAC22.pdf},
   KEYWORDS = {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} 
   }
   




3. 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.
   

   @ARTICLE{dinzhaduabasjovJMLR22,
   AUTHOR = {D. Ding and K. Zhang and J. Duan and T. Basar and M. R. Jovanovi\'c},
   TITLE = {Convergence and sample complexity of natural policy gradient primal-dual methods for constrained {MDP}s },
   JOURNAL = {J. Mach. Learn. Res.},
   YEAR = {2022},
   NOTE = {submitted; also arXiv:2206.02346},
   PDF = {https://arxiv.org/abs/2206.02346},
   KEYWORDS = {Constrained Markov decision processes, Constrained nonconvex optimization, Function approximation, Natural policy gradient, Policy gradient methods, Primal-dual algorithms, Sample complexity} 
   }
   




4. A. Dwivedi, G. S. Sidharth, and M. R. Jovanovic. Oblique transition in hypersonic double-wedge flow. J. Fluid Mech., 948, October 2022. Keyword(s): Compressible flows, Direct numerical simulations, Double-wedge flow, Flow modeling and control, Hypersonic flows, Input-output analysis, Oblique waves, Shock boundary layer interaction, Reattachment vortices, Transition to turbulence, Weakly nonlinear analysis.
   

   @ARTICLE{dwisidjovJFM22,
   AUTHOR = {A. Dwivedi and G. S. Sidharth and M. R. Jovanovi\'c},
   JOURNAL = {J. Fluid Mech.},
   TITLE = {Oblique transition in hypersonic double-wedge flow},
   VOLUME = {948},
   MONTH = {October},
   YEAR = {2022},
   PDF = {https://viterbi-web.usc.edu/~mihailo/papers/dwisidjovJFM22.pdf},
   KEYWORDS = {Compressible flows, Direct numerical simulations, Double-wedge flow, Flow modeling and control, Hypersonic flows, Input-output analysis, Oblique waves, Shock boundary layer interaction, Reattachment vortices, Transition to turbulence, Weakly nonlinear analysis} 
   }
   




5. H. Mohammadi, M. Razaviyayn, and M. R. Jovanovic. Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms. 2022. Note: Submitted; also arXiv:2209.11920. Keyword(s): Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient descent, Heavy-ball method, Nesterov's accelerated method, Nonnormal dynamics, Noise amplification, Second-order moments.
   

   @ARTICLE{mohrazjovTAC22,
   AUTHOR = {H. Mohammadi and M. Razaviyayn and M. R. Jovanovi\'c},
   TITLE = {Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms},
   YEAR = {2022},
   NOTE = {submitted; also arXiv:2209.11920},
   PDF = {https://arxiv.org/abs/2209.11920},
   KEYWORDS = {Accelerated first-order algorithms, Control for optimization, Convergence rate, Convex optimization, Gradient descent, Heavy-ball method, Nesterov's accelerated method, Nonnormal dynamics, Noise amplification, Second-order moments} 
   }
   




6. H. Mohammadi, S. Samuelson, and M. R. Jovanovic. Transient growth of accelerated optimization algorithms. IEEE Trans. Automat. Control, 2022. Note: Doi:10.1109/TAC.2022.3162154. 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.
   

   @ARTICLE{mohsamjovTAC22,
   AUTHOR = {H. Mohammadi and S. Samuelson and M. R. Jovanovi\'c},
   TITLE = {Transient growth of accelerated optimization algorithms},
   JOURNAL = {IEEE Trans. Automat. Control},
   YEAR = {2022},
   NOTE = {doi:10.1109/TAC.2022.3162154},
   PDF = {https://arxiv.org/abs/2103.08017},
   KEYWORDS = {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} 
   }
   




7. 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.
   

