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Publications about 'Nonconvex 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]

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]

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]

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,
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. 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]

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,
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]

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]

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]

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]

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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