Back to MJ's Publications
Publications about 'Optimization'


D. Ding.
Provable reinforcement learning for constrained and multiagent control systems.
PhD thesis,
University of Southern California,
2022.
Keyword(s): Constrained Markov decision processes,
Constrained nonconvex optimization,
Function approximation,
Gameagnostic convergence,
Multiagent reinforcement learning,
Multiagent systems,
Natural policy gradient,
Policy gradient methods,
Proximal policy optimization,
Primaldual algorithms,
Reinforcement learning,
Safe exploration,
Safe reinforcement learning,
Sample complexity,
Stochastic optimization.
[bibtexentry]

S. HassanMoghaddam.
Analysis, design, and optimization of largescale networks of dynamical systems.
PhD thesis,
University of Southern California,
2019.
Keyword(s): Consensus,
Control for optimization,
Convex Optimization,
Distributed control,
Forwardbackward envelope,
DouglasRachford splitting,
Global exponential stability,
Integral quadratic constraints,
Networks of dynamical systems,
Nonsmooth optimization,
PolyakLojasiewicz inequality,
Proximal algorithms,
Primaldual methods,
Proximal augmented Lagrangian,
Regularization for design,
Sparse graphs,
Sparsitypromoting optimal control,
Structured optimal control,
Structure identification,
Topology design.
[bibtexentry]

N. K. Dhingra.
Optimization and control of largescale networked systems.
PhD thesis,
University of Minnesota,
2017.
Keyword(s): Augmented Lagrangian,
Combination drug therapy,
Convex optimization,
Directed networks,
Leader selection,
Method of multipliers,
Nonsmooth optimization,
Optimization,
Proximal algorithms,
Proximal augmented Lagrangian,
Regularization,
Second order primaldual method,
Sparsitypromoting optimal control,
Structured optimal control,
Structure identification.
[bibtexentry]

A. Zare.
Lowcomplexity stochastic modeling of wallbounded shear flows.
PhD thesis,
University of Minnesota,
2016.
Keyword(s): Alternating minimization algorithm,
Colored noise,
Convex optimization,
Disturbance dynamics,
Flow modeling and control,
Lowcomplexity modeling,
Lowrank approximation,
Matrix completion problem,
Nuclear norm regularization,
Structured covariances,
Turbulence modeling.
[bibtexentry]

F. Lin.
Structure identification and optimal design of largescale 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,
Sparsitypromoting optimal control.
[bibtexentry]

I. K. Ozaslan,
H. Mohammadi,
and M. R. Jovanovic.
Computing stabilizing feedback gains via a modelfree policy gradient method.
IEEE Control Syst. Lett.,
7:407412,
July 2023.
Keyword(s): Datadriven control,
Gradient descent,
Linear quadratic regulator,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
Random search method,
Reinforcement learning,
Sample complexity.
[bibtexentry]

N. K. Dhingra,
S. Z. Khong,
and M. R. Jovanovic.
A second order primaldual method for nonsmooth convex composite optimization.
IEEE Trans. Automat. Control,
67(8):40614076,
August 2022.
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.
[bibtexentry]

D. Ding,
K. Zhang,
J. Duan,
T. Basar,
and M. R. Jovanovic.
Convergence and sample complexity of natural policy gradient primaldual 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,
Primaldual algorithms,
Sample complexity.
[bibtexentry]

H. Mohammadi,
M. Razaviyayn,
and M. R. Jovanovic.
Tradeoffs between convergence rate and noise amplification for momentumbased accelerated optimization algorithms.
2022.
Note: Submitted; also arXiv:2209.11920.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convergence rate,
Convex optimization,
Gradient descent,
Heavyball method,
Nesterov's accelerated method,
Nonnormal dynamics,
Noise amplification,
Secondorder moments.
[bibtexentry]

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 firstorder algorithms,
Control for optimization,
Convex optimization,
Gradient descent,
Heavyball method,
Integral quadratic constraints,
Nesterov's accelerated method,
Nonnormal dynamics,
Transient growth.
[bibtexentry]

