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Publications of H. Mohammadi


H. Mohammadi.
Robustness of gradient methods for datadriven decision making.
PhD thesis,
University of Southern California,
2022.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convergence rate,
Convex optimization,
Datadriven control,
Gradient descent,
Gradientflow dynamics,
Heavyball method,
Integral quadratic constraints,
Linear quadratic regulator,
Modelfree control,
Nesterov's accelerated method,
Nonconvex optimization,
Nonnormal dynamics,
Noise amplification,
Optimization,
Optimal control,
PolyakLojasiewicz inequality,
Random search method,
Reinforcement learning,
Sample complexity,
Secondorder moments,
Transient growth.
[bibtexentry]

H. Mohammadi,
S. Samuelson,
and M. R. Jovanovic.
Transient growth of accelerated optimization algorithms.
IEEE Trans. Automat. Control,
68(3):18231830,
March 2023.
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]

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]

H. Mohammadi,
M. Razaviyayn,
and M. R. Jovanovic.
Tradeoffs between convergence rate and noise amplification for momentumbased accelerated optimization algorithms.
IEEE Trans. Automat. Control,
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,
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]

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]

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]

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,
pages 849854,
2023.
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,
pages 38393844,
2023.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convergence rate,
Convex optimization,
Gradient flow dynamics,
Noise amplification,
Nonnormal dynamics,
Twostep momentum algorithm.
[bibtexentry]

S. Samuelson,
H. Mohammadi,
and M. R. Jovanovic.
Performance of noisy threestep accelerated firstorder optimization algorithms for strongly convex quadratic problems.
In Proceedings of the 62nd IEEE Conference on Decision and Control,
Singapore,
pages 13001305,
2023.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convergence rate,
Convex optimization,
Gradient flow dynamics,
Noise amplification,
Nonnormal dynamics,
Threestep 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]

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]

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]

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]

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]

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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Last modified: Tue Jan 23 11:32:51 2024
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