   @ARTICLE{mohzarsoljovTAC22,
   AUTHOR = {H. Mohammadi and A. Zare and M. Soltanolkotabi and M. R. Jovanovi\'c},
   TITLE = {Convergence and sample complexity of gradient methods for the model-free linear-quadratic regulator problem},
   JOURNAL = {IEEE Trans. Automat. Control},
   VOLUME = {67},
   NUMBER = {5},
   PAGES = {2435-2450},
   MONTH = {May},
   YEAR = {2022},
   PDF = {https://viterbi-web.usc.edu/~mihailo/papers/mohzarsoljovTAC22.pdf},
   KEYWORDS = {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} 
   }
   

1. L. Ballotta, M. R. Jovanovic, and L. Schenato. Can decentralized control outperform centralized? The role of communication latency. In Proceedings of the 2022 IFAC Conference on Networked Systems, Zurich, Switzerland, 2022. Keyword(s): Controller architecture, Fundamental limitations, Networks, Networks of dynamical systems, Noise amplification, Performance bounds, Topology design.
   

   @INPROCEEDINGS{baljovschIFAC22,
   AUTHOR = {L. Ballotta and M. R. Jovanovi\'c and L. Schenato},
   TITLE = {Can decentralized control outperform centralized? {T}he role of communication latency},
   BOOKTITLE = {Proceedings of the 2022 IFAC Conference on Networked Systems},
   YEAR = {2022},
   ADDRESS = {Zurich, Switzerland},
   PDF = {https://viterbi-web.usc.edu/~mihailo/papers/baljovschIFAC22.pdf},
   KEYWORDS = {Controller architecture, Fundamental limitations, Networks, Networks of dynamical systems, Noise amplification, Performance bounds, Topology design} 
   }
   




2. D. Ding and M. R. Jovanovic. Policy gradient primal-dual mirror descent for constrained MDPs with large state spaces. In Proceedings of the 61st IEEE Conference on Decision and Control, Cancun, Mexico, 2022. Note: To appear. Keyword(s): Constrained Markov decision processes, Policy gradient methods, Primal-dual algorithms, Mirror descent, Function approximation.
   

   @INPROCEEDINGS{dinjovCDC22,
   AUTHOR = {D. Ding and M. R. Jovanovi\'c},
   BOOKTITLE = {Proceedings of the 61st IEEE Conference on Decision and Control},
   TITLE = {Policy gradient primal-dual mirror descent for constrained {MDP}s with large state spaces},
   YEAR = {2022},
   ADDRESS = {Cancun, Mexico},
   NOTE = {to appear},
   KEYWORDS = {Constrained Markov decision processes, Policy gradient methods, Primal-dual algorithms, Mirror descent, Function approximation} 
   }
   




3. D. Ding, C.-Y. Wei, K. Zhang, and M. R. Jovanovic. Independent policy gradient for large-scale Markov potential games: sharper rates, function approximation, and game-agnostic convergence. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, Baltimore, MD, pages 5166-5220, 2022. Keyword(s): Multi-agent reinforcement learning, Independent reinforcement learning, Policy gradient methods, Markov potential games, Function approximation, Game-agnostic convergence.
   

   @INPROCEEDINGS{dinweizhajovICML22,
   AUTHOR = {D. Ding and C.-Y. Wei and K. Zhang and M. R. Jovanovi\'c},
   TITLE = {Independent policy gradient for large-scale {M}arkov potential games: sharper rates, function approximation, and game-agnostic convergence},
   BOOKTITLE = {Proceedings of the 39th International Conference on Machine Learning},
   SERIES = {Proceedings of Machine Learning Research},
   VOLUME = {162},
   PAGES = {5166-5220},
   YEAR = {2022},
   ADDRESS = {Baltimore, MD},
   PDF = {https://viterbi-web.usc.edu/~mihailo/papers/dinweizhajovICML22.pdf},
   KEYWORDS = {Multi-agent reinforcement learning, Independent reinforcement learning, Policy gradient methods, Markov potential games, Function approximation, Game-agnostic convergence} 
   }
   




4. D. Ding, K. Zhang, T. Basar, and M. R. Jovanovic. Convergence and optimality of policy gradient primal-dual method for constrained Markov decision processes. In Proceedings of the 2022 American Control Conference, Atlanta, GA, pages 2851-2856, 2022. Keyword(s): Constrained Markov decision processes, Policy gradient methods, Primal-dual algorithms.
   

