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Publications about 'Accelerated firstorder algorithms'


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 and M. R. Jovanovic.
Accelerated forwardbackward and DouglasRachford splitting dynamics.
Automatica,
2023.
Note: Submitted.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convex Optimization,
Forwardbackward envelope,
DouglasRachford splitting,
Global exponential stability,
Integral quadratic constraints,
Nesterov's accelerated method,
Nonsmooth optimization,
Proximal algorithms.
[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,
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]

S. Samuelson and M. R. Jovanovic.
Tradeoffs between convergence speed and noise amplification in firstorder optimization: the role of averaging.
In Proceedings of the 2024 American Control Conference,
Toronto, Canada,
2024.
Note: To appear.
Keyword(s): Accelerated firstorder algorithms,
Averaging,
Control for optimization,
Convergence rate,
Convex optimization,
Gradient flow dynamics,
Noise amplification,
Nonnormal dynamics,
Twostep momentum algorithm.
[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]

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,
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]
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