
H. A. Castillo,
M. R. Jovanovic,
S. Kumar,
A. Morozov,
V. Shankar,
G. Subramanian,
and H. J. Wilson.
Understanding viscoelastic flow instabilities: OldroydB and beyond.
J. NonNewtonian 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,
Inputoutput analysis,
Elastic turbulence,
Energy amplification,
Transition to turbulence,
Uncertainty quantification in PDEs,
Spatiotemporal 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. NonNewtonian 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://viterbiweb.usc.edu/~mihailo/papers/oldroyd100.pdf},
keywords = {Flow modeling and control, Inputoutput analysis, Elastic turbulence, Energy amplification, Transition to turbulence, Uncertainty quantification in PDEs, Spatiotemporal frequency responses, Viscoelastic fluids, Viscoelastic instabilities}
}

N. K. Dhingra,
S. Z. Khong,
and M. R. Jovanovic.
A second order primaldual method for nonsmooth convex composite optimization.
IEEE Trans. Automat. Control,
2022.
Note: Doi:10.1109/TAC.2021.3115449; also arXiv:1709.01610.
Keyword(s): Augmented Lagrangian,
Exponential convergence,
Global exponential stability,
Method of multipliers,
Nonsmooth optimization,
Proximal algorithms,
Proximal augmented Lagrangian,
Regularization for design,
Second order primaldual method,
Sparsitypromoting 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 primaldual method for nonsmooth convex composite optimization},
JOURNAL = {IEEE Trans. Automat. Control},
YEAR = {2022},
NOTE = {doi:10.1109/TAC.2021.3115449; also arXiv:1709.01610},
PDF = {https://arxiv.org/abs/1709.01610},
KEYWORDS = {Augmented Lagrangian, Exponential convergence, Global exponential stability, Method of multipliers, Nonsmooth optimization, Proximal algorithms, Proximal augmented Lagrangian, Regularization for design, Second order primaldual method, Sparsitypromoting optimal control, Structured optimal control, Structure identification}
}

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; also arXiv:2103.08017.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convex optimization,
Gradient descent,
Heavyball 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; also arXiv:2103.08017},
PDF = {https://arxiv.org/abs/2103.08017},
KEYWORDS = {Accelerated firstorder algorithms, Control for optimization, Convex optimization, Gradient descent, Heavyball method, Integral quadratic constraints, Nesterov's accelerated method, Nonnormal dynamics, Transient growth}
}

H. Mohammadi,
A. Zare,
M. Soltanolkotabi,
and M. R. Jovanovic.
Convergence and sample complexity of gradient methods for the modelfree linearquadratic regulator problem.
IEEE Trans. Automat. Control,
67(5):24352450,
May 2022.
Keyword(s): Datadriven control,
Gradient descent,
Gradientflow dynamics,
Linear quadratic regulator,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
PolyakLojasiewicz 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 modelfree linearquadratic regulator problem},
JOURNAL = {IEEE Trans. Automat. Control},
VOLUME = {67},
NUMBER = {5},
PAGES = {24352450},
MONTH = {May},
YEAR = {2022},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/mohzarsoljovTAC22.pdf},
KEYWORDS = {Datadriven control, Gradient descent, Gradientflow dynamics, Linear quadratic regulator, Modelfree control, Nonconvex optimization, Optimization, Optimal control, PolyakLojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity}
}

I. K. Ozaslan,
H. Mohammadi,
and M. R. Jovanovic.
Computing stabilizing feedback gains via a modelfree policy gradient.
IEEE Control Syst. Lett.,
2022.
Note: Submitted.
Keyword(s): Datadriven control,
Gradient descent,
Linear quadratic regulator,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
Random search method,
Reinforcement learning,
Sample complexity.
@ARTICLE{ozamohjovLCSS22,
AUTHOR = {I. K. Ozaslan and H. Mohammadi and M. R. Jovanovi\'c},
TITLE = {Computing stabilizing feedback gains via a modelfree policy gradient},
JOURNAL = {IEEE Control Syst. Lett.},
YEAR = {2022},
NOTE = {submitted},
KEYWORDS = {Datadriven control, Gradient descent, Linear quadratic regulator, Modelfree control, Nonconvex optimization, Optimization, Optimal control, Random search method, Reinforcement learning, Sample complexity}
}

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.
Note: To appear.
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},
NOTE = {to appear},
KEYWORDS = {Controller architecture, Fundamental limitations, Networks, Networks of dynamical systems, Noise amplification, Performance bounds, Topology design}
}