   @INPROCEEDINGS{dinzhabasjovACC22,
   AUTHOR = {D. Ding and K. Zhang and T. Basar and M. R. Jovanovi\'c},
   TITLE = {Convergence and optimality of policy gradient primal-dual method for constrained {M}arkov decision processes},
   BOOKTITLE = {Proceedings of the 2022 American Control Conference},
   PAGES = {2851-2856},
   YEAR = {2022},
   ADDRESS = {Atlanta, GA},
   PDF = {https://viterbi-web.usc.edu/~mihailo/papers/dinzhabasjovACC22.pdf},
   KEYWORDS = {Constrained Markov decision processes, Policy gradient methods, Primal-dual algorithms} 
   }
   




5. A. Dwivedi and M. R. Jovanovic. A weakly nonlinear analysis of transition in a hypersonic flow. In Proceedings of the 2022 American Control Conference, Atlanta, GA, pages 4171-4176, 2022. Keyword(s): Compressible flows, Direct numerical simulations, Double-wedge flow, Flow modeling and control, Hypersonic flows, Input-output analysis, Shock boundary layer interaction, Reattachment vortices, Transition to turbulence, Weakly nonlinear analysis.
   

   @INPROCEEDINGS{dwijovACC22,
   AUTHOR = {A. Dwivedi and M. R. Jovanovi\'c},
   TITLE = {A weakly nonlinear analysis of transition in a hypersonic flow},
   BOOKTITLE = {Proceedings of the 2022 American Control Conference},
   PAGES = {4171-4176},
   YEAR = {2022},
   ADDRESS = {Atlanta, GA},
   PDF = {https://viterbi-web.usc.edu/~mihailo/papers/dwijovACC22.pdf},
   KEYWORDS = {Compressible flows, Direct numerical simulations, Double-wedge flow, Flow modeling and control, Hypersonic flows, Input-output analysis, Shock boundary layer interaction, Reattachment vortices, Transition to turbulence, Weakly nonlinear analysis} 
   }
   




6. 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.
   

   @INPROCEEDINGS{mohjovACC22,
   AUTHOR = {H. Mohammadi and M. R. Jovanovi\'c},
   TITLE = {On the noise amplification of primal-dual gradient flow dynamics based on proximal augmented {L}agrangian},
   BOOKTITLE = {Proceedings of the 2022 American Control Conference},
   PAGES = {926-931},
   YEAR = {2022},
   ADDRESS = {Atlanta, GA},
   PDF = {https://viterbi-web.usc.edu/~mihailo/papers/mohjovACC22.pdf},
   KEYWORDS = {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} 
   }
   




7. 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.
   

   @INPROCEEDINGS{ozamogjovACC22,
   AUTHOR = {I. K. Ozaslan and S. Hassan-Moghaddam and M. R. Jovanovi\'c},
   TITLE = {On the asymptotic stability of proximal algorithms for convex optimization problems with multiple non-smooth regularizers},
   BOOKTITLE = {Proceedings of the 2022 American Control Conference},
   PAGES = {132-137},
   YEAR = {2022},
   ADDRESS = {Atlanta, GA},
   PDF = {https://viterbi-web.usc.edu/~mihailo/papers/ozamogjovACC22.pdf},
   KEYWORDS = {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} 
   }
   




8. 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.
   

   @INPROCEEDINGS{ozajovCDC22,
   AUTHOR = {I. K. Ozaslan and M. R. Jovanovi\'c},
   TITLE = {Exponential convergence of primal-dual dynamics for multi-block problems under local error bound condition},
   BOOKTITLE = {Proceedings of the 61th IEEE Conference on Decision and Control},
   YEAR = {2022},
   ADDRESS = {Cancun, Mexico},
   NOTE = {to appear},
   KEYWORDS = {Gradient flow dynamics, Lyapunov functions, Proximal algorithms, Primal-dual gradient flow dynamics, Primal-dual methods, Proximal augmented Lagrangian, Operator splitting} 
   }
   

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