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.
[bibtexentry]

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.
[bibtexentry]

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.
[bibtexentry]

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.
[bibtexentry]

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.
[bibtexentry]

D. Ding,
X. Wei,
Z. Yang,
Z. Wang,
and M. R. Jovanovic.
Fast multiagent temporaldifference learning via homotopy stochastic primaldual optimization.
IEEE Trans. Automat. Control,
2020.
Note: Submitted; also arXiv:1908.02805.
Keyword(s): Convex optimization,
Distributed temporaldifference learning,
Multiagent systems,
Primaldual algorithms,
Reinforcement learning,
Stochastic optimization.
[bibtexentry]

A. Zare,
T. T. Georgiou,
and M. R. Jovanovic.
Stochastic dynamical modeling of turbulent flows.
Annu. Rev. Control Robot. Auton. Syst.,
3:195219,
May 2020.
Keyword(s): Colored noise,
Convex optimization,
Disturbance dynamics,
Flow modeling and control,
Lowcomplexity modeling,
Lowrank approximation,
Matrix completion problems,
Nuclear norm regularization,
Structured covariances,
Turbulence modeling.
[bibtexentry]

A. Zare,
H. Mohammadi,
N. K. Dhingra,
T. T. Georgiou,
and M. R. Jovanovic.
Proximal algorithms for largescale statistical modeling and sensor/actuator selection.
IEEE Trans. Automat. Control,
65(8):34413456,
August 2020.
Keyword(s): Actuator selection,
Augmented Lagrangian,
Convex optimization,
Lowrank perturbation,
Matrix completion problem,
Method of multipliers,
Nonsmooth optimization,
Proximal algorithms,
Regularization for design,
Sensor selection,
Sparsitypromoting optimal control,
Structured covariances.
[bibtexentry]

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):922924,
September 2019.
Keyword(s): Optimization,
Control,
Energy networks,
Power Networks.
[bibtexentry]

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):352362,
March 2019.
Keyword(s): Combination drug therapy,
Convex optimization,
Directed Networks,
Leader selection,
Positive systems,
Proximal algorithms,
Optimization,
Sparsitypromoting optimal control,
Structured design.
[bibtexentry]

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):28612868,
July 2019.
Keyword(s): Augmented Lagrangian,
Control for optimization,
Exponential convergence,
Global exponential stability,
Method of multipliers,
Nonsmooth optimization,
Primaldual gradient flow dynamics,
Proximal algorithms,
Proximal augmented Lagrangian,
Regularization for design,
Sparsitypromoting optimal control,
Structured optimal control,
Structure identification.
[bibtexentry]

S. HassanMoghaddam and M. R. Jovanovic.
Topology design for stochasticallyforced consensus networks.
IEEE Trans. Control Netw. Syst.,
5(3):10751086,
September 2018.
Keyword(s): Consensus,
Convex optimization,
Distributed control,
Interior point method,
Proximal gradient method,
Proximal Newton method,
Sparse graphs,
Topology design.
[bibtexentry]

A. Zare,
Y. Chen,
M. R. Jovanovic,
and T. T. Georgiou.
Lowcomplexity modeling of partially available secondorder statistics: theory and an efficient matrix completion algorithm.
IEEE Trans. Automat. Control,
62(3):13681383,
March 2017.
Keyword(s): Alternating minimization algorithm,
Convex optimization,
Disturbance dynamics,
Lowrank approximation,
Matrix completion problems,
Nuclear norm regularization,
Structured covariances.
[bibtexentry]

A. Zare,
M. R. Jovanovic,
and T. T. Georgiou.
Colour of turbulence.
J. Fluid Mech.,
812:636680,
February 2017.
Keyword(s): Colored noise,
Convex optimization,
Disturbance dynamics,
Flow modeling and control,
Lowcomplexity modeling,
Lowrank approximation,
Matrix completion problems,
Nuclear norm regularization,
Structured covariances,
Turbulence modeling.
[bibtexentry]