D. Ding and M. R. Jovanovic.
Policy gradient primaldual mirror descent for constrained MDPs with large state spaces.
In Proceedings of the 61st IEEE Conference on Decision and Control,
Cancun, Mexico,
2022.
Note: Submitted.
Keyword(s): Constrained Markov decision processes,
Policy gradient methods,
Primaldual 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 primaldual mirror descent for constrained {MDP}s with large state spaces},
YEAR = {2022},
ADDRESS = {Cancun, Mexico},
NOTE = {submitted},
KEYWORDS = {Constrained Markov decision processes, Policy gradient methods, Primaldual algorithms, Mirror descent, Function approximation}
}

D. Ding,
X. Wei,
Z. Yang,
Z. Wang,
and M. R. Jovanovic.
Provably efficient generalized Lagrangian policy optimization for safe multiagent reinforcement learning.
In 39th International Conference on Machine Learning,
Baltimore, MD,
2022.
Note: Submitted.
Keyword(s): Multiagent reinforcement learning,
Constrained Markov games,
Primaldual policy optimization,
Method of Lagrange multipliers,
Minimax optimization.
@INPROCEEDINGS{dinweiyanwanjovICML22,
AUTHOR = {D. Ding and X. Wei and Z. Yang and Z. Wang and M. R. Jovanovi\'c},
BOOKTITLE = {39th International Conference on Machine Learning},
TITLE = {Provably efficient generalized {L}agrangian policy optimization for safe multiagent reinforcement learning},
YEAR = {2022},
ADDRESS = {Baltimore, MD},
NOTE = {submitted},
KEYWORDS = {Multiagent reinforcement learning, Constrained {M}arkov games, Primaldual policy optimization, Method of {L}agrange multipliers, Minimax optimization}
}

D. Ding,
C.Y. Wei,
K. Zhang,
and M. R. Jovanovic.
Independent policy gradient for largescale Markov potential games: sharper rates, function approximation, and gameagnostic convergence.
In 39th International Conference on Machine Learning,
Baltimore, MD,
2022.
Note: Submitted.
Keyword(s): Multiagent reinforcement learning,
Independent reinforcement learning,
Policy gradient methods,
Markov potential games,
Function approximation,
Gameagnostic convergence.
@INPROCEEDINGS{dinweizhajovICML22,
AUTHOR = {D. Ding and C.Y. Wei and K. Zhang and M. R. Jovanovi\'c},
BOOKTITLE = {39th International Conference on Machine Learning},
TITLE = {Independent policy gradient for largescale {M}arkov potential games: sharper rates, function approximation, and gameagnostic convergence},
YEAR = {2022},
ADDRESS = {Baltimore, MD},
NOTE = {submitted},
KEYWORDS = {Multiagent reinforcement learning, Independent reinforcement learning, Policy gradient methods, Markov potential games, Function approximation, Gameagnostic convergence}
}

D. Ding,
K. Zhang,
T. Basar,
and M. R. Jovanovic.
Convergence and optimality of policy gradient primaldual method for constrained Markov decision processes.
In Proceedings of the 2022 American Control Conference,
Atlanta, GA,
2022.
Note: To appear.
Keyword(s): Constrained Markov decision processes,
Policy gradient methods,
Primaldual algorithms,
Minimax optimization.
@INPROCEEDINGS{dinzhabasjovACC22,
AUTHOR = {D. Ding and K. Zhang and T. Basar and M. R. Jovanovi\'c},
TITLE = {Convergence and optimality of policy gradient primaldual method for constrained {M}arkov decision processes},
BOOKTITLE = {Proceedings of the 2022 American Control Conference},
YEAR = {2022},
ADDRESS = {Atlanta, GA},
NOTE = {to appear},
KEYWORDS = {Constrained Markov decision processes, Policy gradient methods, Primaldual algorithms, Minimax optimization}
}

A. Dwivedi and M. R. Jovanovic.
A weakly nonlinear analysis of responses of a hypersonic flow over a doublewedge to oblique disturbances.
In Proceedings of the 2022 American Control Conference,
Atlanta, GA,
2022.
Note: To appear.
Keyword(s): Compressible flows,
Direct numerical simulations,
Doublewedge flow,
Flow modeling and control,
Hypersonic flows,
InputOutput 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 responses of a hypersonic flow over a doublewedge to oblique disturbances},
BOOKTITLE = {Proceedings of the 2022 American Control Conference},
YEAR = {2022},
ADDRESS = {Atlanta, GA},
NOTE = {to appear},
KEYWORDS = {Compressible flows, Direct numerical simulations, Doublewedge flow, Flow modeling and control, Hypersonic flows, InputOutput analysis, Shock Boundary layer interaction, Reattachment vortices, Transition to turbulence, Weakly nonlinear analysis}
}

