
G. Hariharan,
M. R. Jovanovic,
and S. Kumar.
Localized stress amplification in inertialess channel flows of viscoelastic fluids.
J. NonNewtonian Fluid Mech.,
291:104514 (14 pages),
May 2021.
Keyword(s): Flow modeling and control,
Viscoelastic fluids,
Inputoutput analysis,
Elastic turbulence,
Transition to turbulence,
Uncertainty quantification in PDEs,
Spatiotemporal impulse responses.
@article{harjovkumJNNFM21,
author = {G. Hariharan and M. R. Jovanovi\'c and S. Kumar },
title = {Localized stress amplification in inertialess channel flows of viscoelastic fluids},
journal = {J. NonNewtonian Fluid Mech.},
volume = {291},
pages = {104514 (14 pages)},
month = {May},
year = {2021},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/harjovkumJNNFM21.pdf},
keywords = {Flow modeling and control, Viscoelastic fluids, Inputoutput analysis, Elastic turbulence, Transition to turbulence, Uncertainty quantification in PDEs, Spatiotemporal impulse responses}
}

G. Hariharan,
S. Kumar,
and M. R. Jovanovic.
Wellconditioned ultraspherical and spectral integration methods for resolvent analysis of channel flows of Newtonian and viscoelastic fluids.
J. Comput. Phys.,
439:110241 (25 pages),
August 2021.
Keyword(s): Distributed systems theory,
Computational tools for spatially distributed systems,
Flow modeling and control,
Inputoutput analysis,
Spatiotemporal frequency responses,
Spectral integration method,
Uncertainty quantification in PDEs.
@article{harkumjovJCP21,
author = {G. Hariharan and S. Kumar and M. R. Jovanovi\'c},
title = {Wellconditioned ultraspherical and spectral integration methods for resolvent analysis of channel flows of {N}ewtonian and viscoelastic fluids},
journal = {J. Comput. Phys.},
volume = {439},
pages = {110241 (25 pages)},
month = {August},
year = {2021},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/harkumjovJCP21.pdf},
keywords = {Distributed systems theory, Computational tools for spatially distributed systems, Flow modeling and control, Inputoutput analysis, Spatiotemporal frequency responses, Spectral integration method, Uncertainty quantification in PDEs}
}

S. HassanMoghaddam and M. R. Jovanovic.
Proximal gradient flow and DouglasRachford splitting dynamics: global exponential stability via integral quadratic constraints.
Automatica,
123:109311,
January 2021.
Keyword(s): Control for optimization,
Convex Optimization,
Forwardbackward envelope,
DouglasRachford splitting,
Global exponential stability,
Integral quadratic constraints,
Nonsmooth optimization,
PolyakLojasiewicz inequality,
Proximal algorithms,
Primaldual methods,
Proximal augmented Lagrangian.
@ARTICLE{mogjovAUT21,
AUTHOR = {S. HassanMoghaddam and M. R. Jovanovi\'c},
TITLE = {Proximal gradient flow and {D}ouglas{R}achford splitting dynamics: global exponential stability via integral quadratic constraints},
JOURNAL = {Automatica},
VOLUME = {123},
PAGES = {109311},
MONTH = {January},
YEAR = {2021},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/mogjovAUT21.pdf},
KEYWORDS = {Control for optimization, Convex Optimization, Forwardbackward envelope, DouglasRachford splitting, Global exponential stability, Integral quadratic constraints, Nonsmooth optimization, PolyakLojasiewicz inequality, Proximal algorithms, Primaldual methods, Proximal augmented Lagrangian}
}

M. R. Jovanovic.
From bypass transition to flow control and datadriven turbulence modeling: An inputoutput viewpoint.
Annu. Rev. Fluid Mech.,
53(1):311345,
January 2021.
Keyword(s): Colored noise,
Convex optimization,
Drag reduction,
Energy amplification,
Flow modeling and control,
Inputoutput analysis,
Lowcomplexity modeling,
Lowrank approximation,
Matrix completion problems,
NavierStokes equations,
Nuclear norm regularization,
Simulationfree design,
Structured covariances,
Transition to turbulence,
Turbulence modeling.
@ARTICLE{jovARFM21,
AUTHOR = {M. R. Jovanovi\'c},
TITLE = {From bypass transition to flow control and datadriven turbulence modeling: {A}n inputoutput viewpoint},
JOURNAL = {Annu. Rev. Fluid Mech.},
VOLUME = {53},
NUMBER = {1},
PAGES = {311345},
MONTH = {January},
YEAR = {2021},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/jovARFM21.pdf},
KEYWORDS = {Colored noise, Convex optimization, Drag reduction, Energy amplification, Flow modeling and control, Inputoutput analysis, Lowcomplexity modeling, Lowrank approximation, Matrix completion problems, NavierStokes equations, Nuclear norm regularization, Simulationfree design, Structured covariances, Transition to turbulence, Turbulence modeling}
}