M. R. Jovanovic and N. K. Dhingra.
Controller architectures: tradeoffs between performance and structure.
Eur. J. Control,
30:7691,
July 2016.
Keyword(s): Controller architecture,
Convex optimization,
Distributed control,
Networks of dynamical systems,
Nonsmooth optimization,
Performance vs. complexity,
Regularization,
Sparsitypromoting optimal control,
Structured optimal control,
Structure identification.
[bibtexentry]

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

F. Lin,
M. Fardad,
and M. R. Jovanovic.
Algorithms for leader selection in stochastically forced consensus networks.
IEEE Trans. Automat. Control,
59(7):17891802,
July 2014.
Keyword(s): Alternating direction method of multipliers,
Consensus networks,
Convex optimization,
Greedy algorithm,
Leader selection,
Performance bounds,
Sparsity.
[bibtexentry]

R. Moarref,
M. R. Jovanovic,
J. A. Tropp,
A. S. Sharma,
and B. J. McKeon.
A loworder 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,
Lowrank approximation.
[bibtexentry]

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):24262431,
September 2013.
Keyword(s): Alternating direction method of multipliers,
Architectural issues in distributed control design,
Cardinality minimization,
Distributed control,
Optimization,
Sparsitypromoting optimal control.
[bibtexentry]

F. Lin and M. R. Jovanovic.
Leastsquares approximation of structured covariances.
IEEE Trans. Automat. Control,
54(7):16431648,
July 2009.
Keyword(s): Optimization,
Largescale systems,
Leastsquares approximation,
Structured covariances.
[bibtexentry]

H. Mohammadi,
M. Razaviyayn,
and M. R. Jovanovic.
Noise amplification of momentumbased optimization algorithms.
In Proceedings of the 2023 American Control Conference,
San Diego, CA,
2023.
Note: Submitted.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convergence rate,
Convex optimization,
Gradient descent,
Heavyball method,
Nesterov's accelerated method,
Noise amplification,
Nonnormal dynamics,
Twostep momentum algorithm.
[bibtexentry]

S. Samuelson,
H. Mohammadi,
and M. R. Jovanovic.
Performance of noisy higherorder accelerated gradient flow dynamics for strongly convex quadratic optimization problems.
In Proceedings of the 2023 American Control Conference,
San Diego, CA,
2023.
Note: Submitted.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convergence rate,
Convex optimization,
Gradient flow dynamics,
Noise amplification,
Nonnormal dynamics,
Twostep momentum algorithm.
[bibtexentry]

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,
pages 926931,
2022.
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.
[bibtexentry]

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,
pages 132137,
2022.
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.
[bibtexentry]

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.
[bibtexentry]

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.
[bibtexentry]

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.
[bibtexentry]

D. Ding and M. R. Jovanovic.
Global exponential stability of primaldual gradient flow dynamics based on the proximal augmented Lagrangian: A Lyapunovbased approach.
In Proceedings of the 59th IEEE Conference on Decision and Control,
Jeju Island, Republic of Korea,
pages 48364841,
2020.
Keyword(s): Augmented Lagrangian,
Control for optimization,
Convex optimization,
Global exponential stability,
Lyapunovbased approach,
Nonsmooth optimization,
Primaldual gradient flow dynamics,
Primaldual methods,
Proximal augmented Lagrangian.
[bibtexentry]

D. Ding,
K. Zhang,
T. Basar,
and M. R. Jovanovic.
Natural policy gradient primaldual method for constrained Markov decision processes.
In Proceedings of the 34th Conference on Neural Information Processing Systems,
volume 33,
Vancouver, Canada,
pages 83788390,
2020.
Keyword(s): Constrained Markov decision processes,
Constrained nonconvex optimization,
Natural policy gradient,
Policy gradient methods,
Primaldual algorithms.
[bibtexentry]