H. Mohammadi and M. R. Jovanovic.
On the noise amplification of primaldual gradient flow dynamics based on proximal augmented Lagrangian.
In Proceedings of the 2022 American Control Conference,
Atlanta, GA,
2022.
Note: To appear.
Keyword(s): Control for optimization,
Convex Optimization,
Integral quadratic constraints,
Linear matrix inequalities,
Noise amplification,
Nonsmooth optimization,
Proximal algorithms,
Primaldual gradient flow dynamics,
Primaldual methods,
Proximal augmented Lagrangian,
Secondorder moments,
Semidefinite programming.
@INPROCEEDINGS{mohjovACC22,
AUTHOR = {H. Mohammadi and M. R. Jovanovi\'c},
TITLE = {On the noise amplification of primaldual gradient flow dynamics based on proximal augmented {L}agrangian},
BOOKTITLE = {Proceedings of the 2022 American Control Conference},
YEAR = {2022},
ADDRESS = {Atlanta, GA},
NOTE = {to appear},
KEYWORDS = {Control for optimization, Convex Optimization, Integral quadratic constraints, Linear matrix inequalities, Noise amplification, Nonsmooth optimization, Proximal algorithms, Primaldual gradient flow dynamics, Primaldual methods, Proximal augmented Lagrangian, Secondorder moments, Semidefinite programming}
}

H. Mohammadi and M. R. Jovanovic.
Tradeoffs between convergence rate and noise amplification for momentumbased accelerated optimization algorithms.
In Proceedings of the 61th IEEE Conference on Decision and Control,
Cancun, Mexico,
2022.
Note: Submitted.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convergence rate,
Convex optimization,
Noise amplification,
Secondorder moments,
Triple momentum method.
@INPROCEEDINGS{mohjovCDC22,
AUTHOR = {H. Mohammadi and M. R. Jovanovi\'c},
TITLE = {Tradeoffs between convergence rate and noise amplification for momentumbased accelerated optimization algorithms},
BOOKTITLE = {Proceedings of the 61th IEEE Conference on Decision and Control},
YEAR = {2022},
ADDRESS = {Cancun, Mexico},
NOTE = {submitted},
KEYWORDS = {Accelerated firstorder algorithms, Control for optimization, Convergence rate, Convex optimization, Noise amplification, Secondorder moments, Triple momentum method}
}

I. K. Ozaslan,
S. HassanMoghaddam,
and M. R. Jovanovic.
On the asymptotic stability of proximal algorithms for convex optimization problems with multiple nonsmooth regularizers.
In Proceedings of the 2022 American Control Conference,
Atlanta, GA,
2022.
Note: To appear.
Keyword(s): Control for optimization,
Convex Optimization,
DouglasRachford splitting,
Global asymptotic stability,
Lyapunovbased analysis,
Nonsmooth optimization,
Proximal algorithms,
Primaldual gradient flow dynamics,
Primaldual methods,
Proximal augmented Lagrangian.
@INPROCEEDINGS{ozamogjovACC22,
AUTHOR = {I. K. Ozaslan and S. HassanMoghaddam and M. R. Jovanovi\'c},
TITLE = {On the asymptotic stability of proximal algorithms for convex optimization problems with multiple nonsmooth regularizers},
BOOKTITLE = {Proceedings of the 2022 American Control Conference},
YEAR = {2022},
ADDRESS = {Atlanta, GA},
NOTE = {to appear},
KEYWORDS = {Control for optimization, Convex Optimization, DouglasRachford splitting, Global asymptotic stability, Lyapunovbased analysis, Nonsmooth optimization, Proximal algorithms, Primaldual gradient flow dynamics, Primaldual methods, Proximal augmented Lagrangian}
}

I. K. Ozaslan and M. R. Jovanovic.
Exponential convergence of primaldual dynamics for multiblock problems under local error bound condition.
In Proceedings of the 61th IEEE Conference on Decision and Control,
Cancun, Mexico,
2022.
Note: Submitted.
Keyword(s): Gradient flow dynamics,
Lyapunov functions,
Proximal algorithms,
Primaldual gradient flow dynamics,
Primaldual methods,
Proximal augmented Lagrangian,
Operator splitting.
@INPROCEEDINGS{ozajovCDC22,
AUTHOR = {I. K. Ozaslan and M. R. Jovanovi\'c},
TITLE = {Exponential convergence of primaldual dynamics for multiblock problems under local error bound condition},
BOOKTITLE = {Proceedings of the 61th IEEE Conference on Decision and Control},
YEAR = {2022},
ADDRESS = {Cancun, Mexico},
NOTE = {submitted},
KEYWORDS = {Gradient flow dynamics, Lyapunov functions, Proximal algorithms, Primaldual gradient flow dynamics, Primaldual methods, Proximal augmented Lagrangian, Operator splitting}
}