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Publications about 'Control for optimization'


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]

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; also arXiv:2103.08017.
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]

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]

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]

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,
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]

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,
2022.
Note: To appear.
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 and M. R. Jovanovic.
Tradeoffs between convergence rate and noise amplification for momentumbased accelerated optimization algorithms.
In Proceedings of the 61th IEEE Conference on Decision and Control,
Cancun, Mexico,
2022.
Note: Submitted.
Keyword(s): Accelerated firstorder algorithms,
Control for optimization,
Convergence rate,
Convex optimization,
Noise amplification,
Secondorder moments,
Triple momentum method.
[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,
2022.
Note: To appear.
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 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]

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]

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]

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.
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]

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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Last modified: Tue May 3 09:45:45 2022
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