S. HassanMoghaddam and M. R. Jovanovic.
Global exponential stability of the DouglasRachford splitting dynamics.
In Preprints of the 21st IFAC World Congress,
Berlin, Germany,
pages 73507354,
2020.
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.
[bibtexentry]

H. Mohammadi,
M. Soltanolkotabi,
and M. R. Jovanovic.
Learning the modelfree 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 19,
2020.
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.
[bibtexentry]

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 47984803,
2020.
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.
[bibtexentry]

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 59115916,
2020.
Note: (Invited paper).
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convex optimization,
Gradient descent,
Integral quadratic constraints,
Nesterov's accelerated method,
Nonnormal dynamics,
Transient growth.
[bibtexentry]

S. Samuelson,
H. Mohammadi,
and M. R. Jovanovic.
Transient growth of accelerated firstorder methods.
In Proceedings of the 2020 American Control Conference,
Denver, CO,
pages 28582863,
2020.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convex optimization,
Gradient descent,
Transient growth.
[bibtexentry]

D. Ding and M. R. Jovanovic.
Global exponential stability of primaldual gradient flow dynamics based on the proximal augmented Lagrangian.
In Proceedings of the 2019 American Control Conference,
Philadelphia, PA,
pages 34143419,
2019.
Keyword(s): Convex optimization,
Global exponential stability,
Nonsmooth optimization,
Primaldual gradient flow dynamics,
Proximal augmented Lagrangian method.
[bibtexentry]

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 18221827,
2019.
Keyword(s): Byzantine mirror descent,
Distributed optimization,
Dual averaging,
Robust statistical learning.
[bibtexentry]

D. Ding,
X. Wei,
Z. Yang,
Z. Wang,
and M. R. Jovanovic.
Fast multiagent temporaldifference learning via homotopy stochastic primaldual method.
In Optimization Foundations for Reinforcement Learning Workshop, 33rd Conference on Neural Information Processing Systems,
Vancouver, Canada,
2019.
Keyword(s): Convex optimization,
Distributed temporaldifference learning,
Multiagent systems,
Primaldual algorithms,
Reinforcement learning,
Stochastic optimization.
[bibtexentry]

S. HassanMoghaddam,
M. R. Jovanovic,
and S. Meyn.
Datadriven proximal algorithms for the design of structured optimal feedback gains.
In Proceedings of the 2019 American Control Conference,
Philadelphia, PA,
pages 58465850,
2019.
Keyword(s): Datadriven feedback design,
Largescale systems,
Nonsmooth optimization,
Proximal algorithms,
Reinforcement learning,
Sparsitypromoting optimal control,
Structured optimal control.
[bibtexentry]

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 34263431,
2019.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convex optimization,
Integral quadratic constraints,
Linear matrix inequalities,
Noise amplification,
Secondorder moments,
Semidefinite programming.
[bibtexentry]

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 74747479,
2019.
Keyword(s): Datadriven control,
Global exponential stability,
Gradient descent,
Gradientflow dynamics,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
Reinforcement learning.
[bibtexentry]

D. Ding,
B. Hu,
N. K. Dhingra,
and M. R. Jovanovic.
An exponentially convergent primaldual algorithm for nonsmooth composite minimization.
In Proceedings of the 57th IEEE Conference on Decision and Control,
Miami, FL,
pages 49274932,
2018.
Keyword(s): Control for optimization,
Convex optimization,
Euler discretization,
Exponential convergence,
Global exponential stability,
Integral quadratic constraints,
Proximal augmented Lagrangian,
Nonsmooth optimization,
Primaldual gradient flow dynamics,
Proximal algorithms,
Regularization.
[bibtexentry]

S. HassanMoghaddam and M. R. Jovanovic.
Distributed proximal augmented Lagrangian method for nonsmooth composite optimization.
In Proceedings of the 2018 American Control Conference,
Milwaukee, WI,
pages 20472052,
2018.
Keyword(s): Consensus,
Distributed Optimization,
Nonsmooth optimization,
Primaldual gradient flow dynamics,
Proximal augmented Lagrangian.
[bibtexentry]