H. Mohammadi,
M. Razaviyayn,
and M. R. Jovanovic.
Robustness of accelerated firstorder algorithms for strongly convex optimization problems.
IEEE Trans. Automat. Control,
66(6):24802495,
June 2021.
Keyword(s): Accelerated firstorder algorithms,
Consensus networks,
Control for optimization,
Convex optimization,
Integral quadratic constraints,
Linear matrix inequalities,
Noise amplification,
Secondorder moments,
Semidefinite programming.
@ARTICLE{mohrazjovTAC21,
AUTHOR = {H. Mohammadi and M. Razaviyayn and M. R. Jovanovi\'c},
TITLE = {Robustness of accelerated firstorder algorithms for strongly convex optimization problems},
JOURNAL = {IEEE Trans. Automat. Control},
VOLUME = {66},
NUMBER = {6},
PAGES = {24802495},
MONTH = {June},
YEAR = {2021},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/mohrazjovTAC21.pdf},
KEYWORDS = {Accelerated firstorder algorithms, Consensus networks, Control for optimization, Convex optimization, Integral quadratic constraints, Linear matrix inequalities, Noise amplification, Secondorder moments, Semidefinite programming}
}

H. Mohammadi,
M. Soltanolkotabi,
and M. R. Jovanovic.
On the linear convergence of random search for discretetime LQR.
IEEE Control Syst. Lett.,
5(3):989994,
July 2021.
Keyword(s): Datadriven control,
Gradient descent,
Linear quadratic regulator,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
Random search method,
Reinforcement learning,
Sample complexity.
@ARTICLE{mohsoljovLCSS21,
AUTHOR = {H. Mohammadi and M. Soltanolkotabi and M. R. Jovanovi\'c},
TITLE = {On the linear convergence of random search for discretetime {LQR}},
JOURNAL = {IEEE Control Syst. Lett.},
VOLUME = {5},
NUMBER = {3},
PAGES = {989994},
MONTH = {July},
YEAR = {2021},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/mohsoljovLCSS21.pdf},
KEYWORDS = {Datadriven control, Gradient descent, Linear quadratic regulator, Modelfree control, Nonconvex optimization, Optimization, Optimal control, Random search method, Reinforcement learning, Sample complexity}
}

W. Ran,
A. Zare,
and M. R. Jovanovic.
Modelbased design of riblets for turbulent drag reduction.
J. Fluid Mech.,
906:A7 (38 pages),
January 2021.
Keyword(s): Drag reduction,
Riblets,
Sensorfree flow control,
Spatiallyperiodic systems,
Spatiotemporal frequency responses,
Stochasticallyforced NavierStokes equations,
Turbulence modeling.
@ARTICLE{ranzarjovJFM21,
AUTHOR = {W. Ran and A. Zare and M. R. Jovanovi\'c},
TITLE = {Modelbased design of riblets for turbulent drag reduction},
JOURNAL = {J. Fluid Mech.},
YEAR = {2021},
MONTH = {January},
VOLUME = {906},
PAGES = {A7 (38 pages)},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/ranzarjovJFM21.pdf},
KEYWORDS = {Drag reduction, Riblets, Sensorfree flow control, Spatiallyperiodic systems, Spatiotemporal frequency responses, Stochasticallyforced NavierStokes equations, Turbulence modeling}
}

L. Ballotta,
M. R. Jovanovic,
and L. Schenato.
Optimal network topology of multiagent systems subject to computation and communication latency.
In Proceedings of the 29th Mediterranean Conference on Control and Automation,
Bari, Italy,
pages 249254,
2021.
Keyword(s): Controller architecture,
Fundamental limitations,
Networks,
Networks of dynamical systems,
Noise amplification,
Performance bounds,
Topology design.
@INPROCEEDINGS{baljovschMED21,
AUTHOR = {L. Ballotta and M. R. Jovanovi\'c and L. Schenato},
TITLE = {Optimal network topology of multiagent systems subject to computation and communication latency},
BOOKTITLE = {Proceedings of the 29th Mediterranean Conference on Control and Automation},
PAGES = {249254},
YEAR = {2021},
ADDRESS = {Bari, Italy},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/baljovschMED21.pdf},
KEYWORDS = {Controller architecture, Fundamental limitations, Networks, Networks of dynamical systems, Noise amplification, Performance bounds, Topology design}
}