S. HassanMoghaddam 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 42464251,
2018.
Note: (Invited paper).
Keyword(s): Control for optimization,
Distributed optimization,
Forwardbackward envelope,
Exponential convergence,
Global exponential stability,
Gradient flow dynamics,
Largescale systems,
Nonsmooth optimization,
Primaldual method,
Proximal algorithms,
Proximal augmented Lagrangian.
[bibtexentry]

S. HassanMoghaddam and M. R. Jovanovic.
Topology identification via growing a ChowLiu tree network.
In Proceedings of the 57th IEEE Conference on Decision and Control,
Miami, FL,
pages 54215426,
2018.
Keyword(s): ChowLiu tree,
Consensus,
Convex optimization,
Graphical LASSO,
Sparse graphs,
Topology identification,
Maximum likelihood,
Sparse inverse covariance estimation.
[bibtexentry]

H. Mohammadi,
M. Razaviyayn,
and M. R. Jovanovic.
On the stability of gradient flow dynamics for a rankone matrix approximation problem.
In Proceedings of the 2018 American Control Conference,
Milwaukee, WI,
pages 45334538,
2018.
Keyword(s): Nonconvex optimization,
Stability of nonlinear systems,
Matrix approximation,
Gradient flow dynamics.
[bibtexentry]

H. Mohammadi,
M. Razaviyayn,
and M. R. Jovanovic.
Variance amplification of accelerated firstorder algorithms for strongly convex quadratic optimization problems.
In Proceedings of the 57th IEEE Conference on Decision and Control,
Miami, FL,
pages 57535758,
2018.
Keyword(s): Accelerated optimization algorithms,
Control for optimization,
Inputoutput analysis,
Largescale networks,
Fundamental limitations,
Robustness,
Variance amplifications.
[bibtexentry]

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 65146519,
2018.
Keyword(s): Convex optimization,
Proximal algorithms,
Sensor selection,
Semidefinite programming,
Sparsitypromoting estimation and control,
QuasiNewton methods.
[bibtexentry]

N. K. Dhingra,
S. Z. Khong,
and M. R. Jovanovic.
A second order primaldual algorithm for nonsmooth convex composite optimization.
In Proceedings of the 56th IEEE Conference on Decision and Control,
Melbourne, Australia,
pages 28682873,
2017.
Keyword(s): Augmented Lagrangian,
Method of multipliers,
Nonsmooth optimization,
Proximal methods,
Regularization,
Sparsitypromoting optimal control,
Structured optimal control,
Structure identification.
[bibtexentry]

S. HassanMoghaddam 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 65866591,
2017.
Keyword(s): Alternating direction method of multipliers,
Consensus,
Distributed control,
Optimization,
Sparsitypromoting optimal control,
Structured optimal control.
[bibtexentry]

S. HassanMoghaddam,
X. Wu,
and M. R. Jovanovic.
Edge addition in directed consensus networks.
In Proceedings of the 2017 American Control Conference,
Seattle, WA,
pages 55925597,
2017.
Keyword(s): Alternating direction method of multipliers,
Consensus,
Directed networks,
Nonconvex optimization,
Sparsitypromoting optimal control.
[bibtexentry]

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 37753780,
2017.
Keyword(s): Augmented Lagrangian,
Convex optimization,
Lowrank perturbation,
Matrix completion problem,
Method of multipliers,
Nonsmooth optimization,
Proximal methods,
Regularization,
Sparsitypromoting optimal control,
Structured covariances.
[bibtexentry]

J. Annoni,
P. Seiler,
and M. R. Jovanovic.
Sparsitypromoting dynamic mode decomposition for systems with inputs.
In Proceedings of the 55th IEEE Conference on Decision and Control,
Las Vegas, NV,
pages 65066511,
2016.
Note: (Invited paper).
Keyword(s): Dynamic Mode Decomposition,
Flow modeling and control,
Convex optimization,
Sparsity.
[bibtexentry]

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 411413,
2016.
Note: (Invited paper).
Keyword(s): Convex optimization,
Networks,
Monotone systems,
Positive systems,
Proximal algorithms,
Optimization,
Sparsitypromoting optimal control,
Structured design.
[bibtexentry]

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 52635268,
2016.
Note: (Invited paper).
Keyword(s): Combination drug therapy,
Convex optimization,
Networks,
Positive systems,
Proximal algorithms,
Optimization,
Sparsitypromoting optimal control,
Structured design,
Robust Control.
[bibtexentry]

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 27152720,
2016.
Note: (Invited paper).
Keyword(s): Consensus networks,
Convex optimization,
Positive systems,
Leader selection.
[bibtexentry]

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 825830,
2016.
Keyword(s): Combination drug therapy,
Convex optimization,
Leader selection,
Networks,
Positive systems,
Proximal algorithms,
Optimization,
Sparsitypromoting optimal control,
Structured design.
[bibtexentry]

N. K. Dhingra and M. R. Jovanovic.
A method of multipliers algorithm for sparsitypromoting optimal control.
In Proceedings of the 2016 American Control Conference,
Boston, MA,
pages 19421947,
2016.
Note: (Invited paper).
Keyword(s): Augmented Lagrangian,
Method of multipliers,
Proximal algorithms,
Optimization,
Sparsitypromoting optimal control.
[bibtexentry]

N. K. Dhingra,
X. Wu,
and M. R. Jovanovic.
Sparsitypromoting optimal control of systems with invariances and symmetries.
In Proceedings of the 10th IFAC Symposium on Nonlinear Control Systems,
Monterey, CA,
pages 648653,
2016.
Keyword(s): Architectural issues in distributed control design,
Cardinality minimization,
Convex optimization,
Distributed control,
Sparsitypromoting optimal control,
Spatiallyinvariant systems,
Symmetric systems.
[bibtexentry]

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 19781983,
2016.
Keyword(s): Convex optimization,
$k$supportnorm,
Lowrank approximation,
Matrix completion problems,
Nuclear norm regularization,
Structured covariances.
[bibtexentry]

S. HassanMoghaddam,
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 46244629,
2016.
Keyword(s): Consensus,
Convex optimization,
Sparse graphs,
Topology identification,
Maximum likelihood,
Sparse inverse covariance estimation.
[bibtexentry]

S. HassanMoghaddam 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 986991,
2016.
Keyword(s): Convex optimization,
Coordinate descent algorithm,
Networks,
Proximal algorithms.
[bibtexentry]

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 117119,
2016.
Keyword(s): Alternating minimization algorithm,
Convex optimization,
Disturbance dynamics,
Lowrank approximation,
Matrix completion problems,
Nuclear norm regularization,
Structured covariances.
[bibtexentry]

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 70367041,
2016.
Keyword(s): Convex optimization,
Lowrank perturbation,
Matrix completion problems,
Sparsitypromoting optimal control,
Structured covariances.
[bibtexentry]

S. HassanMoghaddam and M. R. Jovanovic.
An interior point method for growing connected resistive networks.
In Proceedings of the 2015 American Control Conference,
Chicago, IL,
pages 12231228,
2015.
Keyword(s): Consensus,
Convex optimization,
Distributed control,
Interior point method,
Sparse graphs,
Topology design.
[bibtexentry]

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 515520,
2015.
Keyword(s): Alternating direction method of multipliers,
Alternating minimization algorithm,
Convex optimization,
Lowrank approximation,
Matrix completion problems,
Nuclear norm regularization,
Structured covariances.
[bibtexentry]

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 40394044,
2014.
Note: (Invited paper).
Keyword(s): Alternating direction method of multipliers,
Convex optimization,
Actuator selection,
Sensor selection.
[bibtexentry]

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 24382443,
2014.
Keyword(s): Architectural issues in distributed control design,
Cardinality minimization,
Convex optimization,
Distributed control,
Sparsitypromoting optimal control.
[bibtexentry]