D. Ding,
X. Wei,
Z. Yang,
Z. Wang,
and M. R. Jovanovic.
Provably efficient safe exploration via primaldual policy optimization.
In 24th International Conference on Artificial Intelligence and Statistics,
volume 130,
Virtual,
pages 33043312,
2021.
Keyword(s): Safe reinforcement learning,
Constrained Markov decision processes,
Safe exploration,
Proximal policy optimization,
Nonconvex optimization,
Online mirror descent,
Primaldual method.
@INPROCEEDINGS{dinweiyanwanjovAISTATS21,
AUTHOR = {D. Ding and X. Wei and Z. Yang and Z. Wang and M. R. Jovanovi\'c},
BOOKTITLE = {24th International Conference on Artificial Intelligence and Statistics},
TITLE = {Provably efficient safe exploration via primaldual policy optimization},
YEAR = {2021},
ADDRESS = {Virtual},
VOLUME = {130},
PAGES = {33043312},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/dinweiyanwanjovAISTATS21.pdf},
KEYWORDS = {Safe reinforcement learning, Constrained Markov decision processes, Safe exploration, Proximal policy optimization, Nonconvex optimization, Online mirror descent, Primaldual method}
}

D. Ding,
X. Wei,
H. Yu,
and M. R. Jovanovic.
Byzantineresilient distributed learning under constraints.
In Proceedings of the 2021 American Control Conference,
New Orleans, LA,
pages 22602265,
2021.
Keyword(s): Byzantine primaldual optimization,
Constrained optimization,
Distributed optimization,
Robust statistical learning.
@INPROCEEDINGS{dinweiyujovACC21,
AUTHOR = {D. Ding and X. Wei and H. Yu and M. R. Jovanovi\'c},
BOOKTITLE = {Proceedings of the 2021 American Control Conference},
TITLE = {Byzantineresilient distributed learning under constraints},
YEAR = {2021},
ADDRESS = {New Orleans, LA},
PAGES = {22602265},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/dinweiyujovACC21.pdf},
KEYWORDS = {Byzantine primaldual optimization, Constrained optimization, Distributed optimization, Robust statistical learning}
}

D. Ding,
J. Yuan,
and M. R. Jovanovic.
Discounted online Newton method for timevarying time series prediction.
In Proceedings of the 2021 American Control Conference,
New Orleans, LA,
pages 15471552,
2021.
Keyword(s): Discounted online Newton method,
Timevarying time series prediction,
Online learning,
COVID19 prediction.
@INPROCEEDINGS{dinyuajovACC21,
AUTHOR = {D. Ding and J. Yuan and M. R. Jovanovi\'c},
BOOKTITLE = {Proceedings of the 2021 American Control Conference},
TITLE = {Discounted online {N}ewton method for timevarying time series prediction},
YEAR = {2021},
ADDRESS = {New Orleans, LA},
PAGES = {15471552},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/dinyuajovACC21.pdf},
KEYWORDS = {Discounted online Newton method, Timevarying time series prediction, Online learning, COVID19 prediction}
}

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 11201124,
2021.
Keyword(s): Datadriven control,
Gradient descent,
Gradientflow dynamics,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
PolyakLojasiewicz inequality,
Random search method,
Reinforcement learning,
Sample complexity.
@INPROCEEDINGS{mohsoljovCDC21,
AUTHOR = {H. Mohammadi and M. Soltanolkotabi and M. R. Jovanovi\'c},
TITLE = {On the lack of gradient domination for linear quadratic {G}aussian problems with incomplete state information},
BOOKTITLE = {Proceedings of the 60th IEEE Conference on Decision and Control},
PAGES = {11201124},
YEAR = {2021},
ADDRESS = {Austin, TX},
PDF = {https://viterbiweb.usc.edu/~mihailo/papers/mohsoljovCDC21.pdf},
KEYWORDS = {Datadriven control, Gradient descent, Gradientflow dynamics, Modelfree control, Nonconvex optimization, Optimization, Optimal control, PolyakLojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity}
}