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 38023807,
2014.
Keyword(s): Consensus,
Optimization,
Social influence,
Social networks,
Stochastic matrices.
[bibtexentry]

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 21242129,
2014.
Note: (Invited paper).
Keyword(s): Optimization,
Perturbation analysis,
Social influence,
Social networks,
Stochastic matrices.
[bibtexentry]

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 16801685,
2014.
Note: (Invited paper; Finalist, Best Student Paper Award).
Keyword(s): Alternating direction method of multipliers,
Convex optimization,
Flow modeling and control,
Lowrank approximation,
Matrix completion problems,
Nuclear norm regularization,
Structured covariances.
[bibtexentry]

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 345354,
2014.
Keyword(s): Convex optimization,
Flow modeling and control,
Lowrank approximation,
Matrix completion problems,
Nuclear norm regularization,
Structured covariances.
[bibtexentry]

D. M. Zoltowski,
N. K. Dhingra,
F. Lin,
and M. R. Jovanovic.
Sparsitypromoting optimal control of spatiallyinvariant systems.
In Proceedings of the 2014 American Control Conference,
Portland, OR,
pages 12611266,
2014.
Keyword(s): Architectural issues in distributed control design,
Cardinality minimization,
Convex optimization,
Distributed control,
Sparsitypromoting optimal control,
Spatiallyinvariant systems.
[bibtexentry]

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 17021707,
2013.
Keyword(s): Convex optimization,
Lowrank approximation,
Matrix completion problems,
Nuclear norm regularization,
Structured covariances.
[bibtexentry]

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 47844789,
2013.
Keyword(s): Optimization,
Social influence,
Social networks,
Stochastic matrices.
[bibtexentry]

M. R. Jovanovic and F. Lin.
Sparse quadratic regulator.
In Proceedings of the 12th European Control Conference,
Zurich, Switzerland,
pages 10471052,
2013.
Keyword(s): Alternating direction method of multipliers,
Cardinality minimization,
Distributed control,
Optimization,
Sparsitypromoting optimal control.
[bibtexentry]

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 16841689,
2013.
Keyword(s): Alternating direction method of multipliers,
Convex optimization,
Lowrank approximation,
Matrix completion problems,
Nuclear norm regularization,
Structured covariances.
[bibtexentry]

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 25392544,
2012.
Keyword(s): Optimization,
Social influence,
Social networks,
Stochastic matrices.
[bibtexentry]

M. R. Jovanovic,
P. J. Schmid,
and J. W. Nichols.
Lowrank and sparse dynamic mode decomposition.
In Center for Turbulence Research Annual Research Briefs,
pages 139152,
2012.
Keyword(s): Dynamic Mode Decomposition,
Flow modeling and control,
Convex optimization,
Sparsity.
[bibtexentry]

M. Fardad,
F. Lin,
and M. R. Jovanovic.
Algorithms for leader selection in large dynamical networks: noisefree leaders.
In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference,
Orlando, FL,
pages 71887193,
2011.
Keyword(s): Consensus networks,
Convex optimization,
Leader selection,
Performance bounds,
Sparsity.
[bibtexentry]

F. Lin,
M. Fardad,
and M. R. Jovanovic.
Algorithms for leader selection in large dynamical networks: noisecorrupted leaders.
In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference,
Orlando, FL,
pages 29322937,
2011.
Keyword(s): Alternating direction method of multipliers,
Consensus networks,
Convex optimization,
Greedy algorithm,
Leader selection,
Performance bounds,
Sparsity.
[bibtexentry]

F. Lin and M. R. Jovanovic.
On the leastsquares approximation of structured covariances.
In Proceedings of the 2007 American Control Conference,
New York City, NY,
pages 26482653,
2007.
Keyword(s): Optimization,
Largescale systems,
Leastsquares approximation,
Structured covariances.
[bibtexentry]

H. Mohammadi,
M. Soltanolkotabi,
and M. R. Jovanovic.
Modelfree 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:10.1007/9783030609900.
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.
[bibtexentry]
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